• Journal of the Korean Institute of Telematics and Electronics T
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• v.35T no.3
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• pp.87-95
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• 1998

This paper presents the method for the automatic tuning of a design weighting polynomial parameter of a generalized minimum-variance stochastic self tuning controller which adapts to changes in the system parameters with time delays and noises. The self tuning effect is achieved through the recursive least square algorithm at the parameter estimation stage and also through the Robbins-Monro algorithm at the stage of optimizing a design weighting polynomial parameters. The proposed self tuning method is simple and effective compared with other existing self tuning methods. The computer simulation results are presented to illustrate the procedure and to show the performance of the control system.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.6
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• pp.1-10
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• 2007

This paper presents a direct nonlinear multivariable self-tuning PID controller using neural network which adapts to the changing parameters of the nonlinear multivariable system with noises and time delays. The nonlinear multivariable system is divided linear part and nonlinear part. The linear controller are used the self-tuning PID controller that can combine the simple structure of a PID controllers with the characteristics of a self-tuning controller, which can adapt to changes in the environment. The linear controller parameters are obtained by the recursive least square. And the nonlinear controller parameters are achieved the through the Back-propagation neural network. In order to demonstrate the effectiveness of the proposed algorithm, the computer simulation results are presented to adapt the nonlinear multivariable system with noises and time delays and with changed system parameter after a constant time. The proposed PID type nonlinear multivariable self-tuning method using neural network is effective compared with the conventional direct multivariable adaptive controller using neural network.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.11
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• pp.22-33
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• 1999

This paper presents the method for the automatic tuning of a design weighting polynomial parameters of a generalized minimum-variance stochastic ultivariable self-tuning controller which adapts to changes in the higher order nonminimum phase system parameters with time delays and noises. The self-tuning effect is achieved through the recursive least square algorithm at the parameter estimation stage and also through the Robbins-Monro algorithm at the stage of optimizing the design weighting polynomial parameters of the controller. The proposed multivariable self-tuning method is simple and effective compared with pole restriction method. The computer simulation results are presented to adapt the higher order multivariable system with nonminimum phase and with changeable system parameters.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.41 no.4
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• pp.21-28
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• 2004

This paper presents a direct multivariable self-tuning controller using neural network which adapts to the changing parameters of the higher order multivariable nonlinear system with nonminimum phase behavior, mutual interactions and time delays. The nonlinearities are assumed to be globally bounded, and a multivariable nonlinear system is divided linear part and nonlinear part. The neural network is used to estimate the controller parameters, and the control output is obtained through estimated controller parameter. In order to demonstrate the effectiveness of the proposed algorithm the computer simulation is done to adapt the multivariable nonlinear nonminimm phase system with time delays and changed system parameter after a constant time. The proposed method compared with direct multivariable adaptive controller using neural network.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.40 no.4
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• pp.264-274
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• 2003

This paper presents a direct generalized minimum-variance self tuning controller with a PID structure using neural network which adapts to the changing parameters of the nonlinear system with nonminimum phase behavior, noises and time delays. The self-tuning controller with a PID structure is a combination of the simple structure of a PID controller and the characteristics of a self-tuning controller that can adapt to changes in the environment. The self-tuning control effect is achieved through the RLS (recursive least square) algorithm at the parameter estimation stage as well as through the Robbins-Monro algorithm at the stage of optimizing the design parameter of the controller. The neural network control effect which compensates for nonlinear factor is obtained from the learning algorithm which the learning error between the filtered reference and the auxiliary output of plant becomes zero. Computer simulation has shown that the proposed method works effectively on the nonlinear nonminimum phase system with time delays and changed system parameter.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.754-758
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• 1996

This paper presents a new approach to the design of self-tuning adaptive control system that is robust to the changing dynamic configuration as well as to the load variation factors using Digital signal processors for robot manipulators. TMS320C50 is used in implementing real-time adaptive control algorithms to provide advanced performance for robot manipulator, In this paper, an adaptive control scheme is proposed in order to design the pole-placement self-tuning controller which can reject the offset due to any load disturbance without a detailed description of robot dynamics. Parameters of discrete-time difference model are estimated by the recursive least-square identification algorithm, and controller parameters we determined by the pole-placement method. Performance of self-tuning adaptive controller is illusrated by the simulation and experiment for a SCARA robot.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1996.10a
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• pp.150-155
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• 1996

This paper presents a new approach to the design of self-tuning adaptive control system that is robust to the changing dynamic configuration as well as to the load variation factors using digital signal processors for robot manipulators. TMS3200C50 is used in implementing real-time adaptive control algorithms provide advanced performance for robot manipulator. In this paper an adaptive control scheme is proposed in order to design the pole-placement self-tuning controller which can reject the offset due to any load disturbance without a detailed description of robot dynamics. parameters of discrete-time difference model are estimated by the recursive least-square identification algorithm and controller parameters are detemined by the pole-placement method. Performance of self-tuning adaptive controller is illusrated by the simulation and experiment for a SCARA robot.

• ICROS
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• v.3 no.5
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• pp.38-42
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• 1997

본 고에서는 적응제어의 설계 기법의 연구 현황에 대해 살펴보았다. 매개변수 추정을 기반으로 적응제어기 설계 기법(Estimation-based Design Technique)은 자기동조제어를 시작으로 적응 최적제어 단계를 거쳐, 현재 다변수 적응예측제어로 발전하였고, 이는 화학공정 등 실제 플랜트에 응용될 가능성이 가장 높은 실용적인 설계기법으로 평가받고 있다. 그러나 비선형 시스템에 대한 적용 연구는 활발하지 못한 형편이다. 한편, 안정도 이론을 기반으로 적응 메카니즘을 설계하는 연구는 선형 시스템에 대한 적응제어 연구로 확장되어 현재 활발한 연구가 진행되고 있다. 앞으로 이에 대한 이론 연구가 한동안 지속될 것으로 전망된다. 그러나 이 방법은 실제 시스템에 적용시 적합한 형태로 불확실성을 모델링해야 하며 그 작업은 그리 용이하지 않을 것으로 판단된다. 그러나 부분적으로 이러한 응용연구에 대한 노력이 경주되어야 할 것이다. 비매개변수 추정방법은 적응제어 이론의 주 설계기법은 아니기 때문에 이론으로의 확장 연구는 그리 활발하지 못한 것으로 판단된다. 그러나 비교적 설계방법과 불확실성 모델링이 간단하여 실제 응용에 가장 용이하게 적용될 수 있을 것으로 생각된다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.15 no.8
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• pp.708-714
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• 1990

This paper has proposed a self-tuning controller for tracking reference trajectory by measuring End-point of arm on robot manipulator whose link is light and flexibls, and proved the perforformance of the algorithm proposed through the computer simulation. As an object of control, a flexible robot manipulator with two-links was selected. As for structure of model, it utilized an assume mode shape method with include travity force and derived a dynaics equation by adapting two kinds of vibration mode of each.

• Nuclear Engineering and Technology
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• v.27 no.6
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• pp.833-844
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• 1995

In self-tuning predictive control algorithm for steam generator is presented. The control algorithm is derived by suitably modifying the generalized predictive control algorithm. The main feature of the unposed method relies on considering the measurable disturbance and a simple adaptive scheme for obtaining the controller gain when the parameters of the plant are unknown. This feature makes the proposed approach particularly appealing for water level control of steam generator when measurable disturbance is used. In order to evaluate the performance of the proposed algorithm, computer simulations are done for an PWR steam generator model. Simulation result show satisfactory performances against load variations and steam flow rate estimation errors. It can be also observed that the proposed algorithm exhibit better responses than a conventional PI controller.