Adaptive fuzzy predictive sliding control of uncertain nonlinear systems with bound-known input delay. (November 2015) - Khazaee, Mostafa; Markazi, Amir H.D.; Omidi, Ehsan
Abstract: In this paper, a new Adaptive Fuzzy Predictive Sliding Mode Control (AFP-SMC) is presented for nonlinear systems with uncertain dynamics and unknown input delay. The control unit consists of a fuzzy inference system to approximate the ideal linearization control, together with a switching strategy to compensate for the estimation errors. Also, an adaptive fuzzy predictor is used to estimate the future values of the system states to compensate for the time delay. The adaptation laws are used to tune the controller and predictor parameters, which guarantee the stability based on a Lyapunov-Krasovskii functional. To evaluate the method effectiveness, the simulation and experiment on an overhead crane system are presented. According to the obtained results, AFP-SMC can effectively control the uncertain nonlinear systems, subject to input delays of known bound. Abstract : Highlights: Stabilizing controller is proposed for uncertain nonlinear systems with bound-known input delay. Control method combines an adaptive fuzzy predictor with adaptive fuzzy sliding mode controller. Uniform ultimate stability of the closed-loop system is guaranteed. There is no need to know the nonlinear functions of system and the uncertainty bound.