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Design of a Direct Self-tuning Controller Using Neural Network  

조원철 (경도대학교 인터넷정보통신계열)
이인수 (상주대학교 전자 전기공학부)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SC / v.40, no.4, 2003 , pp. 264-274 More about this Journal
Abstract
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.
Keywords
generalized minimum-variance self-tuning controller; neural network; design parameter; nonminimum phase system; nonlinear system;
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Times Cited By KSCI : 2  (Citation Analysis)
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