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Barrier-function based adaptive trajectory tracking control for high-order nonlinear systems with collision avoidance

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  • Zhang, Lili
  • Liu, Chenglong
  • An, Liwei

Abstract

This paper considers the problem of trajectory tracking and collision avoidance for a class of high-order nonlinear strict feedback systems with unknown nonlinearities. The main issue is how to ensure collision avoidance and tracking performance simultaneously in the presence of unknown nonlinear functions. To address the issue, an integral-multiplicative barrier Lyapunov function (BLF) is integrated into the backstepping procedure to remove the dynamic mismatching issue of the existing SUM-type BLF. It has been proven that the proposed adaptive approach ensures both collision avoidance and tracking performance of high-order nonlinear systems in multi-obstacle environments, and all the signals in the closed-loop system are uniformly ultimately bounded (UUB). Simulation results confirm the effectiveness of the proposed method.

Suggested Citation

  • Zhang, Lili & Liu, Chenglong & An, Liwei, 2025. "Barrier-function based adaptive trajectory tracking control for high-order nonlinear systems with collision avoidance," Applied Mathematics and Computation, Elsevier, vol. 484(C).
    • Handle: RePEc:eee:apmaco:v:484:y:2025:i:c:s009630032400465x
    DOI: 10.1016/j.amc.2024.129004
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    References listed on IDEAS

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    1. Zhou, Xingyu & Tian, Yang & Wang, Haoping, 2022. "Neural network state observer-based robust adaptive fault-tolerant quantized iterative learning control for the rigid-flexible coupled robotic systems with unknown time delays," Applied Mathematics and Computation, Elsevier, vol. 430(C).
    2. Liu, Shanlin & Niu, Ben & Karimi, Hamid Reza & Zhao, Xudong, 2024. "Self-triggered fixed-time bipartite fault-tolerant consensus for nonlinear multiagent systems with function constraints on states," Chaos, Solitons & Fractals, Elsevier, vol. 178(C).
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