Robust adaptive integral terminal sliding mode control for steer-by-wire systems based on extreme learning machine

Abstract

In this paper, a novel extreme-learning-machine (ELM)-based robust adaptive integral terminal sliding mode (AITSM) control strategy is developed for the precise tracking control of a steer-by-wire (SBW) system with uncertain dynamics. The proposed control not only ensures the finite-time error convergence but also effectively estimates the lumped uncertainty via a single-hidden layer feedforward network (SLFN) with ELM. Different from conventional ELM using least square optimization approach, the ELM in this work is designed to adaptively estimate the lumped uncertainty from the perspective of global stability of the closed-loop system. The stability of the closed-loop control system is proved in Lyapunov sense. Simulations are carried out to demonstrate the superior control performance of the proposed control.