Uncertain Chaotic System Control via Adaptive Neural Design

Though chaotic behaviors are exhibited in many simple nonlinear models, physical chaotic systems are much more complex and contain many types of uncertainties. This paper presents a robust adaptive neural control scheme for a class of uncertain chaotic systems in the disturbed strict-feedback form, with both unknown nonlinearities and uncertain disturbances. To cope with the two types of uncertainties, we combine backstepping methodology with adaptive neural design and nonlinear damping techniques. A smooth singularity-free adaptive neural controller is presented, where nonlinear damping terms are used to counteract the disturbances. The differentiability problem in controlling the disturbed strict-feedback system is solved without employing norm operation, which is usually used in robust control design. The proposed controllers can be applied to a large class of uncertain chaotic systems in practical situations. Simulation studies are conducted to verify the effectiveness of the scheme.

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