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Robust Trajectory Tracking Control of an Autonomous Tractor-Trailer Considering Model Parameter Uncertainties and Disturbances

Author

Listed:
  • En Lu

    (Key Laboratory for Theory and Technology of Intelligent Agricultural Machinery and Equipment, Jiangsu University, No. 301 Xuefu Road, Zhenjiang 212013, China
    School of Agricultural Engineering, Jiangsu University, No. 301 Xuefu Road, Zhenjiang 212013, China)

  • Jialin Xue

    (Key Laboratory for Theory and Technology of Intelligent Agricultural Machinery and Equipment, Jiangsu University, No. 301 Xuefu Road, Zhenjiang 212013, China
    School of Agricultural Engineering, Jiangsu University, No. 301 Xuefu Road, Zhenjiang 212013, China)

  • Tiaotiao Chen

    (Key Laboratory for Theory and Technology of Intelligent Agricultural Machinery and Equipment, Jiangsu University, No. 301 Xuefu Road, Zhenjiang 212013, China
    School of Agricultural Engineering, Jiangsu University, No. 301 Xuefu Road, Zhenjiang 212013, China)

  • Song Jiang

    (School of Mechanical Engineering, Jiangsu University, No. 301 Xuefu Road, Zhenjiang 212013, China)

Abstract

This paper discusses the robust trajectory tracking control of an autonomous tractor-trailer in agricultural applications. Firstly, considering the model parameter uncertainties and various disturbances, the kinematic and dynamic models of the autonomous tractor-trailer system are established. Moreover, the coordinate transformation is adopted to convert the trajectory tracking error into a new unconstrained error state space model. On this basis, the prescribed performance control (PPC) technique is designed to ensure the convergence speed and final tracking control accuracy of the tractor-trailer control system. Then, this paper designs a double closed-loop control structure. The posture control level adopts the model predictive control (MPC) method, and the dynamic level adopts the sliding mode control (SMC) method. At the same time, it is worth mentioning that the nonlinear disturbance observer (NDO) is designed to estimate all kinds of system disturbances and compensate for the tracking control system to improve the system’s robustness. Finally, the proposed control strategy is validated through comparative simulations, demonstrating its effectiveness in achieving robust trajectory tracking of the autonomous tractor-trailer system.

Suggested Citation

  • En Lu & Jialin Xue & Tiaotiao Chen & Song Jiang, 2023. "Robust Trajectory Tracking Control of an Autonomous Tractor-Trailer Considering Model Parameter Uncertainties and Disturbances," Agriculture, MDPI, vol. 13(4), pages 1-17, April.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:4:p:869-:d:1123932
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    Citations

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    Cited by:

    1. Phummarin Thavitchasri & Dechrit Maneetham & Padma Nyoman Crisnapati, 2024. "Intelligent Surface Recognition for Autonomous Tractors Using Ensemble Learning with BNO055 IMU Sensor Data," Agriculture, MDPI, vol. 14(9), pages 1-21, September.
    2. David Marcos-Andrade & Francisco Beltran-Carbajal & Ivan Rivas-Cambero & Hugo Yañez-Badillo & Antonio Favela-Contreras & Julio C. Rosas-Caro, 2024. "Sliding Mode Speed Control in Synchronous Motors for Agriculture Machinery: A Chattering Suppression Approach," Agriculture, MDPI, vol. 14(5), pages 1-25, May.
    3. Hamed Etezadi & Sulaymon Eshkabilov, 2024. "A Comprehensive Overview of Control Algorithms, Sensors, Actuators, and Communication Tools of Autonomous All-Terrain Vehicles in Agriculture," Agriculture, MDPI, vol. 14(2), pages 1-42, January.

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