신경회로망과 유전알고리즘을 이용한 과감쇠 시스템용 자기동조 PID 제어기의 설계

Design of a Self-tuning PID Controller for Over-damped Systems Using Neural Networks and Genetic Algorithms

  • 진강규 (한국해양대학교 기계ㆍ정보공학부) ;
  • 유성호 (㈜Rotem 전장품 개발팀) ;
  • 손영득 (㈜현대중공업 선박해양연구소 제품개발팀)
  • 발행 : 2003.01.01

초록

The PID controller has been widely used in industrial applications due to its simple structure and robustness. Even if it is initially well tuned, the PID controller must be retuned to maintain acceptable performance when there are system parameter changes due to the change of operation conditions. In this paper, a self-tuning control scheme which comprises a parameter estimator, a NN-based rule emulator and a PID controller is proposed, which can cope with changing environments. This method involves combining neural networks and real-coded genetic algorithms(RCGAs) with conventional approaches to provide a stable and satisfactory response. A RCGA-based parameter estimation method is first described to obtain the first-order with time delay model from over-damped high-order systems. Then, a set of optimum PID parameters are calculated based on the estimated model such that they cover the entire spectrum of system operations and an optimum tuning rule is trained with a BP-based neural network. A set of simulation works on systems with time delay are carried out to demonstrate the effectiveness of the proposed method.

키워드

참고문헌

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