Abstract
In this paper, a command-filter based neural network adaptive control method is proposed for high-order nonlinear systems with strict feedback. The command filter is used to avoid the computational complexity explosion problem of the traditional backstepping method, and the filtering error compensation mechanism is designed to reduce the impact of filtering error on the performance of the closed-loop system. In addition, the radial basis function neural network is used to deal with the uncertainty of the system, so as to improve the robustness and tracking performance of the controller. By constructing Lyapunov function, the asymptotic stability of the closed-loop system is proved theoretically. Simulation results show that the designed controller can achieve accurate and fast tracking of the reference signal and significantly improve the system performance.
Published Version
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