• Journal of the Korean Society for Precision Engineering
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• v.15 no.4
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• pp.15-26
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• 1998

Full car model is needed for investigating as a entire dynamics of vehicle. In this study, 7DOF of full car model's dynamics is selected. This paper proposes the output feedback controller based on optimal control theory. Input data and output data from the optimal controller are used for neural network system identification of the suspension system. To do system identification, neural network which has robustness against nonlinearities and disturbances is adapted. This study uses back-propagation algorithm to train a multil-layer neural network. After obtaining a neural network model of a suspension system, a neuro-controller is designed. Neuro-controller controls suspension system with off-line learning method and multistep ahead prediction model based on the neural network model and a neuro-controller. The optimal controller and the neuro-controller are designed and then, both performances are compared through. For simulation, sinusoidal and rectangular virtual bumps are selected.

• Journal of Power Electronics
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• v.12 no.5
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• pp.769-777
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• 2012

In this paper, a new development in the time optimal control theory in sliding mode control systems for multi-quadrant buck converters with a variable load is presented. In general, the closed-loop time optimal control system is applied to multi-quadrant buck converters for output regulation, so that an optimal switching surface is obtained. Moreover, an adjusted optimal sliding mode controller is suggested which adjusts the controller parameters to give an optimal switching surface. In addition, a description of the transient response of the closed-loop system is proposed and used to damp any output or input disturbances in minimum time. Numerical simulations and experimental results are employed to demonstrate that the output regulation time and transient performances of dc/dc converters using the proposed technique are improved effectively when compared to the classical sliding mode control method.

• Nuclear Engineering and Technology
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• v.35 no.5
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• pp.399-411
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• 2003

A model predictive control method is applied to design an automatic controller for thermal power control in a reactor core. The basic concept of the model predictive control is to solve an optimization problem for a finite future at current time and to implement as the current control input only the first optimal control input among the solutions of the finite time steps. At the next time step, the second optimal control input is not implemented and the procedure to solve the optimization problem is then repeated. The objectives of the proposed model predictive controller are to minimize the difference between the output and the desired output and the variation of the control rod position. The nonlinear PWR plant model (a nonlinear point kinetics equation with six delayed neutron groups and the lumped thermal-hydraulic balance equations) is used to verify the proposed controller of reactor power. And a controller design model used for designing the model predictive controller is obtained by applying a parameter estimation algorithm at an initial stage. From results of numerical simulation to check the controllability of the proposed controller at the $5\%/min$ ramp increase or decrease of a desired load and its $10\%$ step increase or decrease which are design requirements, the performances of this controller are proved to be excellent.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.28-33
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• 1994

An optimal controller, e.g. LQG controller, may not be realistic in the sense that the required control power may not be achieved by existing actuators, and the measured output is not satisfactory. To be realistic, the controller should meet such constraints as sensor or actuator limitation, performance limit, etc. In this paper, the lnput/Output Variance Constrained (IOVC) control problem will be considered from the viewpoint of mathematical programming. A dual version shall be developed to solve the IOVC control problem, whose objective is to find a stabilizing control law attaining a minimum value of a quadratic cost function subject to the inequality constraint on each input and output variance for a stabilizable and detectable plant. One approach to the constrained optimization problem is to use the Kuhn-Tucker necessary conditions for the optimality and to seek an optimal point by an iterative algorithm. However, since the algorithm uses only the necessary conditions, the convergent point may not be optimal solution. Our algorithm will guarantee a sufficiency.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.05a
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• pp.452-456
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• 2004

A study on gust loads alleviation using aircraft control surface was performed. Aeroservoelastic model including control surface controller was formulated and validated by comparing the results of continuous turbulence response analysis with those of MSC/NASTRAN. Optimal control with output feedback was adopted for designing the control surface controller, and the effects of gust loads alleviation was validated by performing the numerical simulation for the controller designed.

• Proceedings of the IEEK Conference
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• 2003.07c
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• pp.2493-2496
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• 2003

This paper presents an optimal tuning method of decentralized PID controller of two-input, two-output(TITO) second order system to be formulated as LQR.

• Journal of Electrical Engineering and Technology
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• v.12 no.3
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• pp.1146-1155
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• 2017

Normally, the artificial intelligence algorithms are widely applied to the optimal controller design. Then, it is expected that the best output performance is achieved. Unfortunately, when resulting controller parameters are implemented by using the practical devices, the output performance cannot be the best as expected. Therefore, the paper presents the optimal controller design using the combination between the state-space averaging model and the adaptive Tabu search algorithm with the new criteria as two penalty conditions to handle the mentioned problem. The buck-boost converter regulated by the cascade PI controllers is used as the example power system. The results show that the output performance is better than those from the conventional design method for both input and load variations. Moreover, it is confirmed that the reported controllers can be implemented using the realistic devices without the limitation and the stable operation is also guaranteed. The results are also validated by the simulation using the topology model of MATLAB and also experimentally verified by the testing rig.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.32 no.6
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• pp.211-218
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• 1983

In this paper, an attempt is made to match the continuous state trajectory of the digital control system with that of its continuous data model. Matching the state trajectories instead of the output responses assures that the performances of the internal variables of the plant as well as the output variables are preserved in the discretization. The mathematical tool used in this research is an extended maximum principle of the Pontryagin type, which enables one to synthesize a staircase type of optimal control signals, such as the output signal of a zero-order hold asociated with a digital controller. A general mathematical expression of the digital controller which may be used to replace the analog controller of a general system while preserving as mauch as possible the performance characteristics of the original continuous-data control system is derived in this paper.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.276-280
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• 1992

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 time-delay. Then, computer simulations are conducted at step input and the performances are evaluated in the ITAE.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.830-833
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• 1993

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