Abstract
Adaptive neural network sliding mode control problem of a class of dynamic systems with uncetain and nonlinear characteristics is studied. Firstly, the RBF neural network and adaptive control are respectively applied to approximate the uncertainty part of dynamic systems and to estimate the external disturbance part of dynamic systems. Then, by combining the Lyapunov stability theory and sliding mode control theory, an adaptive neural network sliding mode tracking controller is designed such that the state trajectories of the tracking error system can be driven onto the sliding mode surface, and the asymptotic convergence of the tracking error is guaranteed. Finally, a simulation example verifes the effectiveness of the developed control approach.
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