Adaptive Neural Control for a Class of MIMO Non-linear Systems with Guaranteed Transient Performance
A robust adaptive control scheme is presented for a class of uncertain continuous-time multi-input multi-output (MIMO) nonlinear systems. Within these schemes, multiple multi-layer neural networks are employed to approximate the uncertainties of the plant’s nonlinear functions and robustifying control term is used to compensate for approximation errors. All parameter adaptive laws and robustifying control term are derived based on Lyapunov stability analysis so that all the signals in the closed loop are guaranteed to be semi-globally uniformly ultimately bounded and the tracking error of the output is proven to converge to a small neighborhood of zero. While the relationships among the control parameters, adaptive gains and robust gains are established to guarantee the transient performance of the closed loop system.
KeywordsClose Loop System Tracking Error Radial Basis Function Neural Network Adaptive Gain Neural Network Weight
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