Constrained adaptive optimal control using a reinforcement learning agent

被引：15

作者：

Lin, Wei-Song ^{[1
,2
]}

Zheng, Chen-Hong ^{[2
]}

机构：

[1] NTUEE, Taipei 106, Taiwan

[2] Natl Taiwan Univ, Dept Elect Engn, Taipei, Taiwan

来源：

AUTOMATICA | 2012年 / 48卷 / 10期

关键词：

Adaptive optimal control; Reinforcement learning; Constrained optimization; Approximate dynamic programming;

D O I：

10.1016/j.automatica.2012.06.064

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

To synthesize the optimal control strategies of nonlinear systems on infinite horizon while subject to mixed equality and inequality constraints has been a challenge to control engineers. This paper regards it as a problem of finite-time optimization in infinite-horizon control then devises a reinforcement learning agent, termed as the Adaptive Optimal Control (AOC) agent, to carry out the finite-time optimization procedures. Adaptive optimal control is in the sense of activating the finite-time optimization procedure whenever needed to improve the control strategy or adapt to a real-world environment. The Nonlinear Quadratic Regulator (NQR) is shown a typical example that the AOC agent can find out. The optimality conditions and adaptation rules for the AOC agent are deduced from Pontryagin's minimum principle. The requirements for convergence and stability of the AOC system are shown. (C) 2012 Elsevier Ltd. All rights reserved.

引用

页码：2614 / 2619

页数：6

共 50 条

[1] Resilient adaptive optimal control of distributed multi-agent systems using reinforcement learning
Moghadam, Rohollah
Modares, Hamidreza
[J]. IET CONTROL THEORY AND APPLICATIONS, 2018, 12 (16): : 2165 - 2174
[2] Adaptive Control of a Mechatronic System Using Constrained Residual Reinforcement Learning
Staessens, Tom
Lefebvre, Tom
Crevecoeur, Guillaume
[J]. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2022, 69 (10) : 10447 - 10456
[3] Optimal control in microgrid using multi-agent reinforcement learning
Li, Fu-Dong
Wu, Min
He, Yong
Chen, Xin
[J]. ISA TRANSACTIONS, 2012, 51 (06) : 743 - 751
[4] Optimal adaptive control of drug dosing using integral reinforcement learning
Padmanabhan, Regina
Meskin, Nader
Haddad, Wassim M.
[J]. MATHEMATICAL BIOSCIENCES, 2019, 309 : 131 - 142
[5] Adaptive optimal control of stencil printing process using reinforcement learning
Khader, Nourma
Yoon, Sang Won
[J]. ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING, 2021, 71
[6] Reinforcement Learning and Adaptive Optimal Control of Congestion Pricing
Nguyen, Tri
Gao, Weinan
Zhong, Xiangnan
Agarwal, Shaurya
[J]. IFAC PAPERSONLINE, 2021, 54 (02): : 221 - 226
[7] Robust Optimal Well Control using an Adaptive Multigrid Reinforcement Learning Framework
Dixit, Atish
Elsheikh, Ahmed H.
[J]. MATHEMATICAL GEOSCIENCES, 2023, 55 (03) : 345 - 375
[8] Robust Optimal Well Control using an Adaptive Multigrid Reinforcement Learning Framework
Atish Dixit
Ahmed H. Elsheikh
[J]. Mathematical Geosciences, 2023, 55 : 345 - 375
[9] Constrained Reinforcement Learning for Stochastic Dynamic Optimal Power Flow Control
Wu, Tong
Scaglione, Anna
[J]. 2023 IEEE POWER & ENERGY SOCIETY GENERAL MEETING, PESGM, 2023,
[10] Reinforcement learning and optimal adaptive control: An overview and implementation examples
Khan, Said G.
Herrmann, Guido
Lewis, Frank L.
Pipe, Tony
Melhuish, Chris
[J]. ANNUAL REVIEWS IN CONTROL, 2012, 36 (01) : 42 - 59

← 1 2 3 4 5 →