This study presents an incorporating sliding-mode neural-network (SMNN) and fuzzy control system for the position control of an induction motor. In the SMNN control system, a neural network controller is developed to mimic an equivalent control law in the sliding mode c
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This study presents an incorporating sliding-mode neural-network (SMNN) and fuzzy control system for the position control of an induction motor. In the SMNN control system, a neural network controller is developed to mimic an equivalent control law in the sliding mode control, and a robust controller is designed to curb the system dynamics on the sliding surface for guaranteeing the asymptotic stability property. Moreover, an adaptive bound estimation algorithm is employed to estimate the upper bound of uncertainties. All adaptive learning algorithms in the SMNN control system are derived from the sense of Lyapunov stability analysis, so that system-tracking stability can be guaranteed in the closed-loop system whether the uncertainties occur or not. In spite of these merits, SMNN suffers from chattering problem which can excite unmodeled dynamics and harm the control system. In this paper, to avoid this problem, a combined controller in clued SMNN term and Fuzzy term is proposed. The proposed control scheme possesses three salient merits: (1) it guarantees the stability of the controlled system, (2) no constrained conditions and prior knowledge of the controlled plant is required in the design process, and (3) the chattering is avoided.
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