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• W3131678030 abstract "An unmanned surface vehicle (USV) under complicated marine environments can hardly be modeled well such that model-based optimal control approaches become infeasible. In this article, a self-learning-based model-free solution only using input–output signals of the USV is innovatively provided. To this end, a data-driven performance-prescribed reinforcement learning control (DPRLC) scheme is created to pursue control optimality and prescribed tracking accuracy simultaneously. By devising state transformation with prescribed performance, constrained tracking errors are substantially converted into constraint-free stabilization of tracking errors with unknown dynamics. Reinforcement learning paradigm using neural network-based actor–critic learning framework is further deployed to directly optimize controller synthesis deduced from the Bellman error formulation such that transformed tracking errors evolve a data-driven optimal controller. Theoretical analysis eventually ensures that the entire DPRLC scheme can guarantee prescribed tracking accuracy, subject to optimal cost. Both simulations and virtual-reality experiments demonstrate the remarkable effectiveness and superiority of the proposed DPRLC scheme." @default.
• W3131678030 created "2021-03-01" @default.
• W3131678030 creator A5040965135 @default.
• W3131678030 creator A5052897084 @default.
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• W3131678030 date "2021-12-01" @default.
• W3131678030 modified "2023-10-17" @default.
• W3131678030 title "Data-Driven Performance-Prescribed Reinforcement Learning Control of an Unmanned Surface Vehicle" @default.
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• W3131678030 doi "https://doi.org/10.1109/tnnls.2021.3056444" @default.
• W3131678030 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33606641" @default.
• W3131678030 hasPublicationYear "2021" @default.
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