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Adaptive Trajectory Tracking Algorithm for the Aerospace Vehicle Based on Improved T-MPSP

Author

Listed:
  • Chao Ou

    (School of Aerospace Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
    Aerospace Technology Institute of China Aerodynamics Research and Development Center, Mianyang 621000, China)

  • Chengjun Shan

    (School of Aerospace Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Zhongtao Cheng

    (School of Aerospace Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Yaosong Long

    (School of Aerospace Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

Abstract

To deal with the uncertainty and disturbance that exist in the tracking system of an aerospace vehicle, an adaptive trajectory-tracking method based on a novel tracking model predictive static programming (T-MPSP) is proposed. Firstly, to make the proposed method more adaptive to uncertain parameter deviations, an extended Kalman filter (EKF) parameter correction strategy is designed. Then, the control constraints are considered to form a novel T-MPSP algorithm. By combining the parameter correction strategy with the improved T-MPSP algorithm, a novel adaptive tracking guidance scheme is presented. Finally, simulations are carried out to demonstrate the effectiveness of the proposed method.

Suggested Citation

  • Chao Ou & Chengjun Shan & Zhongtao Cheng & Yaosong Long, 2023. "Adaptive Trajectory Tracking Algorithm for the Aerospace Vehicle Based on Improved T-MPSP," Mathematics, MDPI, vol. 11(9), pages 1-16, May.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:9:p:2160-:d:1139344
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    References listed on IDEAS

    as
    1. Xuechao Qiu & Changchun Hua & Jiannan Chen & Liuliu Zhang & Xinping Guan, 2020. "Model-free adaptive iterative sliding mode control for a robotic exoskeleton trajectory tracking system," International Journal of Systems Science, Taylor & Francis Journals, vol. 51(10), pages 1782-1797, July.
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    Cited by:

    1. Yajun Zeng & Jun Wang & Shaoming Wei & Chi Zhang & Xuan Zhou & Yingbin Lin, 2024. "Gaussian Mixture Probability Hypothesis Density Filter for Heterogeneous Multi-Sensor Registration," Mathematics, MDPI, vol. 12(6), pages 1-32, March.

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