Robust backstepping control of a class of nonlinear systems using fuzzy logic
Information Sciences: an International Journal - Special issue analytical theory of fuzzy control with applications
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International Journal of Computer Applications in Technology
International Journal of Computer Applications in Technology
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International Journal of Computer Applications in Technology
Adaptive neural network control for strict-feedback nonlinear systems using backstepping design
Automatica (Journal of IFAC)
IEEE Transactions on Neural Networks
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Based on cerebellar model articulation controller CMAC neural network and dynamic surface, a novel design scheme of adaptive backstepping and sliding controller for a class of hypersonic vehicle non-linear uncertain MIMO systems is proposed in this paper. Control law is designed by backstepping. At each step, the design of a sliding surface with a virtual zero-order controller is given. The robustness with the using of sliding mode control is to ensure the stability of each uncertain subsystem. Uncertain items of system and virtual derivative are online approached by employing CMAC neural network. A second-order filter is instead of virtual control input to avoid calculation expansion caused by multiple differentials. By Lyapunov stability analysis, the method can guarantee that the tracking errors of closed-loop system are finally convergence. Simulation results show that the control law designed can effectively suppress system uncertainties and the design method is feasible.