Anti-Saturation-Based Adaptive Sliding-Mode Control for Active Suspension Systems With Time-Varying Vertical Displacement and Speed Constraints

Hao Chen, Yan Jun Liu, Lei Liu, Shaocheng Tong, Zhiwei Gao

    Research output: Contribution to journalArticlepeer-review

    52 Citations (Scopus)

    Abstract

    In this article, an adaptive sliding-mode control scheme is developed for a class of uncertain quarter vehicle active suspension systems with time-varying vertical displacement and speed constraints, in which the input saturation is considered. The integral terminal SMC is adopted to improve convergence accuracy and avoid singular problems. In addition, neural networks are used to model unknown terms in the system and the backstepping technique is taken into account to design the actual controller. To guarantee that the time-varying state constraints are not violated, the corresponding Barrier Lyapunov functions are constructed. At the same time, a continuous differentiable asymmetric saturation model is developed to improve the stability of the system. Then, the Lyapunov stability theory is used to verify that all signals of the resulting system are semi globally uniformly ultimately bounded, time-varying state constraints are not violated, and error variables can converge to the small neighborhood of 0. Finally, results of the simulation of the designed control strategy are given to further prove the effectiveness.

    Original languageEnglish
    Pages (from-to)6244-6254
    Number of pages11
    JournalIEEE Transactions on Cybernetics
    Volume52
    Issue number7
    Early online date21 Jan 2021
    DOIs
    Publication statusPublished - Jul 2022

    Keywords

    • Active suspension systems
    • adaptive control (AC)
    • input saturation
    • neural networks
    • state constraints

    Fingerprint

    Dive into the research topics of 'Anti-Saturation-Based Adaptive Sliding-Mode Control for Active Suspension Systems With Time-Varying Vertical Displacement and Speed Constraints'. Together they form a unique fingerprint.

    Cite this