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Dynamic neural network identification and decoupling control approach for MIMO time-varying nonlinear systems. (English) Zbl 1406.93153

Summary: Overcoming the coupling among variables is greatly necessary to obtain accurate, rapid and independent control of the real nonlinear systems. In this paper, the main methodology, on which the method is based, is dynamic neural networks (DNN) and adaptive control with the Lyapunov methodology for the time-varying, coupling, uncertain, and nonlinear system. Under the framework, the DNN is developed to accommodate the identification, and the weights of DNN are iteratively and adaptively updated through the identification errors. Based on the neural network identifier, the adaptive controller of complex system is designed in the latter. To guarantee the precision and generality of decoupling tracking performance, Lyapunov stability theory is applied to prove the error between the reference inputs and the outputs of unknown nonlinear system which is uniformly ultimately bounded (UUB). The simulation results verify that the proposed identification and control strategy can achieve favorable control performance.

MSC:

93C35 Multivariable systems, multidimensional control systems
93C10 Nonlinear systems in control theory
93B30 System identification
68T05 Learning and adaptive systems in artificial intelligence
93C40 Adaptive control/observation systems
93C41 Control/observation systems with incomplete information
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