Version 1
: Received: 29 February 2024 / Approved: 29 February 2024 / Online: 29 February 2024 (13:21:56 CET)
How to cite:
Xie, F.; Liang, G.; Chien, Y. Graph-Guided Adaptive Formation Control with Prescribed Performance Enhanced by Neural Network-Based Backstepping. Preprints2024, 2024021744. https://doi.org/10.20944/preprints202402.1744.v1
Xie, F.; Liang, G.; Chien, Y. Graph-Guided Adaptive Formation Control with Prescribed Performance Enhanced by Neural Network-Based Backstepping. Preprints 2024, 2024021744. https://doi.org/10.20944/preprints202402.1744.v1
Xie, F.; Liang, G.; Chien, Y. Graph-Guided Adaptive Formation Control with Prescribed Performance Enhanced by Neural Network-Based Backstepping. Preprints2024, 2024021744. https://doi.org/10.20944/preprints202402.1744.v1
APA Style
Xie, F., Liang, G., & Chien, Y. (2024). Graph-Guided Adaptive Formation Control with Prescribed Performance Enhanced by Neural Network-Based Backstepping. Preprints. https://doi.org/10.20944/preprints202402.1744.v1
Chicago/Turabian Style
Xie, F., Guozhen Liang and Ying-Ren Chien. 2024 "Graph-Guided Adaptive Formation Control with Prescribed Performance Enhanced by Neural Network-Based Backstepping" Preprints. https://doi.org/10.20944/preprints202402.1744.v1
Abstract
This research addresses the formidable challenge of achieving precise trajectory tracking for autonomous vehicle formations in uncertain environments. It introduces an adaptive control system tailored for autonomous vehicles, with a primary focus on ensuring prescribed performance in trajectory tracking within a leader-follower formation paradigm. The system incorporates a guidance law that allows the leader vehicle to dynamically adjust desired yaw angles and speeds for follower vehicles based on the established reference trajectory, enhancing tracking accuracy and responsiveness to environmental changes. To address practical external disturbances, the study utilizes Radial Basis Function Neural Networks (RBFNN) in conjunction with second-order filters for error approximation. This approach is further strengthened by a carefully formulated adaptive law. The innovative integration of a barrier Lyapunov function with the backstepping method significantly enhances the system’s adaptability and robustness, ensuring trajectory tracking performance meets predetermined standards. Simulation results illustrate the control system’s adept handling of various external disturbances, consistently maintaining trajectory tracking errors within predefined limits. This underscores the system’s potential to markedly enhance the operational reliability and efficiency of autonomous vehicle formations in unpredictable environmental conditions.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
