Title: Path planning and tracking control of an autonomous vehicle using extended state observer-based adaptive recursive terminal sliding mode

Authors: Zhe Sun; Shengrui Li; Shuwang Du; He Ren; Bo Chen; Jinchuan Zheng; Zhihong Man

Addresses: College of Information Engineering, Zhejiang University of Technology, Hangzhou, China ' College of Information Engineering, Zhejiang University of Technology, Hangzhou, China ' Zhijiang College of Zhejiang University of Technology, Shaoxing, China ' College of Information Engineering, Zhejiang University of Technology, Hangzhou, China ' College of Information Engineering, Zhejiang University of Technology, Hangzhou, China ' School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, Australia ' School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, Australia

Abstract: Obstacle avoidance is of great significance to autonomous vehicles. In this paper, an obstacle-avoidance path planning and tracking control method is developed for an autonomous vehicle. First, a path-planning algorithm based on artificial potential field is designed for the autonomous vehicle to bypass obstacles, and the generated discrete path model is transformed into a continuous one for the path-tracking control design. Then, according to the lateral dynamics and path-tracking characteristics of the vehicle, a kinematic-and-dynamic model is established for the autonomous vehicle. Afterward, an extended state observer-based adaptive recursive terminal sliding mode control scheme is proposed such that the vehicle can track the planned path precisely. The stability of the control system is proved according to the Lyapunov stability theory. Finally, MATLAB/Simulink-Carsim co-simulations are implemented to demonstrate the performance of the presented method under the conditions of different vehicle velocities and road surfaces. The simulation results indicate that the designed path-planning algorithm can plan an obstacle-avoidance path effectively, and the proposed control scheme possesses higher control precision and stronger robustness than the benchmark controllers.

Keywords: autonomous vehicle; path planning and tracking; artificial potential field; adaptive recursive terminal sliding mode; RTSM; extended state observer; ESO.

DOI: 10.1504/IJAMECHS.2023.132519

International Journal of Advanced Mechatronic Systems, 2023 Vol.10 No.3, pp.132 - 145

Received: 15 Feb 2023
Accepted: 08 May 2023
Published online: 25 Jul 2023 *

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