Nonlinear Model Predictive Guidance (NMPG) based on optimal control (trajectory optimization) is an extremely promising method for achieving optimal and precise guidance. Event-triggered NMPG offers a dynamic cycle strategy where updates to the optimal guidance law are made only when the deviation between the actual state and the optimal reference trajectory exceeds a set threshold, thus conserving computational resources effectively. To further minimize event-triggering occurrences, we propose embedding an optimal parameter estimation step into it. With increasing sampled flight-state data over time, the motion model for NMPG progressively converges towards the actual one, thereby yielding more precise open-loop guidance laws. The simulation results of an ascent guidance problem show that the proposed method has higher guidance accuracy, a shorter simulation time, and fewer guidance law update times than traditional iterative guidance (IG) and general NMPGs.


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    Title :

    Optimal Parameter Estimation-Assisted Event-Triggered Model Predictive Guidance for Launch Vehicle


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Yan, Liang (editor) / Duan, Haibin (editor) / Deng, Yimin (editor) / Zhang, Tengfei (author) / Su, Hua (author) / Liu, Songyu (author) / Gong, Chunlin (author)

    Conference:

    International Conference on Guidance, Navigation and Control ; 2024 ; Changsha, China August 09, 2024 - August 11, 2024



    Publication date :

    2025-03-02


    Size :

    12 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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