Title :
Application of neural networks to nonlinear adaptive control systems
Author :
Lu, Jianming ; Yahag, Takashi
Author_Institution :
Graduate Sch. of Sci. & Technol., Chiba Univ., Japan
Abstract :
This paper presents a method of model reference adaptive control for nonlinear systems using neural networks. The control input is given by the sum of the output of a model reference adaptive controller and the output of the neural network. The neural network is used to compensate the nonlinearity of the plant dynamics that is not taken into consideration in the usual model reference adaptive control. The role of the neural network is to construct a linearized model by minimizing the output error caused by nonlinearities in the control systems. New parallel neural networks are proposed
Keywords :
learning (artificial intelligence); model reference adaptive control systems; neural net architecture; nonlinear control systems; parallel architectures; control input; learning; linearized model; model reference adaptive control; nonlinear adaptive control systems; nonlinear systems; nonlinearity compensation; output error minimisation; parallel neural networks; plant dynamics; Adaptive control; Control nonlinearities; Control system synthesis; Error correction; Neural networks; Nonlinear control systems; Nonlinear dynamical systems; Nonlinear systems; Programmable control; Regulators;
Conference_Titel :
Signal Processing Proceedings, 2000. WCCC-ICSP 2000. 5th International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7803-5747-7
DOI :
10.1109/ICOSP.2000.894486
