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Disturbance observer-based integral sliding-mode control design for leader-following consensus of multi-agent systems and its application to car-following model

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  • Arockia Samy, Stephen
  • Anbalagan, Pratap

Abstract

In this manuscript, the memory-based integral sliding-mode control (MBISMC) that depends on a disturbance observer (DOB) is designed for the multi-agent systems (MASs) with mismatched disturbance. To do this, DOB is provided to estimate disturbances, which are included in the controller design procedure. Besides that, an integral-type surface function (ISF) is intended to model the properties of the MASs and DOB. The designed ISF contains not only a state-dependent input matrix but also memory information. To date, the stability properties of the MAS with external disturbances, parameter uncertainties, and MBISMC have not been studied in the literature, which is the primary impetus for this research. Further, an H∞-theory and Lyapunov–Krasovskii functional (LKF) is applied to derive sufficient stability conditions that ensure the global asymptotic consensus of the presented model. Meanwhile, the mismatched disturbances of considered MASs are attenuated effectively by the MBISMC method. In conclusion, three numerical examples are offered to demonstrate the practicality and usefulness of the proposed design techniques.

Suggested Citation

  • Arockia Samy, Stephen & Anbalagan, Pratap, 2023. "Disturbance observer-based integral sliding-mode control design for leader-following consensus of multi-agent systems and its application to car-following model," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    • Handle: RePEc:eee:chsofr:v:174:y:2023:i:c:s0960077923006343
    DOI: 10.1016/j.chaos.2023.113733
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    References listed on IDEAS

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    1. Zhang, Xuefeng & Chen, Shunan & Zhang, Jin-Xi, 2022. "Adaptive sliding mode consensus control based on neural network for singular fractional order multi-agent systems," Applied Mathematics and Computation, Elsevier, vol. 434(C).
    2. Puangmalai, Jirapong & Tongkum, Jakkrapong & Rojsiraphisal, Thaned, 2020. "Finite-time stability criteria of linear system with non-differentiable time-varying delay via new integral inequality," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 171(C), pages 170-186.
    3. Narayanan, G. & Syed Ali, M. & Karthikeyan, Rajagopal & Rajchakit, Grienggrai & Jirawattanapanit, Anuwat, 2022. "Novel adaptive strategies for synchronization control mechanism in nonlinear dynamic fuzzy modeling of fractional-order genetic regulatory networks," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
    4. Shi, Sangli & Wang, Zhengxin & Song, Qiang & Xiao, Min & Jiang, Guo-Ping, 2022. "Leader-following quasi-bipartite synchronization of coupled heterogeneous harmonic oscillators via event-triggered control," Applied Mathematics and Computation, Elsevier, vol. 427(C).
    5. Liu, Mingyu & Xie, Jing & Kao, Yonggui, 2023. "Stochastic bounded consensus for multi-agent systems with fractional Brownian motions via sliding mode control," Applied Mathematics and Computation, Elsevier, vol. 446(C).
    6. Saravanakumar, Ramasamy & Datta, Rupak & Cao, Yang, 2022. "New insights on fuzzy sampled-data stabilization of delayed nonlinear systems," Chaos, Solitons & Fractals, Elsevier, vol. 154(C).
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    1. Meng, Hao & Pang, Denghao & Cao, Jinde & Guo, Yechen & Niazi, Azmat Ullah Khan, 2024. "Optimal bipartite consensus control for heterogeneous unknown multi-agent systems via reinforcement learning," Applied Mathematics and Computation, Elsevier, vol. 476(C).

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