In this chapter, the data-based optimal tracking controlOptimal tracking controlTrajectory tracking approach is developed by involving the iterative dual heuristic dynamic programmingDual heuristic dynamic programming (DHP) algorithm for nonlinear systems. According to the iterative DHP method, the updating formula of the costate function and the new optimal controlOptimal control policy for unknown nonlinear systems are provided to solve the optimal tracking controlOptimal tracking control problem. Moreover, three neural networks are used to facilitate the implementation of the proposed algorithm. The unknown nonlinear system dynamics is first identified by establishing a model neural network. To improve the identification precision, biases are introduced to the model network. The model networkModel network with biases is trained by the gradient descent algorithm, where the weights and biases across all layers are updated. The uniform ultimate boundedness stability with a proper learning rateLearning rate is analyzed, by using the Lyapunov approach. The effectiveness of the proposed method is demonstrated through a simulation example.


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    Titel :

    Data-Driven Optimal Trajectory Tracking via a Novel Self-Learning Approach


    Weitere Titelangaben:

    Intelligent Control & Learning Systems


    Beteiligte:
    Wang, Ding (Autor:in) / Ha, Mingming (Autor:in) / Zhao, Mingming (Autor:in)


    Erscheinungsdatum :

    2023-01-22


    Format / Umfang :

    25 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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