Novel SMC for Discrete Interval Type-2 Fuzzy Semi-Markovian Switching Models With Incomplete Semi-Markovian Kernel

This work studies the novel sliding mode control (SMC) of discrete nonlinear stochastic switching models under semi-Markovian parameter and incomplete semi-Markovian kernel (SMK). The characteristic of nonlinear system is described by an interval type-2 fuzzy (IT2F) model that can be recognized as a collection of several type-1 fuzzy models. The uncertainties in system parameters is efficiently captured using the lower and upper grades of membership. Based on the mode-dependent Lyapunov function and incomplete SMK, sufficient conditions are proposed to ensure the stability of sliding dynamics. Moreover, an IT2F SMC law based on learning strategy is developed such that the state signals are guided onto the predetermined sliding region and the mode switchings-induced chattering is effectively reduced. Finally, the IT2F SMC strategy is validated through the simulation of truck-trailer model.