Self‐learning‐based optimal tracking control of an unmanned surface vehicle with pose and velocity constraints

In this article, subject to both pose and velocity constraints within narrow waters, a self‐learning‐based optimal tracking control (SLOTC) scheme is innovatively created for an unmanned surface vehicle (USV) by deploying actor‐critic reinforcement learning (RL) mechanism and backstepping technique....

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Veröffentlicht in:	International journal of robust and nonlinear control 2022-03, Vol.32 (5), p.2950-2968
Hauptverfasser:	Wang, Ning, Gao, Ying, Liu, Yongjin, Li, Kun
Format:	Artikel
Sprache:	eng
Schlagworte:	Adaptive control Constraints Learning Liapunov functions Neural networks pose and velocity constraints reinforcement learning control self‐learning‐based optimal control Surface vehicles Tracking control Tracking errors trajectory tracking unmanned surface vehicle Unmanned vehicles
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