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Robust Leader–Follower Formation Control Using Neural Adaptive Prescribed Performance Strategies

Author

Listed:
  • Fengxi Xie

    (Department of Electrical Engineering and Computer Science, Technische Universität Berlin, 10623 Berlin, Germany
    These authors contributed equally to this work.)

  • Guozhen Liang

    (Department of Electrical Engineering and Computer Science, Technische Universität Berlin, 10623 Berlin, Germany
    These authors contributed equally to this work.)

  • Ying-Ren Chien

    (Department of Electrical Engineering, National Ilan University, Yilan 260007, Taiwan)

Abstract

This paper introduces a novel leader–follower formation control strategy for autonomous vehicles, aimed at achieving precise trajectory tracking in uncertain environments. The approach is based on a graph guidance law that calculates the desired yaw angles and velocities for follower vehicles using the leader’s reference trajectory, improving system stability and predictability. A key innovation is the development of a Neural Adaptive Prescribed Performance Controller (NA-PPC), which incorporates a Radial Basis Function Neural Network (RBFNN) to approximate nonlinear system dynamics and enhances disturbance estimation accuracy. The proposed method enables high-precision trajectory tracking and formation maintenance under random disturbances, which are vital for autonomous vehicle logistics and detection technologies. Leveraging a graph-based guidance law reduces control complexity and improves robustness against external disturbances. The inclusion of second-order filters and adaptive RBFNNs further enhances nonlinear error handling, improving control performance, stability, and accuracy. The integration of guidance laws, leader–follower control strategies, backstepping techniques, and RBFNNs creates a robust formation control system capable of maintaining performance under dynamic conditions. Comprehensive computer simulations validate the effectiveness of this controller, highlighting its potential to advance autonomous vehicle formation control.

Suggested Citation

  • Fengxi Xie & Guozhen Liang & Ying-Ren Chien, 2024. "Robust Leader–Follower Formation Control Using Neural Adaptive Prescribed Performance Strategies," Mathematics, MDPI, vol. 12(20), pages 1-21, October.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:20:p:3259-:d:1500998
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    References listed on IDEAS

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    1. Wang, Fang & Gao, Yali & Zhou, Chao & Zong, Qun, 2022. "Disturbance observer-based backstepping formation control of multiple quadrotors with asymmetric output error constraints," Applied Mathematics and Computation, Elsevier, vol. 415(C).
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