This article develops a reinforcement learning (RL) strategy to address the robust optimal tracking control problem for an intelligent surface vehicle (ISV) with modeling uncertainties and unknown ocean disturbances. A neural network (NN) identifier is constructed to learn uncertain nonlinear dynamics. Unlike the typical architecture of actor-critic networks, a single-critic network is employed to obtain the approximate solution of Hamilton-Jacobi-Bellman (HJB) equation. By introducing an additional stability term and experience replay (ER) technique, we present a novel critic weight update rule, such that (i) the traditional persistent excitation (PE) condition is relaxed, and (ii) the request of an initial admissible control is alleviated. Subsequently, a performance-guaranteed adaptive optimal tracking control algorithm is developed to guarantee the prescribed transient behavior of tracking errors and minimize the cost function simultaneously. A rigorous theoretical analysis indicates that the developed controller guarantees semi-globally uniformly ultimate boundedness of the closed-loop adaptive system with prescribed performance. Simulation studies demonstrate the effectiveness and robustness of the presented control algorithm.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Performance-Guaranteed Adaptive Optimized Control of Intelligent Surface Vehicle Using Reinforcement Learning


    Beteiligte:
    Dong, Chao (Autor:in) / Chen, Lin (Autor:in) / Dai, Shi-Lu (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2024-02-01


    Format / Umfang :

    1895255 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Adaptive Routing with Guaranteed Delay Bounds using Safe Reinforcement Learning

    Nayak Seetanadi, Gautham / Maggio, Martina / Årzén, Karl-Erik | BASE | 2020

    Freier Zugriff


    Optimal Q-laws via reinforcement learning with guaranteed stability

    Holt, Harry / Armellin, Roberto / Baresi, Nicola et al. | Elsevier | 2021


    Guaranteed Globally Optimal continuous Reinforcement Learning (AIAA 2014-0010)

    Bijl, H. / Van Kampen, E.-J. / Chu, Q.P. et al. | British Library Conference Proceedings | 2014


    Adaptive Control of a Rigid Body Vehicle on Exponential Coordinates with Guaranteed Performance

    Arabi, Ehsan / Sarsilmaz, Selahattin Burak / Yucelen, Tansel et al. | AIAA | 2018