Adaptive Interval Type-2 Fuzzy Neural Network Sliding Mode Control of Nonlinear Systems Using Improved Extended State Observer

Abstract

An adaptive sliding mode control (ASMC) based on improved linear extended state observer (LESO) is proposed for nonlinear systems with unknown and uncertain dynamics. An improved LESO is designed to estimate total disturbance of the uncertain nonlinear system, and an interval type-2 fuzzy neural network (IT2FNN) is used to optimize and approximate the observe bandwidth of LESO, and the adaptive parameter tuning is realized based on the gradient descent (GD) method. Based on the total disturbance estimated by LESO, an ASMC strategy is designed to ensure the system stability. By adapting the sliding mode gain, the observation performance of LESO compared to the total disturbance can be better utilized, and system chattering is reduced. Finally, some simulation results are given which show that the proposed control strategy has a good control effect, strong practicability, and wide versatility.