This paper proposes a model predictive control (MPC) approach for ducted fan aerial robots using physics-informed machine learning (ML), where the task is to fully exploit the capabilities of the predictive control design with an accurate dynamic model by means of a hybrid modeling technique. For this purpose, an indigenously developed ducted fan miniature aerial vehicle with adequate flying capabilities is used. The physics-informed dynamical model is derived offline by considering the forces and moments acting on the platform. On the basis of the physics-informed model, a data-driven ML approach called adaptive sparse identification of nonlinear dynamics is utilized for model identification, estimation, and correction online. Thereafter, an MPC-based optimization problem is computed by updating the physics-informed states with the physics-informed ML model at each step, yielding an effective control performance. Closed-loop stability and recursive feasibility are ensured under sufficient conditions. Finally, a simulation study is conducted to concisely corroborate the efficacy of the presented framework.


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    Title :

    Model Predictive Control Technique for Ducted Fan Aerial Vehicles Using Physics-Informed Machine Learning


    Contributors:
    Tayyab Manzoor (author) / Hailong Pei (author) / Zhongqi Sun (author) / Zihuan Cheng (author)


    Publication date :

    2022




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




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