Abstract

This document explains the uncertain-nonlinear-system'control problem. I present a robust adaptive sliding mode control, combing the Backstepping algorithm with sliding mode control. Design sliding mode control recursive rules, and then get the virtual control value. By the time, CMAC neural network learns system' uncertainty as well as the derivative information of each order virtual control value Real-time. The conventional sliding mode control easily leads to chattering as the result of using sign function as switching function. So, we use hyperbolic tangent function instead of sign function as sliding mode switching function, to avoid the possible chattering problem owing to discontinuous terms. Finally, the proposed control algorithm is verified by simulation, within the mathematical model of electric hydraulic servo system.

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