제어로봇시스템학회:학술대회논문집
Institute of Control, Robotics and Systems (ICROS)
 기타
Domain
 Machinery ＞ Robot/Automated Machinery
1992.10

A principle of "orthogonalization" is proposed as an extended notion of hybrid (force and position) control for robot manipulators under geometric endpoint constraints. The principle realizes the hybrid control in a strict sense by letting position and velocity feedback signals be orthogonal in joint space to the contact force vector whose components are exerted at corresponding joints. This orthogonalization is executed via a projection matrix computed in realtime from a gradient of the equation of the surface in joint coordinates and hence both projected position and velocity feedback signals become perpendicular to the force vector that is normal to the surface at the contact point in joint space. To show the important role of the principle in control of robot manipulators, three basic problems are analyzed, the first is a hybrid trajectory tracking problem by means of a "modified hybrid computed torque method", the second is a modelbased adaptive control problem for robot manipulators under geometric endpoint constraints, and the third is an iterative learning control problem. It is shown that the passivity of residual error dynamics of robots follows from the orthogonalization principle and it plays a crucial role in convergence properties of both positional and force error signals.force error signals.

This lecture is about an investigation into a desired property of fuzzy systems when degrees of uncertainty involved are uncertain. We characterize the robustness of fuzzy logic operators by their moduli of continuity. Theoretical results for design methodology are presented and a case study is discussed.

Modern manufacturing process requires machine intelligence to meet the demands for high technology products as well as intelligencebased operating skills to lessen human worker's intervene. To meet this trend there has been wide spread interest in applying artificial neural network(ANN) to the areas of manufacturing process monitoring and control. This paper addresses application problems in such processes as welding, assembly, hydroforming process and inspection of solder joints.

The need for intelligent systems that can operate in an unstructured, dynamic environment has created a growing demand for the use of multiple, distributed sensors. While most research in multisensor fusion has revolved around applications in object recognitionincluding military applications for automatic target recognitiondevelopments in microsensor technology are encouraging more research in affordable, highlyredundant sensor networks. Three trends that are described at length are the increasing use of microsensors, the techniques that are used in the handling of partial or uncertain data, and the application of neural network techniques for sensor fusion.

Interest in process control has rebounded from an alltime low in the mid1970s, with a new focus on bridging the gap between academic theory and industrial practice. Since then, much progress has been made in the new generation of process control theory to bridge this gap. This review summarizes the recent advances and current problems in process control on a qualitative level.

Conflicting or inconsistent rules sometimes help us to represent the control actions of an expert more freely. Also, uncertainties about the control actions of the expert may render rules with conclusions whore membership functions have different width in their shapes. Conventional inference methods for FLC may not effectively handle such inconsistencies and/or rules containing such conclusions. In this paper, an effective inference method dealing with such IfThen rules is proposed.

This paper describes motion control system applied to mechatronics devices. It is pointed out that a new approach is necessary to realize a good performance motion control. At first, a motion controller of mechatronics devices is introduced. The controller is constructed from four layer of hierarchical structure. After that two practical examples are presented to introduce the new approach to advanced motion control exactly.

In this paper, we developed a Receding Horizon Predictive Control for Stochastic state space models(RHPCS). RHPCS was designed to minimize a quadratic cost function. RHPCS consists of Receding Horizon Tracking Control(RHTC) and a state observer. It was shown that RHPCS is equivalent to Generalized Predictive Control(GPC) when the underlying state space model is equivalent to the I/O model used in the design of GPC. The equivalence between GPC and RHPCS was shown through. the comparison of the transfer functions of the two controllers. RHPCS provides a timeinvarient optimal control law for systems for which GPC can not be used. The stability properties of RHPCS was derived. From the GPC's equivalence to RHPCS, the stability properties of GPC were shown to be the same as those for RHTC.

This paper addresses the problem of designing a neural network based controller for a discretetime nonlinear dynamical system. Using two multilayered neural networks we first design an indirect controller the weights of which are updated by the informations obtained from system identification. The weight update is executed by parameter optimization method under Lagrangian formulation. For the nonlinear dynamical system, we define several cost functions and by computer simulations analyze the control performances of them and the effects of penaltyweighting values.

In this paper, a visual pattern recognition system is proposed, which can recognize both a pattern and its location. This system, referred to as the expanded neocognitron, has the following capabilities: (1) A higher performance in extraction of features, and (2) A new capability for recognizing the locations of patterns. This system adopts the learning and recognizing mechanism of the neocognitron. First, the ability to classify pattern is enhanced by improving the mechanisms of feature extraction and learning algorithm. Second, the function of detecting the location of each pattern is realized by developing an architecture which does not reduce structure, i.e., the unit density is constant all the way from the input stage to the output stage.

In this paper, an implementation of neurocontroller with an application of artificial neural network for an adjustment and tuning process for the completed electronics devices is presented. Multilayer neural network model is employed with the learning method of error backpropagation. For the intelligent control of adjustment and tuning process, the neural network emulator (NNE) and the neural network controller(NNC) are developed. Computer simulation reveals that the intelligent controllers designed can function very effectively as tools for computer aided adjustment system. The applications of the controllers to the real systems are also demonstrated.

This paper describes the modeling of human memory using a nerve field model which is proposed for modeling the mechanism of brain mathematically. In our model, two phases of memory, retention and recollection, are focused on. The former consists of two stages, shortterm memory (STM) and longterm memory (LTM). The proposed model consists of three parts, the STM Layer, LTM Layer and the Intermediate Layer between them. Each of these is constructed by a nerve field. In the STM Layer, memorized information is retained dynamically in the form of the reverberating states of units within the layer, while in the LTM Layer, it is stored statically in the form of structures of the weight on the links between units. the Intermediate Layer is introduced to translate this dynamic representation in the STM Layer to the LTNI Layer, and also to extract the static information from the STM Layer. In addition to this, we consider the recollection of information stored in the LTM. Finally, the behavior of this model is demonstrated by computer simulation.

Kang, SoonJu;Ryu, ChanHo;Choi, InSeon;Kim, YoungIll;Kim, killYoo;Hur, YoungHwan;Choi, SeongSoo;Choi, BaengJae;Woo, HeeGon 74
This paper describes an intelligent system to automatic evaluation of eddy current(EC) signal for Inspection of steam generator(SG) tubes in nuclear power plant. Some features of the intelligent system design in the proposed system are : (1) separation of representation scheme ,or event capturing knowledge in EC signal and for structural inspection knowledge in SG tubes inspection; (2) each representation scheme is implemented in different methods, one is syntactic pattern grammar and the other is rule based production. This intelligent system also includes an data base system and an user interface system to support integration of the hybrid knowledge processing methods. The intelligent system based on the proposed concept is useful in simplifying the knowledge elicitation process of the rule based production system, and in increasing the performance in real time signal inspection application. 
Path planning is an important task for optimal motion of a robot in structured or unstructured environment. The goal of this paper is to plan the optimal collisionfree path in 3D, when a robot is navigated to pick up some tools or to repair some parts from various locations. To accomplish the goal, the Path Coordinator is proposed to have the capabilities of an obstacle avoidance strategy and a traveling salesman problem strategy (TSP). The obstacle avoidance strategy is to plan the shortest collisionfree path between each pair of n locations in 2D or in 3D. The TSP strategy is to compute a minimal system cost of a tour that is defined as a closed path navigating each location exactly once. The TSP strategy can be implemented by the Hopfield Network. The obstacle avoidance strategy in 2D can be implemented by the VGraph Algorithm. However, the VGraph Algorithm is not useful in 3D, because it can't compute the global optimality in 3D. Thus, the Path Coordinator is used to solve this problem, having the capabilities of selecting the optimal edges by the modified Genetic Algorithm and computing the optimal nodes along the optimal edges by the Recursive Compensation Algorithm.

This report his discussed emergency evacuation in hotel fires and proposed an integrated system of emergency evacuation. By using "A New Type of the Computerized Emergency Evacuation System" all guests will be able to quickly and safety escapes the fire. the fire.

Authors have developed the method of selecting the efficient variant of designing a systems or products from a some number of competition variants in the conditions of vagueness of the initial information. Registration of information vagueness degree concerning the quantity index values is carried out of the expense of giving to the expert the possibility of using different methods of index evaluation numerical evaluations with physical scale; phrases of limited language; and points evaluation. Using of this method and software is important for marketing research, for systems of quality control of products.

Currently, there are various robot programming methods for articulated robots. Although each method has merits and drawbacks, they have commonly weak points for practical application, and especially the weak point can be even more vulnerable when the robot programming requires the subtle feelings of human being. This is because the movement of a human being is synthetic while the robot programming is analytic. Therefore, the present method of programming has limits in performing these kinds of subtle robot movement. In this paper, we propose a direct robot programming method, which generates robot programs based on the force/torque vector applied to a force/torque sensor by the human operator. The method reduces the effort required in the robot programming.

A mathematical model is developed for a CSTR in which free radical solution polymerization of methyl methacrylate(MMA) takes place. It turns out that five ordinary differential equations are to be treated simultaneously in order to predict the reactor performance. Although the reaction proceeds under the conditions of relatively low temperature and pressure, the system shows very complex bifurcation features due to the diffusion limitation (gel effect) and the temperature dependence of the kinetic parameters and physical properties. The effects of various system parameters on the reactor performance as well as on the polymer properties are investigated by using the bifurcation analysis. The application of the singularity theory enables us to divide the parameter space into several different regions, in each of which the system takes a unique steady state structure. Under certain circumstances, complex dynamic features such as HB points and limit cycles are observed and these should be taken into consideration in the reactor design.

In order to achieve stable and efficient use of energy at iron and steel works, a model for the prediction of supply and demand of electric power system is developed on the basis of the information on operation and particular patterns of electric power consumption. The optimal amount of electric power to be purchased and the optimal fuel allocation for the inhouse electric power plants are also obtained by a mixedinteger linear programming(MILP) and a nonlinear programming (NLP) solutions, respectively. The validity and the effectiveness of the proposed model are investigated by several illustrative examples. The simulation results show the satisfactory energy saving by the optimal solution obtained through this research.

The Realtime Distributed Control Systems(RDCS) consist of several distributed control processes which share a network medium to exchange their data. Performance of feedback control loops in the RDCS is subject to the networkinduced delays from sensor to controller and from controller to actuator. The networkinduced delays are directly dependent upon the data sampling times of the control components which share a network medium. In this study, a scheduling algorithm of determining data sampling times is developed using the window concept, where the sampling data from the control components dynamically share a limited number of windows.

Analysis is made for a fishdrying process control in order to implement a human expertise into an automated fish drying system. Together with the idea described in the companion paper, a methodology is found effective in a general drying system other than fish drying.

The goal of the proposed Intelligent Assisting System  IAS is to assist human operators in an intelligent way, while leaving decision and goal planning instances for the human. To realize the IAS the very important issue of manipulation skill identification and analysis has to be solved, which then is stored in a Skill Data Base. Using this data base the IAS is able to perform complex manipulations on the motion control level and to assist the human operator flexibly. We propose a model for manipulation skill based on the dynamics of the grip transformation matrix, which describes the dynamic transformation between object space and finger joint space. Interaction with a virtual world simulator allows the calculation and feedback of appropriate forces through controlled actuators of the sensor glove with 10 degreesoffreedom. To solve the sensor glove calibration problem, we learn the nonlinear calibration mapping by an artificial neural network(ANN). In this paper we also describe the experimental system setup of the skill acquisition and transfer system as a first approach to the IAS. Some simple manipulation examples and simulation results show the feasibility of the proposed manipulation skill model.

The ability of a robot system to comply to an environment via the control of toolenvironment interaction force is of vital for the successful task accomplishment in many robot application. This paper presents the implementation of external force control for two dimensional contour following task using a commercial robot system. Force accommodation is used since a constraint imposed in our work is not to modify the commercial robot system. A linear, decoupled model of two dimensional contour following system in the discrete time domain is derived first. Then the experimental verification of linear control is obtained using a PUMA 560 manipulator with standard Unimation controller, Astek FS6120A six axis wrist force sensor attached externally to the arm and LSI11173 microcomputer. Experimentally obtained data shows that the RMS contact force error is 0.8246 N when following the straight edge and 2.3768 N when following 40 mm radius curved contour.

Control of toolenvironment interaction force to comply the robot system to an environment is of vital in many automated process. This paper presents the implementation of an adaptive force control with commercial robot system in two dimensional contour following task. A model reference adaptive control system, combined with the linear compensators, is implemented. That is, a use of adaptive control is to provide an auxiliary control system so that the contour following performance can be improved from that of using linear control system only. Hyperstability is used to derive the adaptive control law. Experimental verification of the proposed control system is obtained using PUMA 560 robot system. Data obtained experimentally shows that the use of additional adaptive control system improves the contour following performance about 30% in RMS contact force errors upon that of the system controlled by the linear compensators only.

This paper presents a decentralized model reference adaptive control scheme for an interconnected linear system composed of a number of singleinput singleoutput subsystems in which outgoing interactions pass through the measurement channel and are subjected to bounded external disturbances. The scheme can treat the unknown strength of interactions as well as uncertainties in subsystem dynamics, and allows for the case when the relative degree of each decoupled subsystem does not exceed two.

A twocompartment fourcell model is developed for the adiabatic autoclave slim type reactor for free radical polymerization of low density polyethylene(LDPE). The mass and energy balances give rise to a set of ordinary differential equations, and by analyzing the system it is possible to predict properly not only the reactor performance but also the properties of polymer product. The steady state multiplicity is found to exist and examined by constructing the bifurcation diagram. The effects of various operation parameters on the reactor performance and polymer properties are investigated systematically to show that the temperature distribution plays the central role for the properties of polymer product. Therefore, it is essential to establish a good control strategy for the temperature in each compartment. In this study it is shown that the reactor system can be adoptively controlled by poleplacement algorithm with conventional PID controller. To accomplish a satisfactory control, the estimator and controller are initialized during the period of startup.

The simple adaptive control(SAC) method has attracted attention for interest of the simple structure of its adaptive controller. We establish that the introduction of output derivative action to the original SAC system definitely improves the response characteristic of the control system. The effect of such an introduction is confirmed through experimental results by applying the method to a servo control system using a direct drive (DD) motor.

This paper describes the estimation of the solid friction in mechanical systems by using the extended Kalman filtering techniques. We proposed two stochastic model for the estimation. The one is the 'parametric model' which represents the friction characteristics by an exponential function with unknown parameters. The other is the 'blind model' which does not assume an explicit model but regard the effect of the friction as an unknown input to a known dynamic system. For both models, we give estimation algorithms to generate the filtered estimate and the smoothed estimate with a fixed lag. The filtered estimate can be generated online for compensating the solid friction in mechanical systems. Although online applications are impossible, the smoothed estimate is more accurate and can be used to grasp precise friction characteristics. Simulation and experimental results arc presented to show the effectiveness of the proposed techniques.

Classical methods for estimating transfer function models have not always been successful. A statistic approach to the identification of transfer function models which is corrupted by disturbances or noise is presented. The estimated impulse response is obtained from the autocorrelation function and cross correlation function between the measured input and output. Several data analysis tools such as R , S and GPAC array for the estimated impulse response give us pretty clear information on the order of transfer function models.

A leak detection method for diagnosis of the leak position in a pipeline was developed using an estimation theory with the assumption that the measured flow rates and pressures are stochastic processes. A notch filter was designed using power spectral density analysis of measurements to reduce the effects of disturbances. The noise model dimension was determined by hypothesis testing and then recursive extended least square method was applied to estimate the leak position in real time. The proposed method was applied to an experimental system for evaluation of its performance.

At the KACC'91 conference, we proposed a method of automatic detection of shape of the faulty holes of a shadow mask which is used in a cathoderay tube of a color television. In this method, the image data are taken from two areas of the mask with CCD camera. Comparing the shape of holes in these two areas by use of a signal processing technique, we can find any fault in the shape of holes. This paper describes the effect of smoothing filters of effectively finding the faulty holes from the difference image data. A computer simulation and actual experiment with a shadow mask have shown that this method of fault detection is very effective for practical use.

Many realworld problems are concerned with estimation rather than classification. This paper presents an adaptive technique to estimate the mechanical properties of materials from acoustoultrasonic waveforms. This is done by adapting a piecewise linear approximation technique to a multilayered neural network architecture. The piecewise linear approximation network (PWLAN) finds a set of connected hyperplanes that fit all input vectors as close as possible. A corresponding architecture requires only one hidden layer to estimate any curve as an output pattern. A learning rule for PWLAN is developed and applied to the acoustoultrasonic data. The efficiency of the PWLAN is compared with that of classical backpropagation network which uses generalized delta rule as a learning algorithm.

There are several potential error sources that can affect the estimation of the position of an object using combined vision and acceleration measurements. Two of the major sources, accelerometer dynamics and random noise in both sensor outputs, are considered. Using a secondorder model, the errors introduced by the accelerometer dynamics are reduced by the smaller value of damping ratio and larger value of natural frequency. A Kalman filter approach was developed to minimize the influence of random errors on the position estimate. Experimental results for the endpoint movement of a flexible beam confirmed the efficacy of the Kalman filter algorithm.

When there exist parameter uncertainty, modelling errors and nonminimum phase zeros in control object system. the stability robustness of conventional LQG and LOG/LTR methods are not satisfactory[2, 8]. Since these methods are performed on the infinite horizon, it is very hard to establish exact design parameters and thus they have lots of problems to be applied to real systems, So in this paper we propose RHLQG/FIRF optimal controller which has robust stability against parameter uncertainty, nonminimum phase zeros and modelling errors. This method uses only the information around at present and therefore shows good performance even when we do not know exact design parameters. We here compare LQG and LQG/LTR method with RHLQG/FIRF controller and exemplify that RHLQG/FIRF controller has better robust stability performance via simulations.

In this paper, we consider the synthesis of mixed H
$_{2}$ /H$_{\infty}$ controllers such that the closedloop poles are located in a specified region in the complex plane. Using solution to a generalized Riccati equation and a change of variable technique, it is shown that this synthesis problem can be reduced to a convex optimization problem over a bounded subset of matrices. This convex programming can be further reduced to Generalized Eigenvalue Minimization Problem where Interior Point method has been recently developed to efficiently solve this problem.. 
In this paper, a new GPC(Generalized Predictive Control) algorithm which is robust to disturbances isproposed. This controller minimizes the LQ cost function when the disturbance maximizes this cost function. The solution is obtained from the minmax problem which can be solved by differential game theory and has the nonrecursive form which does not use the Riccati equation. Its another solution for state space models is investigated.

In this paper we are concerned with optimal control problems whose costs am quadratic and whose states are governed by linear delay equations and general boundary conditions. The basic new idea of this paper is to Introduce a new class of linear operators in such a way that the state equation subject to a starting function can be viewed as an inhomogeneous boundary value problem in the new linear operator equation. In this way we avoid the usual semigroup theory treatment to the problem and use only linear operator theory.

In this paper, we propose a method for obtaining statespace realization form of twodimensional transfer function matrices (2DTFM). It contains free parameters. And, we perform various consideration about it. Moreover, we present the conditions so that the statespace realization form exists.

This paper deals with the linear quadratic optimal regulator problem for descriptor systems without performing a preliminary transformation for a descriptor system. We derive a generalized Riccati differential equation (GRDE) based on the twopoint boundary value problem for a Hamiltonian equation. We then obtain an optimal feedback control and the optimal cost in terms of the solution of GRE. A simple example is included.

The purpose of this project is the presentation of new method for selection of a scalar control of linear timeperiodic system. The approach has been proposed by Radziszewski and Zaleski [4] and utilizes the quadratic form of Lyapunov function. The system under consideration is assigned either in closedloop state or in modal variables as in Calico, Wiesel [1]. The case of scalar control is considered, the gain matrix being assumed to be at worst periodic with the system period T, each element being represented by a Fourier series. As the optimal gain matrix we consider the matrix ensuring the minimum value of the larger real part of the two Poincare exponents of the system. The method, based on twostep optimization procedure, allows to find the approximate optimal gain matrix. At present state of art determination of the gain matrix for this case has been done by systematic numerical search procedure, at each step of which the Floquet solution must be found.

This paper presents a compensator design method for multivariable feedback control systems with saturating actuators based on the concept of the equilibrium point. Am explicit expression for the compensation matrix of the general antireset windup(ARW) scheme is derived by minimizing the distances between the equilibrium points. The resulting dynamics of the compensated controller exhibits the reduced model form of the unsaturated system which can be obtained by the singular perturbational method. The proposed method is applicable to any openloop stable plants with saturating actuators whose controllers are determined by some design technique. An example is given to show the effectiveness of the proposed method.

The paper describes an approach for estimating unsteady flow rate through oil hydraulic pipelines and components in real time. Recently we have proposed following three unsteady flow rate measurement approaches; RIFM, QIFM and TPFM, in which hydraulic pipeline dynamics are made use of. In this paper, we firstly propose new approaches, i.e, an interpolation and an extrapolation methods in combination with RIFM and TPFM. In the interpolation method, unsteady flow rate at the arbitrary internal location along the pipeline between two points for measuring the two point pressure can be estimated. In this paper, the accuracy and dynamic response of interpolation method are mainly experimentally investigated in detail.

A new method for generation of binary random sequences, called random sampling method, has been proposed by the authors. However, the random sampling method has the defect that binary random sequence can not be rapidly generated. In this paper, two methods based on the random sampling method are proposed for fast generation of binary random sequences. The optimum conditions for obtaining ideal binary random sequences are derived.

In this paper, we present a nev algorithm for the fast computation of the discrete cosine transform(DCT). This algorithm consists of the three dimensional prime factordecomposed algorithm(PFA) and three dimensional common factordecomposed algorithm(CFA). We can compute Npoint DCT for the number N decomposable Into three relative prime numbers using PFA and into three common numbers using CFA. We also show input and output index mapping for the three decomposition. it results in requiring fever multiplicaions than the previous algorithms. Particularly, for the large number N, it is more powerful in reducing the number of multiplication.

We present a system to measure 3dimensional coordinates of large structures such as ships, buildings and oil tanks. Our system consists of two important units which are a laser spot pointer and a laser spot tracker. Employing a tactful image processing, our system has some features: e.g. downsize, cost, accuracy and robustness to hazardous environments.

It is reported on the methodology of signal detection in noise which is based on a comparison of statistical parameters of observation sample from region of frequencytime noise space where a signal may be present and observation sample from region of this noise space and it is known a priori about the latter that the signal is absent in this region.

This paper proposes a neurofuzzy adaptive controller which includes the procedure of initializing the identification neural network(INN) and that of learning the control neural network(CNN). The identification neural network is initialized with the informations of the plant which are obtained by a fuzzy controller and the control neural network is trained by the weight informations of the identification neural network during online operation.

It is the purpose of this paper to present a dialogical designing method for control system using a rough grasp of the unknown process property. We deal with a singleinput singleoutput feedback control system with a fuzzy controller. The process property is roughly estimated by the step response, and the fuzzy controller is interactively modified according to the operator's requests. The modifying rules mainly derived from computer simulation are useful for almost every process, such as an unstable process and a nonminimum phase process. The fuzzy controller is tuned by taking notice of four characteristics of the step response: (1) rising time, (2) overshoot, (3) amplitude and (4) period of vibration. The tuning position of the controller is fourfold: (1) antecedent gain factor GE or GCE, (2) consequent gain factor GDU, (3) arrangement of the antecedent fuzzy labels and (4) arrangement of the control rules. The rules give an instance to the respective items of the controller in an effective order. The modified fuzzy PI controller realizes a good response of a stable process. However, because the GDU tuning becomes difficult for the unstable process, it is necessary to evaluate the stability of the process from the initial step response. The fuzzy PI controller is applied to the process whose initial step response converges with GDU tuning. The fuzzy PI controller with modified sampling time is applied to the process whose step response converges under the repeated application of the GDU tuning. The fuzzy PD controller is applied to the process whose step response never converges by the GDU tuning.

A novel fault diagnosis method based on likelihood decomposition is proposed for linear stochastic systems described by autoregressive (AR) model. Assuming that at some time instant .tau. the fault of one of the following two types is occurs: innovation fault (actuator fault); and observation fault (sensor fault), the loglikelihood function is decomposed into two components based on the observations before and after .tau., respectively, Then, the type of the fault is determined by comparing the loglikelihoods corresponding two types of faults. Numerical examples demonstrate the usefulness of the proposed diagnosis method.

In the paper, an optimal tuning algorithm is presented to automatically improve the performance of a hybrid controller, using the simplified reasoning method and the proposed complex method. The method estimates automatically the optimal values of the parameters of a hybrid controller, according to the change rate and limitation condition of output, The controller is applied to plants with timedelay. Then, computer simulations are conducted at step input and the performances are evaluated in the ITAE.

This paper is aiming to apply the Genetic Algorithms (GAs) to the interactive design. For that purpose, the scheme for utilizing the past design processes for the next interactive design process is proposed. In this scheme, the process consists of three phases: the searching phase, the tuning phase and the design phase. The first phase searches the optimal decision sequences for the past design instances by GAs. By the collected sequences, the second phase tunes the criteria of selecting decision sequences for the next design process. By this scheme, the implicit constraints satisfied in the past design can be applied to the next design. Finally, the computer simulations on the simple geartrain design were carried out to show the effectiveness of the scheme.

This paper describes a new optimization technique for the design of traffic signal patterns. The proposed method uses a Genetic Algorithm for searching through the better signal patterns. Since the Genetic Algorithm is effective to search directly through a huge binary coded state spaces, the proposed design method has the following advantages over the conventional OR methods: (1) online optimization is available within a reasonable time, (2) there is no limitation to the types of signals to be optimized. Some computer simulations are carried out and its ability of getting high quality control in a short period is demonstrated.

Tendon driven method to drive one joint using two actuators is developed and implemented. While the method has advantages over conventional transmissions, it also has several drawbacks like tendon slack, elongation and endurability. In this paper, a compensation method of the intrinsic nonlinearities of tendon is proposed to improve the performance of antagonistic tendon driven method. In this method, tendon tension measurement is prerequisite which is measured with strain gauge type tension sensor. The developed method is implemented on one link test bed with colocated and noncolocated position sensor.

This paper deals with a dual mode control system design for the starching work robot. From the feature of this work, the robot has redundant degree of freedom. In this paper, we try to split the whole movement the robot into a gross motion part ai. a fine motion part so as to achieve a good tracking performance. The preview learning control is applied to the gross motion part. The validity of the dual mode control architecture is demonstrated.

We propose software algorithms which provide the characteristics of acceleration/deceleration essential to high dynamic performance at the transient state where industrial robots or CNC machine tools start and stop. The path error, which is one of the most significant factors in performance evaluation of industrial robots and CNC machine tools, is analyzed for linear, exponential, and parabolic acceleration/deceleration algorithms in case of circular interpolation. The analysis shows that the path error depends on the acceleration/deceleration routine and the servo control system. In experiments, the entire control algorithm including the proposed acceleration/deceleration algorithms is executed on the motion control system with a floating point digital signal processor(DSP) TMS320C30 as a CPU. The experimental results demonstrate that the proposed algorithms are very effective in controlling axes of motion of industrial robots or CNC machine tools with the desired characteristics.

This paper proposes a new framework of an autonomous and distributed flexible manufacturing system  Multi Client Robot Groups(MCR)  and describes a stochastic learning scheme applied to managerial problems of the system. The MCR is composed of groups of manufacturing robots, named Client Robots (CRs), which are capable of both versatility and independence in their performances. The MCR is expected to have high performance because the MCR can perform concurrent and corporative processing. However, the system performance is determined by the organizations of the CR groups. Therefore the treatment of the managerial problems and organizations of the system are important problems. In this paper, it is assumed that CR groups being able to processing tasks are selected stochastically based on the strengths of the robot groups. The learning scheme adjusting the strength is introduced to organize the groups in the system and control the each performance of the groups according to the total system performance. Finally, some experimental results of the learning scheme are shown.

The improvement of the production capability of multi PCB assembly line can not be simply done by improving the capacities of each assembly robot cells but must be done by controlling the production line effectively with the line host computer which controls over the whole assembly line. A real time production control, a real time model change and a real time trouble shooting compose the specific concepts of this technique. In this paper, we present and analyze the definition and application method of real time assembly concept. The meaning of real time model change, troubles and error sooting and its algorithm will be introduced. Also, the function of the host computer which is in charge of all of many different tasks mentioned above and the method are presented. The improvement of the productivity is mainly focused on the efficiency of multiPCB production control. The importance of this aspect is gradually increasing, which we have presented the analysis and the solution.

A physical phenomenon is observed through analysis of the HodgkinHuxley's model that is, according to Maxwell field equations a fired neuron can yield magnetic fields. The magnetic signals are an output of the neuron as some type of information, which may be supposed to be the conscious control information. Therefore, study on neural networks should take the field effect into consideration. Accordingly, a study on the behavior of a unit neuron in the field is made and a new neuron model is proposed. A mathematical MemoryLearning Relation has been derived from these new neuron equations, some concepts of memory and learning are introduced. Two learning theorems are put forward, and the control mechanisms of memory are also discussed. Finally, a theory, i.e. Neural Electromagnetic(NEM) field theory is advanced.

This study is concerned how to construct a model of life as physical/ mathematical representation. This model is called here a bionic model and vibrating potential field is introduced as fundamental world background of the model. Namely, required information creating/ processing/ controlling are done on this field. Especially this paper reports how to realize the simulation of a bionic self organization and its functional expression based on the mutual actions among a set of life units.

Uchida, Yoshiyuki;Nohira, Shigemitsu;Seike, Yoshiyuki;Shingu, Hiroyasu;Sumi, Tetsuo;Furuhashi, Hideo;Yamada, Jun 340
Fundamental positioning characteristics of a dualaxis Sawyer linear motor are described. The Sawyer motor is capable of high positional accuracy. An electronic control unit of the motor whose velocity is proportional to the frequency of the electric current was produced in our laboratory. The positioning system was constructed using two Sawyer motors, an air bearings suspension unit and an electronic control unit. The stable motion of the motor was confirmed on the open loop operation. The adjustable operating conditions were the live load of 1 kg, the maximum acceleration of 1.2G and the maximum velocity of 350 mm/s. Absolute positioning accuracy was improved within .+.5.mu.m, on microstep operating conditions of dividing one pitch of 508.mu.m into 508 steps. The following two conclusions were obtained. An acceleratingcruisingdecelerating control is effective for reduction in the travel time required. Also, microstep operation is effective for improving the resolution of position. 
This paper describes a procedure to design a hovering flight controller for a model helicopter using LQG theory. Parameters of the model helicopter in hover are obtained using direct measurements and calculations proposed by other research. A feedback co is by using digital LQG theory. First, a full state feedback controller is designed to the discretized system taking desirable transient response and other assumptions into account. Then a fullstate estimator is designed and revised until desirable response is obtained while global stability is maintained. Performance of the controller is tested by computer simulations. Experiments have been performed using a 3degreeoffreedom gimbal that holds the model helicopter, and the controller exhibited stable hover capability.

In this paper, a modal analysis is applied for a hung EulerBernoulli beam with a lumped mass. We first derive the equations of motion using the Hamilton's principle. Then regarding the tension of beam as constant, we characterize the eigenfrequencies and the feature of eigenfunctions. The approximation employed here is corresponding that the lumped mass is sufficiently large than that of beam. Finally we compare the eigenfrequencies derived here with those obtained based on the Southwell's method.

This paper presents a procedure to design a real time control system for a magnetic levitation system based on the state space approach by adopting a control method compensating attractive force according to load variation of maglev vehicle. Also the paper has realized a robust control algorithm for the change of selfinductance parameters and the disturbance such as the change of mass of Maglev vehicles. The theoretical results are applied to the gap control problems of an attractivetypemagnetic levitation system and the effectiveness is proved by the implementation of digital control using 16 bits microcomputer.

An optimal design method to determine the lengths of finger phalanges is proposed especially for anthropomorphic design. The quality of designs are quantified by several measures of global isotropy for design, Also, for an example, optimal design of two fingers is performed and the results are compared with the anatomical data.

In this paper we propose a 3dimensional formation system using an arc welding robot. The principle of our system is just only to accumulate welding beads, so that the target 3dimensional surfaces can be built up. Considering the effects of the gravity on the arc welding, the welding torch is steadily clamped and the position and the posture of the target board on which target work is formed is controlled by a 6axis robot hand. Movements of the target board are controlled considering the 3dimensional shape of the target and the accumulating speed of the welding bead. In order to realize such systems, a distance sensor is mounted on the tip of the robot hand. And a coordinate transformation technique is employed

An algorithm for the notion planning of the robotic hand is proposed to generate finite displacements and changes in orientation of objects by considering sliding effects between the fingertips and the object at contact points. Specifically, an optimization problem is firstly solved to find minimum contact forces and minimum joint velocities to impart a desired motion to the object at each time step. Then the instantaneous relative velocity at the contact point is found by determining velocities of the fingertip and the velocity of the object at the contact point. Finally time derivatives of the surface variables and contact angle of the fingertip and the object at the present time step is computed using the Montana's contact equation to find the contact parameters of the fingertip and the object at the next time step. To show the validity of the proposed algorithm, a numerical example is illustrated by employing the robotic hand manipulating a sphere with three fingers each of which has four joints.

This paper presents an expert system for the segmentation of a 2.5D image. The results of two segmentation approaches, edgebased and regionbased, are combined to produce a consistent and reliable segmentation. Rich information embedded in the 2.5D image is utilized to obtain a view independent surface patch description of the image, which can facilitate object recognition considerably.

In order to obtain desired arc welding performance, we already developed an arc welding robot system that enabled coordinated motions of dual arm robots. In this system one robot arm holds a welding target as a positioning device, and the other robot moves the welding torch. Concerning to such a dual arm robot system, the positioning accuracy of robots is one important problem, since nowadays conventional industrial robots unfortunately don't have enough absolute accuracy in position. In order to cope with this problem, our robot system employed teaching playback method, where absolute error are compensated by the operator's visual feedback. Due to this system, an ideal arc welding considering the posture of the welding target and the directions of the gravity has become possible. Another problem still remains, while we developed an original teaching method of the dual arm robots with coordinated motions. The problem is that manual teaching tasks are still tedious since they need fine movements with intensive attentions. Therefore, we developed a 3dimensional vision guided robot control method for our welding robot system with coordinated motions. In this paper we show our 3dimensional vision sensor to guide our arc welding robot system with coordinated motions. A sensing device is compactly designed and is mounted on the tip of the arc welding robot. The sensor detects the 3dimensional shape of groove on the target work which needs to be weld. And the welding robot is controlled to trace the grooves with accuracy. The principle of the 3dimensional measurement is depend on the slitray projection method. In order to realize a slitray projection method, two laser slitray projectors and one CCD TV camera are compactly mounted. Tactful image processing enabled 3dimensional data processing without suffering from disturbance lights. The 3dimensional information of the target groove is combined with the rough teaching data they are given by the operator in advance. Therefore, the teaching tasks are simplified

Suzuki, Takashi;Shinohara, Shigenobu;Yoshida, Hirofumi;Ikeda, Hiroaki;Saitoh, Yasuhiro;Nishide, KenIchi;Sumi, Masao 388
An infrared range finder using a selfmixing laser diode (SMLD), which has been proposed and developed by the Authors, can measure not only a range of a moving target but its velocity simultaneously. In this paper, described is that the precise modehop pulse train can be obtained by employing a new signal processing circuit even when the backscattered light returning into the SMLD is much more weaker. As a result, the distance to a tilted square sheet made from aluminium or white paper, which is placed 10 cm through 60 cm from the SMLD, is measured with accuracy of a few percent even when the tilting angle is less than 75 degrees or 85 degrees, respectively. And in this paper, described is the rangeimage recognition of a plane object under the condition of standstill. The output laser beam is scanned by scanning two plane mirrorsequipped with each stepping motor. And we succeeded in the acquisition of the rangeimage of a plane object in a few tens of seconds. Furthermore, described is a feasibility study about the rangeimage recognition of a slowly moving plane object. 
Tada, KenIchi;Shinohara, Shigenobu;Yoshida, Hirofumi;Ikeda, Hiroaki;Saitoh, Yasuhiro;Nishide, KenIchi;Sumi, Masao 394
The measurable speed range of the selfmixing type semiconductor laser range finder has been greatly improved by employing a new processing circuit. Using this range finder as an external finder of a single lens reflex (SLR) autofocus (AF) camera, some clear photographs of an object moving at a medium speed of 20 mm/s is obtained. 
Curved 3D objects represented by range data contain large amounts of information compared with planar objects, but do not have distinct features for matching to those of object models. This makes it difficult to represent and identify a general 3D curved object. This paper introduces a new approach to representing and finding a holdsite of general 3D curved objects using range data. We develop a threedimensional generalized Hough transformation which can be also applied to general 3D curved object recognition and which reduces both the computation time and storage requirements. Our approach makes use of the relative geometric differences between particular points on the object surface and some model points which are prespecified arbitrarily and task dependently.

The problem we consider in this paper is more demanding than the problem of inputoutput linearization with state equivalence recently solved by Cheng, Isidori, Respondek, and Tarn. We request that the MIMO nonlinear system, for which the problem of inputoutput linearization with stateequivalence is solvable, can be decoupled. In exchange for further requirement like this, our problem produces more usable and informative results than the problem of inputoutput linearization with stateequivalence. We present the necessary and sufficient conditions for our problem to be solvable. We characterize each of the nonlinear systems satisfying these conditions by a set of parameters which are invariant under the group action of state feedback and transformation. Using this set of parameters, we can determine directly the unique one, among the canonical forms of decouplable and controllable linear systems, to which a nonlinear system can be transformed via appropriate state feedback and transformation. Finally, we present the necessary and sufficient conditions for our problem to be solvable with internal stability, that is, for stable decoupling.

This paper presents a tree search technique to solve the dynamic control problems. To illustrate the proposed procedure, the swinging control of a pendulum carried by a motordriven cast is discussed as an example. Since the control system is of two degrees and the control problem is a nonlinear one, it is difficult to determine a swinging control rule analytically. However, by means of the proposed tree search approach, the problem can be solved in a relatively easy way. Some numerical calculations axe performed to verify the methodology. The result of the study shows that the proposed tree search technique is suitable for the dynamic control problems, in particular, for the complicated nonlinear dynamic control problem.

A robust nonlinear predictive control strategy using a disturbance estimator is presented. The disturbance estimator is comprised of two parts: one is the disturbance model parameter adaptation and the other is future disturbance prediction. RLSM(recurrsive least square method) with a forgetting factor is used to de the uncertain distance model parameters and for the future disturbance prediction, future process outputs and inputs projected by the process model are used. The simulation results for chemical reactors indicate that a substantial improvement in nonlinear predictive control performance is possible using the disturbance estimator.

Point sets in the Euclidean and digital planes are discussed. The local necessary and sufficient conditions are suggested for pointed lattice extraction from these sets are presented. A number of theorems and corollaries are given. The regular and "almost" regular point sets are studied. The results can be used in automatic control of textured textile images by both general and multiprocessing systems.g systems.

Hazard identification is one of the most important task in process design and operation. This work has focused on the development of a knowledgebased expert system for HAZOP (Hazard and Operability) studies which are regarded as one of the most systematic and logical qualitative hazard identification methodologies but which require a multidisciplinary team and demand much timeconsuming, repetitious work. The developed system enables design engineers to implement existing checklists and past experiences for safe design. It will increase efficiency of hazard identification and be suitable for educational purposes. This system has a framebased knowledge structure for equipment failures/process material properties and rule networks for consequence reasoning which uses both forward and backward chaining. To include wide process knowledge, it is openended and modular for future expansion. An application to LPG storage and fractionation system shows the efficiency and reliability of the developed system.

A tubular reactor model represented by partial differential equations was studied as one of nonlinear distributed parameter optimal control problems. An optimal control theory in the form of maximum principles based on nonlinear integral equations was used to develop an algorithm to solve the problem.

The major purpose here is to estimate the drying time required in the fishdrying process employed. The basic element of the prediction of the drying time is the model or the equation, which governs the change in weight. By an intuitive consideration on the mechanism of dehydration, a mathematical model of the fishdrying process is built, which is described by a system of linear differential equations. Further, a modified system of linear differential equations for a model of drying is also proposed for more accurate estimation. The parameter estimation of this system of equations provides the prediction of necessary drying time.

In this paper, we propose a method to manage production system easily for operators when either equipments or products are changed. And we explain the scheduling AI tool which realizes the proposal method. The tool's knowledge expression consists of models, rules, mathematical expression and fuzzy logic. The model expresses the relations between products and manufacture, and properties of products. The models are separated into three type, equipment model, operation model, and product model. These models are classified by applicable fields as the assembly process or continuous plant process, The model expression of each type is based on object oriented paradigm. We report systems utilizing our approach.

Current available methods for generating assembly sequences have a large undesirable searchspace. This paper presents a method for reducing the searchspace. The method acquires explicitly assembly constraints caused by not only the geometry of parts but also the connectivity between the parts, in simplified form. Then the method generates assembly sequences without searching undesirable tasks using the assembly constraints. If these undesirable tasks are excluded, assembly sequences can be generated by searching only a fraction of all assembly tasks for a product and its subassemblies.

This paper presents a decomposition method to evaluate the performance measures of transfer line with unreliable machine and finite buffers. The proposed method is to decompose the transfer fine into a set of two machine lines for analysis. Nonhomogeneous lines are considered. In such fines, the machines may take the different lengths of time performing operations on parts. A simple transformation is employed in order to replace the line by a homogeneous line. The approximate transformation enables one to determine parameters of performance such as production rate and average buffer levels, and it also shows better applications for a large class of systems.

This paper describes the automatic generation of sequence control programs for DCS(Distributed Control System), PLC(Programable Logic Controller) and so on. Since there is no same manufacturing process, it is difficult to standardize sequence programs. We propose the automatic sequence control program generator which is CAD software using knowledge engineering technique.

In recent years, lots of researches on autonomous mobile robot have been accomplished. However they focused on environment recognition and its processing to make a decision on the motion, And cooperative multirobot, which must be able to avoid crash and to make mutual communication, has not been studied much. This paper deals with cooperative motion of two robots, 'Meari 1" and "Meari 2 " made in our laboratory, based on communication between the two. Because there is an interference on communication occurring in cooperative motion of multirobot, many restrictive conditions are required. Therefore, we have designed these robot system so that communication between them is available and mutual interference is precluded, and we used fuzzy interference to overcome unstability of sensor data.of sensor data.

This paper aims to investigate the navigation control of a mobile robot in a confined environment. Steering angle becomes control variable which is computed from the fuzzy control rules. The identification method proposed in this paper presents the fuzzy control rules obtained through modelling of. the driving actions of human operator. The feasibility of the proposed method is evaluated through the application of the identified fuzzy controls rules to the navigation control of a mobile robot which follows the center of a corridor.

The resolved motion rate control (RMRC) is converting to Joint space trajectory from given Cartesian space trajectory. The RMRC requires the inverse of Jacobian matrix. Since the Jacobian matrix of the redundant robot is generally not square, the pseudoinverse must be introduced. However the pseudoinverse is not easy to be implemented on a digital computer in real time as well as mathematically complex. In this paper, a simple fuzzy resolved motion rate control (FRMRC) that can replace the RMRC using pseudoinverse of Jacobian is proposed. The proposed FRMRC with appropriate fuzzy rules, membership functions and reasoning method can solve the mapping problem between the spaces without complexity. The mapped Joint space trajectory is sufficiently accurate so that it can be directly used to control redundant manipulators. Simulation results verify the efficiency of the proposed idea.

This paper describes a hardware structure and a communication system of a multiple autonomous robots system. Many studies have been devoted to the development of a single autonomous robot. It is, however, also necessary to investigate decentralized multiple autonomous robots system in order to make wider use of such robots. We have been studying a multiple autonomous robots system employing two mobile robots. In this paper, problems are overviewed on the developed multiple autonomous robots system from the viewpoint of hardware and communication, and an improved system is presented, which employs a new control strategy of a mobile robot and realizes reliable data communication between host computers.

An analytic solution approach to the timevarying obstacle avoidance problem is pursued. We formulate the problem in robot joint space(JS), and introduce the viewtime concept to deal with the timevarying obstacles. The viewtime is a set of continuous times in which a timevarying obstacle is viewed and approximated by an equivalent stationary obstacle. The equivalent stationary obstacle is transformed into the JS obstacle. In JS, the path and trajectory avoiding the JS obstacle is planned.

In this paper, a new design technique called Task Based Design (TBD) is proposed to design an optimal robot manipulator for a given task. Optimal design of a manipulator is difficult because it involves implicit and highly nonlinear functions of many design variables for a complex task. TBD designs an optimal manipulator which performs a given task best, by using a framework called Progressive Design which decomposes the complexity of the task into three steps: kinematic design, planning and kinematic control. An example of TBD is presented to demonstrate the efficiency and effectiveness of our framework.

A new method for making automatic electroencephalogram(EEG) report based on the automatic quantitative interpretation of awake EEG was developed. We first analysed a. relationship between EEG reports and quantitative EEG interpretation done by a qualified electroencephalographer(EEGer) for 22 subjects. Based on the analysed relationship and usual process of report making by the EEGer, we defined all terminology necessary for EEG report and established rules for EEG report making. By the combined use of the proposed EEG report making and the method for automatic quantitative EEG interpretation presented at '90 KACC, we were able to make the automatic EEG reports which were equivalent to the EEG reports written by the EEGer. As all the procedures were programmed in a personal computer equipped with an AD (analoguetodigital) converter, the automatic EEG reports were obtained in almost real time in usual actual EEG recording situation with only a few seconds time lag for the analysis in the computer. The proposed report making method and the quantitative EEG interpretation method will be effectively applicable to the clinical use as an assistant tool for physicians.

This paper investigates the EEG waveform distortions caused by the transient responses of various types of signal conditioning filters, which are generally introduced for automated analysis of EEG. This study explicitly simulates the filter responses to the typical EEG waveform models, and compares the distortions. The filter distortion effects are also illustrated with the experiments on real EEG signals.

Decomposition of category mixture in a pixel and its application for supervised image classificationTo make an accurate retrieval of the proportion of each category among mixed pixels (Mixel's) of a remotely sensed imagery, a maximum likelihood estimation method of category proportion is proposed. In this method, the observed multispectral vector is considered as probability variables along with the approximation that the supervised data of each category can be characterized by normal distribution. The results show that this method can retrieve accurate proportion of each category among Mixel's. And a index that can estimate the degree of error in each category is proposed. AS one of the application of the proportion estimation, a method for image classification based on category proportion estimation is proposed. In this method all pixel in a remotely sensed imagery are assumed to be Mixel's, and are classified to most dominant category. Among the Mixel's, there exists unconfidential pixels which should be categorized as unclassified pixels. In order to discriminate them, two types of criteria, Chi square and AIC, are proposed for fitness test on pure pixel hypothesis. Experimental result with a simulated dataset show an usefulness of proposed classification criterion compared to the conventional maximum likelihood criterion and applicability of the fitness tests based on Chi square and AIC,

This paper reports several mathematical properties of the filter vector developed for processing linearlyadditive spatiallyinvariant image sequences. In this filtering of an image sequence into a single filtered image, the information about the image components originally distributed over the entire sequence is compressed into the one new image in a way that the desired component is enhanced and the undesired (interfering) components and noise are suppressed.

Partial derivatives are easily computed analytically assuming that all the geometric information is known. However, there are computational difficulties in getting accurate partial derivatives directly from a range image since an image is a discrete version of continuous data contaminated with some noise. In this paper, we develop a general window function to compute partial derivatives based on the least square surface fitting method. A dynamic selective surface fitting method is introduced to make the window less sensitive to noise. Any degree of partial derivative can be obtained by a simple convolution between an image and window functions.

Control system design for attraction type Maglev system is dealt in this paper. Characteristics of levitation and guidance control is explained and a kind of active guidance controller performance is compared with passive guidance control. Also, a method of using absolute and relative information simultaneously is adopted for levitation control. All the methods studied performed very well in the experiments as well as simulation.

An iterative learrung control scheme is newly designed in tile frequency domain. Purposing for batch process control, a generic form of feedbackassisted firstorder learning is considered first, and the inverse modelbased learning algorithm is derived through convergence analysis in the frequency domain. To enhance the robustness of the proposed scheme, a filtered version is also presented. Performance of the proposed scheme is evaluated through numerical simulations.

In general, recognition of Hand written characters requires to apply an algorithm which takes into consideration of the individual differences. Considering the differences, the authors propose a new method for recognizing Hand written Hangul by parallel procedure analyzing both the segments and the structure of the character. In the previous recognition method proposed by the authors two severe restrictions were placed. The element representing consonant/O/ was closed, and the character elements were separated each other. In order to remove these two restrictions, the authors propose an improved algorithm. It is shown that Hangul in its simplified form is well recognized by using this improved algorithm.

In this paper the issue of convergence rate is introduced for a learning control scheme we have developed and applied for tracking of unknown linear systems. A sufficient condition under which the output trajectory converges exponentially fast is obtained using the controllability grammian of controllable linear systems. Under the same condition it is also shown that the learning control input converges exponentially with the same rate as the rate of output convergence. A numerical example with computer simulation results is presented to show the feasibility of the scheme.

Recently, realization of an intelligent cooperative interaction system between a man and robot systems is required. In this paper, HyperCard with a voice control is used for above system because of its easy handling and excellent human interfaces. Clicking buttons in the HyperCard by a mouse device or a voice command means controlling each joint of a robot system. Robot teaching operation of grasping a bin and pouring liquid in it into a cup is carried out. This robot teaching method using HyperCard provides a foundation for realizing a user friendly cooperative interaction system.

In this paper an identification of nonlinear continuous systems by using neural network is considered. The nonlinear continuous system is identified by two steps. At first, a linear approximate model of the continuous system with nonlinearity is obtained by IIR filtering approach. Then the modeling error due to the nonlinearity is reduced by a neural network compensator. The teaching signals to train the neural network is gotten by smoothing the measurement data corrupted by noise. An illustrative example is given to demonstrate the effectiveness of the proposed approach.

A neural optimization network is designed to solve the collsionfree inverse kinematics problem for redundant robot manipulators under the constraints of joint limits, maximum velocities and maximum accelerations. And the fuzzy rules are proposed to determine the weightings of neural optimization networks to avoid the collision between robot manipulator and obstacles. The inputs of fuzzy rules are the resultant distance, change of the distance and sum of the changes. And the output of fuzzy rules is defined as the capability of collision avoidance of joint differential motion. The weightings of neural optimization networks are adjusted according to the capability of collision avoidance of each joint. To show the validities of the proposed method computer simulation results are illustrated for the redundant robot with three degrees of freedom,

This paper describes a new method of pseudorandom testing of a digital circuit by use of correlation method and a neural network. The authors have recently proposed a new method of fault diagnosis of logical circuit by applying a pseudorandom Msequence to the circuit under test, calculating the crosscorrelation function between the input and the output, and comparing the crosscorrelation functions with the references. This method, called MSEC method, is further extended by using a neural network in order to not only detect the existence of faults but also find the place or location of the faults. An experiment by using a simple digital circuit shows enough applicability of this method to industrial testing of circuit board.

Recognition and tracking system of moving objects based on artificial neural network and PWM controlWe developed a recognition and tracking system of moving objects. The system consists of one CCD video camera, two DC motors in horizontal and vertical axles with encoders, pluse width modulation(PWM) driving unit, 16 bit NEC 9801 microcomputer, and their interfaces. The recognition and tracking system is able to recognize shape and size of a moving object and is able to track the object within a certain range of errors. This paper presents the brief introduction of the recognition and tracking system developed in our laboratory.

Mixed H
$_{2}$ /H$_{\infty}$ robust control synthesis is considered for finite dimensional linear timeinvariant systems under the presence of diagonal structured uncertainties. Such uncertainties arise for instance when there is real perturbation in the nominal model of the state space system or when modeling multiple (unstructured) uncertainty at different locations in the feedback loop. This synthesis problem is reduced to convex optimization problem over a bounded subset of matrices as well as diagonal matrix having certain structure. For computational purpose, this convex optimization problem is further reduced into Generalized Eigenvalue Minimization Problem where a powerful algorithm based on interior point method has been recently developed.. 
A unified approach to continuous and discretetime Nehari problems, based on recently developed results by the authors for the oneblock and Hankelnorm model reduction problems, is proposed. First, we derive discretetime solutions in delta domain where numerical error is small and then we show that the derived form becomes same as the continuous form when the sampling interval approaches to zero.

In this paper a robust stabilization problem is discussed for plant with both timevarying parameter perturbations and unstructured uncertainty. It is shown that, a robust L
$_{2}$ stabilizing controller can be obtained by solving an H$_{\infty}$ standard problem with a scaling parameter. Using an H$_{\infty}$ design method, a robust L$_{2}$ stabilizing controller is derived. Finally, a numerical example is given.n. 
For a high bandwidth, accurate end of arm motion control with good disturbance rejection, the, Momentum Management Approach to Motion control (MMAM) is proposed. The MMAM is a kind of position control technique that uses inertial forces, applied at or near the end of arm to achieve, high bandwidth and accuracy in movement and in the face of force disturbances. To prove the concept of MMAM, the, end point, control of a flexible manipulator is considered. For this purpose, a flexible beam is mounted on the xy table, and the MMAM actuator is attached on the top of the flexible beam. A mathematical model is developed for the flexible, beam being controlled by the, MMAM actuator and slide base DC motor. A system identification method is applied to estimate some system parameters in the, model which can not be determined because of the complexity of the mechanism. For the end point, control of the. flexible beam, the, optimal linear output feedback control is introduced.

A manipulator system that needs significantly large workspace volume and high payload capacity has greater link flexibility than typical industrial robots and teleoperators. If link flexibility is significant, position control of the manipulator's endeffector exhibits the nonminimum phase, noncollocated, and flexible structure system control problems. This paper addresses inverse dynamic trajectory planning issues of a flexible manipulator. The inverse dynamic equation of a flexible manipulator was solved in the time domain. By dividing the inverse system equation into the causal part and the anticausal part, the inverse dynamic method calculates the feedforward torque and the trajectories of all state variables that do not excite structural vibrations for a given endpoint trajectory. Through simulation and experiment with a singleUnk flexible manipulator, the effectiveness of the inverse dynamic method has been demonstrated.

This paper presents a tip position control of a singlelink flexible arm with a payload by using closed loop control. The shifting problem of the arm from the initial position to desired position is considered by the variation of the displacement gain
$G_{p}$ and velocity gain$G_{v}$ . The system is composed of a flexible arm with payload, DC servomotor, and a ballscrew mechanism. The flexible arm is mounted on a mobile stage driven by a servomotor and ballscrew. As a result, the increase of the displacement and velocity gain respectively comes to the reduction of tip vibration. Theoretical results are approximately in good agreement with those obtained experimentally.y.y. 
The tracking control problem of a flexible manipulator with a prismatic joint along a given path is discussed. The nondimensionalization of the elastic part of the manipulator makes it possible to model such a flexible manipulator. For a discontinuous velocity trajectory, an optimal control theory has been applied to formulate the problem. The optimal scheme is given to find the input commands(e.g., joint torques) necessary to produce a, specified end effector motion. Simulated results show the potential use of this scheme for a discontinuous velocity trajectory control.

In this paper robotic manipulators in which the joints exhibit a certain amount of elasticity are considered. Based on a feedback linearized model, sliding mode control system is designed. In the control system design, weak joint stiffness assumption does not needed. Simulation results are presented to verify the validity of the control scheme. A robustness analysis for a feedback linearized model is also given with respect to uncertainties on the robot parameters.

This paper deals with the architecture of an information processing model for the human concept understanding required in constructing intelligent manmachine interfaces. The architecture employed is a parallel processing by networking. For this purpose, personal computers are interconnected by LAN and are, in their roles, divided into three levels. A concept has two aspects; i.e., language and image. In the present model, the system as the holistic whole of personal computers together with peripheral devices processes visual information in cognitive level, searching for feasible solutions from a variety of aspects. An image inputted through peripheral systems is categorized and matched with those ever experienced with the aid of that categorization, and thus an image is identified.

Communication using a language sometimes leads to the partner's misconception, as the content that a language can describe is not sufficient enough to accurately transmit one's idea in his mind. In order to supplement this difficulty, introduction of idea of imagery to link with verbal information applying the notion of prototype which is learned through experiencing and is a part of the idea of experience sequence to deal with experiencing.

In the human cognitive activity, experiencing plays a basic role. This is modeled by the idea of experience sequence here, which has been proposed by the author for the incorporation of the factor of experiencing in manmachine communication. Experience sequence is for modeling the human concept formation through experiencing. Knowledge manipulation requires concept understanding as its basis. An experience sequence deals with such a process of concept formation.