Title :
A PID-like controller for nonlinear systems
Author :
Jin, Wang ; Wenzhong, Gao ; Fuli, Wang
Author_Institution :
Dept. of Autom. Control, Northeastern Univ., ShenYang, China
Abstract :
This paper presents an adaptive PID-like controller (PIDLC) using a modified neural network (MNN) for learning the characteristics of a dynamic system. The PIDLC can adapt parameters variation and uncertainty in the controlled plant through online learning. The MNN´s learning algorithm is considerably faster because of the introduction of recursive least squares algorithm. The simulation results show that this kind of control algorithm is very effective especially when there are variations in the plant dynamics
Keywords :
adaptive control; learning (artificial intelligence); least squares approximations; neurocontrollers; nonlinear systems; recurrent neural nets; three-term control; PID-like controller; adaptive control; learning algorithm; nonlinear systems; recurrent neural network; recursive least squares algorithm; system dynamics; Adaptive control; Control systems; Least squares methods; Multi-layer neural network; Neural networks; Nonlinear control systems; Nonlinear dynamical systems; Nonlinear systems; Programmable control; Uncertainty;
Conference_Titel :
American Control Conference, 1997. Proceedings of the 1997
Conference_Location :
Albuquerque, NM
Print_ISBN :
0-7803-3832-4
DOI :
10.1109/ACC.1997.610826
