DocumentCode :
2749171
Title :
Adaptive control of a class of nonlinear systems using neural networks
Author :
Chen, Fu-Chuang ; Khalil, Hassan K.
Author_Institution :
Dept. of Control Eng., Nat. Chiao Tung Univ., Hsinchu, Taiwan
Volume :
3
fYear :
1995
fDate :
13-15 Dec 1995
Firstpage :
2427
Abstract :
Layered neural networks are used in nonlinear adaptive control problems. Both the discrete-time case and the continuous-time case are considered. For the discrete-time case, the plant is an unknown SISO feedback-linearizable discrete-time system with general relative degree, represented by an input-output model; a state space model of the plant is obtained. This model is used to define the zero dynamics, which are assumed to be stable. A layered neural network is used to model the unknown system and generate the feedback control. Based on the error between the plant output and the model output, the weights of the neural network are updated. For the continuous-time case, we work on a SISO relative-degree-one system with zero dynamics, and on a MIMO general relative degree system without zero dynamics. The neural network is used to model the nonlinear functions of the continuous-time systems, instead of modeling the whole system as in the discrete-time case; and the control law for the continuous-time case does not involve an explicit system identification process, as appears in the discrete-time case. The convergence results obtained for both cases are regional in state space, yet local in parameter space
Keywords :
MIMO systems; adaptive control; continuous time systems; discrete time systems; dynamics; feedforward neural nets; neurocontrollers; nonlinear systems; state-space methods; MIMO general relative degree system; SISO feedback-linearizable systems; SISO relative-degree-one system; adaptive control; continuous-time systems; discrete-time systems; layered neural networks; nonlinear systems; state space model; zero dynamics; Adaptive control; Error correction; Feedback control; MIMO; Neural networks; Nonlinear control systems; Nonlinear dynamical systems; Nonlinear systems; State-space methods; System identification;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Decision and Control, 1995., Proceedings of the 34th IEEE Conference on
Conference_Location :
New Orleans, LA
ISSN :
0191-2216
Print_ISBN :
0-7803-2685-7
Type :
conf
DOI :
10.1109/CDC.1995.478454
Filename :
478454
Link To Document :
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