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CMAC neural networks-based adaptive control for discrete-time nonlinear systems with unmatched uncertainties by backstepping

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4 Author(s)
Youan Zhang ; Dept. of Autom. Control Eng., Naval Aeronaut. Eng. Acad., Yantai, China ; Yun-an Hu ; Zhao-Qing Song ; Ping-Yuan Cui

Considers adaptive control for a class of SISO discrete-time nonlinear systems with unmatched uncertainties. The discrete-time nonlinear systems with unmatched uncertainties are firstly transformed into a class of new discrete-time nonlinear systems with matched uncertainties, and a CMAC neural network-based controller which linearizes the new discrete-time nonlinear systems is presented. Secondly, the states of the new discrete-time nonlinear systems are estimated using CMAC neural networks by backstepping. A stability proof is given in the sense of Lyapunov using the persistency of excitation (PE) condition. It is shown that all the signals in the closed-loop system are uniformly ultimately bounded. A simulation example is also given

Published in:

Intelligent Control and Automation, 2000. Proceedings of the 3rd World Congress on  (Volume:5 )

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