Robust adaptive integral terminal sliding mode control for steer-by-wire systems based on extreme learning machine. (September 2020)

• Record Type: Journal Article
• Authors: Ye, Mao; Wang, Hai
• Is Part Of: Computers & electrical engineering. Volume 86(2020)
• Publication Date: 2020-09
• Subjects: ELM -- Integral terminal sliding mode control -- Steer-by-wire -- Compensator; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2020.106756
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlight: First, the integral terminal sliding surface is used to both eliminate the reaching phase in the sliding mode and ensure a finite-time state error convergence. Meanwhile, two important sliding mode parameters are adaptively adjusted in Lyapunov sense, which eases the complex parameter selection process. The lumped uncertainty is estimated and compensated by the extreme learning machine (ELM) and thus the switching control component is only needed to handle the effects of the uncertainty estimation error rather than the uncertainty itself. This technique greatly reduces the control chattering and simplifies the selection of the switching gain for practical applications. The ELM used for the estimation of the lumped uncertainty is regarded as an essential component of the closed-loop control and does not require any training process. Not only can the input weights be randomly chosen as the conventional ELM for pattern classifications, but also the output weights are adaptively adjusted in the sense of Lyapunov to ensure the global stability of the closed-loop control. This is one of the most significant superiorities of the proposed ELM compared to the traditional ELM approaches. 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. Graphical abstract: Image, graphical abstract