سال انتشار:

2020

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عنوان انگلیسی مقاله:

Reinforcement Learning-Based Control for Nonlinear Discrete-Time Systems with Unknown Control Directions and Control Constraints

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ترجمه فارسی عنوان مقاله:

کنترل مبتنی بر یادگیری تقویتی برای سیستم های غیر خطی زمان گسسته با جهت های کنترل ناشناخته و محدودیت های کنترل

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منبع:

Sciencedirect - Elsevier - Neurocomputing, 402 (2020) 50-65. doi:10.1016/j.neucom.2020.03.061

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نویسنده:

Miao Huang a , ∗, Cong Liu b , Xiaoqi He c , Longhua Ma d , Zheming Lu e , Hongye Su e

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چکیده انگلیسی:

In this work, output-feedback control problems for a class of discrete-time non-affine nonlinear systems with unknown control directions and input constraints are considered by using reinforcement learning (RL) method. Two neural networks (NNs) implement the control: 1) a critic NN that estimates a non- quadratic strategic utility function (SUF) and 2) an action NN that generates optimized control input and minimizes the SUF. The implicit function theorem is applied to obtain the optimal control law since the control is appeared in a non-affine form. For the first time, the discrete Nussbaum gain is introduced to overcome the difficulty that the control directions are unknown and a non-quadratic SUF is used to deal with the control constraints in the RL-based control. The theoretical derivation of the uniformly ultimately boundedness of the NN weights and the closed-loop output tracking error is given. And two numerical examples have been supplied to valid the proposed method.
Keywords: Neural networks | Reinforcement learning | Non-affine nonlinear systems | Output feedback | Unknown control directions

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قیمت: رایگان

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توضیحات اضافی:

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