Title :
Practical stability issues in CMAC neural network control systems
Author :
Chen, Fu-Chuang ; Chang, Chih-Homg
Author_Institution :
Dept. of Control Eng., Nat. Chiao Tung Univ., Hsinchu, Taiwan
fDate :
1/1/1996 12:00:00 AM
Abstract :
The cerebellar model articulation controller (CMAC) neural network is a practical tool for improving existing nonlinear control systems. A typical simulation study is used to clearly demonstrate that CMAC can effectively reduce tracking error, and also destabilize a control system which is otherwise stable. Then quantitative studies are presented to search for the cause of instability in the CMAC control system. Based on these studies, methods are discussed to improve system stability. Experimental results on controlling a real world system are provided to support the findings in simulations
Keywords :
cerebellar model arithmetic computers; discrete time systems; learning (artificial intelligence); neurocontrollers; nonlinear control systems; stability; CMAC neural network; cerebellar model articulation controller; discrete time systems; generalisation; learning rate; neural control; nonlinear control systems; quantisation; stability; tracking error; Control systems; Electrical equipment industry; Intelligent networks; Neural networks; Nonlinear control systems; PD control; Pi control; Proportional control; Stability; Three-term control;
Journal_Title :
Control Systems Technology, IEEE Transactions on
