No abstract

No abstract

This paper presents an observer-based hybrid adaptive cerebellar model articulation controller (CMAC) with a supervisory controller for uncertain chaotic systems, in which the hybrid adaptive control composed of a direct adaptive CMAC and an indirect adaptive CMAC control is performed as the sliding mode control (SMC). The total states of the chaotic system are not assumed to be available for measurement. A state observer is used to estimate unmeasured states of the systems. The supervised control is appended to assure that the hybrid adaptive CMAC controller achieve a stable closed-loop system through Lyapunov stability theory. Finally, simulation results show that the effect of the approximation error on the tracking error can be attenuated efficiently.

This paper addresses the SMC-based adaptive wavelet-neural tracking control problem for a class of uncertain nonlinear systems. The adaptive wavelet-neural controller is designed under constrain that not all atate variables are available for measurement. A state observer is used to estimate unmeasured states of the systems. The global asymptotic stability of the closed-loop system is guaranteed according to the Lyapunov stability criterion. The simulation presented in the inverted pendulum system control indicates that the proposed approach is capable of achieving a good trajectory following performance without the knowledge of plant parameters.

A stable direct adaptive CMAC PI controller for a class of uncertain nonlinear systems is investigated under the constrain that only the system output is available for measurement. First, a state observer is used to estimate unmeasured states of the systems. Then, the PI control structure is used for improving robustness in the closed-loop system and avoiding affection of uncertainties and external disturbances. The global asymptotic stability of the closed-loop system is guaranteed according to the Lyapunov stability criterion. To demonstrate the effectiveness of the proposed method, simulation results indicate that the proposed approach is capable of achieving a good trajectory following performance without the knowledge of plant parameters.