سال انتشار:

2020

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عنوان انگلیسی مقاله:

Reinforcement learning control for underactuated surface vessel with output error constraints and uncertainties

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ترجمه فارسی عنوان مقاله:

کنترل یادگیری تقویتی برای سطوح کم بهره با محدودیت ها و عدم قطعیت خطای خروجی

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منبع:

Sciencedirect - Elsevier - Neurocomputing, 399 (2020) 479-490: doi:10:1016/j:neucom:2020:03:021

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نویسنده:

Zewei Zheng a , Linping Ruan b , Ming Zhu c , Xiao Guo d , ∗

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چکیده انگلیسی:

This study investigates the trajectory tracking control problem of an underactuated marine vessel in the presence of output constraints, model uncertainties and environmental disturbances. The error transfor- mation technique can ensure that the tracking errors remain within the predefined constraint boundaries. The controller is designed in combination with the critic function and the reinforcement learning (RL) al- gorithm based on actor-critic neural networks. The RL method is applied to solve model uncertainties and disturbances, and the critic function modifies the control action to supervise the system performance. Based on Lyapunov’s direct method, a stability analysis is proposed to prove that the boundedness of system signals and the desired tracking performance can be guaranteed. Finally, the simulation illustrates the effectiveness and feasibility of the proposed controller.
Keywords: Reinforcement learning | Actor-Critic (AC) | Output constraints | Underactuated marine vessel | Trajectory tracking | Neural networks

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قیمت: رایگان

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توضیحات اضافی:

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