Reinforcement learning for optimal control of stochastic nonlinear systems

IF 3.5 3区 工程技术 Q2 ENGINEERING, CHEMICAL
AIChE Journal Pub Date : 2025-03-28 DOI:10.1002/aic.18840
Xinji Zhu, Yujia Wang, Zhe Wu
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引用次数: 0

Abstract

A reinforcement learning (RL) approach is developed in this work for nonlinear systems under stochastic uncertainty. A stochastic control Lyapunov function (SCLF) candidate is first constructed using neural networks (NNs) as an approximator to the value function, and then a control policy designed using this SCLF is developed to ensure the stability in probability of the stochastic nonlinear system. An RL algorithm is proposed for stochastic nonlinear systems to iteratively update the value function and control policy, driving them toward optimal values. We further extend its feasibility under the sample-and-hold implementation of control actions, and demonstrate its application to two chemical reactor examples to show its practical advantages and efficiency.
随机非线性系统最优控制的强化学习
本文提出了一种针对随机不确定性非线性系统的强化学习方法。首先利用神经网络作为值函数的逼近器构造随机控制Lyapunov函数(SCLF)候选函数,然后利用该候选函数设计控制策略以保证随机非线性系统的概率稳定性。针对随机非线性系统,提出了一种迭代更新值函数和控制策略的强化学习算法,使其趋向最优值。进一步扩展了该方法在取样保持控制作用下的可行性,并对两个化工反应器实例进行了应用验证,证明了该方法的实用性和有效性。
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来源期刊
AIChE Journal
AIChE Journal 工程技术-工程:化工
CiteScore
7.10
自引率
10.80%
发文量
411
审稿时长
3.6 months
期刊介绍: The AIChE Journal is the premier research monthly in chemical engineering and related fields. This peer-reviewed and broad-based journal reports on the most important and latest technological advances in core areas of chemical engineering as well as in other relevant engineering disciplines. To keep abreast with the progressive outlook of the profession, the Journal has been expanding the scope of its editorial contents to include such fast developing areas as biotechnology, electrochemical engineering, and environmental engineering. The AIChE Journal is indeed the global communications vehicle for the world-renowned researchers to exchange top-notch research findings with one another. Subscribing to the AIChE Journal is like having immediate access to nine topical journals in the field. Articles are categorized according to the following topical areas: Biomolecular Engineering, Bioengineering, Biochemicals, Biofuels, and Food Inorganic Materials: Synthesis and Processing Particle Technology and Fluidization Process Systems Engineering Reaction Engineering, Kinetics and Catalysis Separations: Materials, Devices and Processes Soft Materials: Synthesis, Processing and Products Thermodynamics and Molecular-Scale Phenomena Transport Phenomena and Fluid Mechanics.
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