Title :
Self-tuning PID control by neural-networks
Author :
Akhyar, Saiful ; Omatu, Sigeru
Author_Institution :
Dept. of Inf. Sci. & Intelligent Syst., Tokushima Univ., Japan
Abstract :
It has been proved that a multilayered neural network (NN) can approximate any continuous function within arbitrarily small error. Thus, NNs enable us to represent any nonlinear system. Furthermore, adaptive control theory or parameter tuning of PID controller is mainly limited in linear systems. In this paper, using a nonlinear mapping capability of NNs, we derive a tuning method of PID controller based on a backpropagation method of multilayered NNs. Simulated and experimental results show that the proposed method can identify the appropriate parameters of PID controller when it is implemented to both linear and nonlinear plants.
Keywords :
adaptive control; backpropagation; feedforward neural nets; neurocontrollers; nonlinear systems; self-adjusting systems; three-term control; adaptive control; backpropagation; multilayered neural network; nonlinear mapping; nonlinear system; parameter tuning; self-tuning PID control; Adaptive control; Control systems; Neural networks; Neurons; PD control; Pi control; Process control; Proportional control; Three-term control; Tuning;
Conference_Titel :
Neural Networks, 1993. IJCNN '93-Nagoya. Proceedings of 1993 International Joint Conference on
Print_ISBN :
0-7803-1421-2
DOI :
10.1109/IJCNN.1993.714292