• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.63-67
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• 2004

The problem is improving the positioning precision of a permanent magnet linear synchronous motor (PMLSM). Thus, this paper presents the design and realization of an adaptive dither to reduce the force ripple in PMLSM. A composite control structure is used, consisting of three components: a simple feed-forward component, a PID feedback component and an adaptive feed-forward compensator (AFC). Especially adaptive feed-forward component cancel out detent force using wavelet transformation. Computer simulation results verify the effectiveness of the proposed scheme for high precision motion trajectory tracking using the PMLSM

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.189-194
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• 1994

Adaptive control systems based upon the command generator tracker(CGT) approach have attracted considerable interest because of the simple structure of its adaptive controller. Some attempts to such improve the adaptive control algorithm, for the sake of the application to broader class of plants, are made. Recently, Su and Sobel(1992) proposed that those schemes can be treated by an unified theory using a metasystem representation with some types of supplementary dynamics. However, in their method, it is difficult to find the dynamic compensator, which is proper and output feedback stabilizable, for the uncertain plant. This paper proposes a new design method of such supplementary dynamics and some parameters of adaptive control system for linear time invariant SISO plants. The method gives a concrete and systematic design method using only a few priori knowledge of the plant.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.9
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• pp.1821-1826
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• 2009

An adaptive neural controller for perturbed strict-feedback nonlinear system is proposed. All the previous adaptive neural (or fuzzy) controllers are based on the backstepping scheme where the universal approximators are employed in every design steps. These schemes involve virtual controls and their time derivatives that make the stability analysis and implementation of the controller very complex. This fact is called 'explosion of complexty ' since the complexity grows exponentially as the system dynamic order increases. The proposed adaptive neural control scheme adopt the backstepping design procedure only for determining ideal control law and employ only one neural network to approximate the finally selected ideal controller, which makes the controller design procedure and stability analysis considerably simple compared to the previously proposed controllers. It is shown that all the time-varing signals containing tracking error are stable in the Lyapunov viewpoint.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1997.04a
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• pp.131-136
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• 1997

This paper represent the variable structure adaptive mode control technique which is new approach to implement the robust control of industrial robot manipulator with external disturbances and parameter uncertainties. Sliding mode control is a well-known technique for robust control of uncertain nonlinear systems. The robustness of sliding model controllers can be shown in contiuous time, but digital implementation may not preserve robustness properties because the sampling process limits the existence of a true sliding mode. the sampling process often forces the trajectory to oscillate in the neighborhood of the sliding surface. Adaptive control technique is particularly well-suited to robot manipulators where dynamic model is highly complex and may contain unknown parameters. Adaptive control algorithm is designed by using the principle of the model reference adaptive control method based upon the hyperstability theory. The proposed control scheme has a simple sturcture is computationally fast and does not require knowledge of the complex dynamic model or the parameter values of the manipulator or the payload. Simulation results show that the proposed method not only improves the performance of the system but also reduces the chattering problem of sliding mode control, Consequently, it is expected that the new adaptive sliding mode control algorithm will be suited for various practical applications of industrial robot control system.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1955-1959
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• 2005

This paper presents a systematic analysis and a simple design of a robust adaptive control law for a class of non linear systems with modeling errors and a time-delay input. The theory for designing a robust adaptive control law based on input- output feedback linearization of non linear systems with uncertainties and a time-delay in the manipulated input by the approach of parameterized state feedback control is presented. The main advantage of this method is that the parameterized state feedback control law can effectively suppress the effect of the most parts of nonlinearities, including system uncertainties and time-delay input in the pp-coupling perturbation form and the relative order of non linear systems is not limited.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.71.3-71
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• 2001

Passive Stabilization method and active stabilization method are mainly used to comprise a control system of platform stabilizer. Passive Stabilization method has demerits because of size and weight except that control structure is simple while active stabilization method using sensors can reduce size and weight, it requires high sensor technique and control algorithm. In this paper, a stabilizing controller using adaptive fuzzy control technique and floating-point processor(DSP) is suggested.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.5
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• pp.315-319
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• 2016

This paper proposes a simple adaptive sliding mode control algorithm for controlling a permanent magnet synchronous generator (PMSG) of a MW-class direct-driven wind turbine system. The proposed adaptive sliding mode controller does not require accurate knowledge of the PMSG parameter or turbine torque values. The proposed controller can accurately track the reference angular speed computed by the maximum power point tracking(MPPT) algorithm. Finally, this paper gives Matlab/Simulink simulation results to verify the practicality and effectiveness of the proposed adaptive sliding mode controller.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.46-51
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• 2004

This paper presents an adaptive control method based on parameter linearization for incompletely restrained wire-suspended mechanisms. The main purpose of this control method is utilizing it in a walking assist service robot for elderly people. This method is computationally simple and requires neither end-effector acceleration feedback nor inversion of estimated inertia matrix. In the proposed adaptive control law, mass, moment of inertia and external force and torque on the end-effector are considered as components of parameter adaptation vector. Nonlinear simulation for walking an elderly shows the effectiveness of the parameter adaptation law.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.8
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• pp.647-655
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• 2001

In adaptive neuro-control, neural networks are used to approximate unknown plant nonlinearities. Until now, most of the studies in the field of controller design for nonlinear system using neural network considers the affine system with fixed number of neurons. This paper considers nonaffine nonlinear systems and on-line variation of the number of neurons. A control law and adaptive laws for neural network weights are established so that the whole system is stable in the sense of Lyapunov. In addition, at the expense of th input, tracking error converges to the arbitrary small neighborhood of the origin. The efficiency of the proposed scheme is shown through simulations ofa simple nonaffine nonlinear system.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.4
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• pp.330-340
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• 2002

In this Paper. a non-linear approach to a design of model reference adaptive control is presented. The approach is demonstrated by a case study of a simple single-pole and no zero, linear, discrete-time plant. The essence of the idea is to generate a full non-linear model of the plant dynamics and the parameter adaptation dynamics as a gradient descent algorithm with respect to a Riemannian metric. It is shown how a Riemannian metric can be chosen so that the modelled plant dynamics do in fact match the true plant dynamics. The performance of the proposed scheme is compared to a traditional model reference adaptive control scheme using the classical sensitivity derivatives (Euclidean gradients) for the descent algorithm.