RBF NN-Based Backstepping Control for Strict Feedback Block Nonlinear System and Its Application
Based on neural networks, a robust control design method is proposed for strict-feedback block nonlinear systems with mismatched uncertainties. Firstly, Radial-Basis-Function (RBF) neural networks are used to identify the nonlinear parametric uncertainties of the system, and the adaptive tuning rules for updating all the parameters of the RBF neural networks are derived using the Lyapunov stability theorem to improve the approximation ability of RBF neural networks on-line. Considering the known information, neural network and robust control are used to deal with the design problem when control coefficient matrices are unknown and avoid the possible singularities of the controller. For every subsystem, a nonlinear tracking differentiator is introduced to solve the “computer explosion” problem in backstepping design. It is proved that all the signals of the closed-loop system are uniform ultimate bounded.
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