A sliding motion can be divided into two phases: reaching phase and sliding phase. One of the features of sliding mode control is that it is robust to parameter uncertainties and external disturbances in the sliding phase. But in the reaching phase, SMC may be sensitive to parameter uncertainty and external disturbance. The moving sliding surface proposed by Choi et al can minimize or eliminate the reaching phase. In this article, the sliding mode fuzzy controller design method with a moving sliding surface is presented. The simulation results show the superiority of SMFC over classical SMC and PID controller in the presence of external disturbances. تفاصيل المقالة

In sliding mode control, the sliding movement can be divided into two phases: reaching phase and sliding phase. In each phase, we face a series of problems. In the sliding phase, switching leads to the occurrence of undesirable chattering phenomenon, so that such high frequency oscillations stimulate the unmodeled dynamics of the system and may cause damage to the controlled device. In this paper, a fuzzy-sliding model controller (FSMC) is presented to solve this problem. On the other hand, during the reaching phase, SMC is sensitive to parameter uncertainty and external disturbance. In the continuation of the paper, a sliding mode fuzzy controller (SMFC) with a moving sliding surface to minimize or even eliminate the reaching phase is introduced. تفاصيل المقالة

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. تفاصيل المقالة