强化学习
李雅普诺夫函数
控制理论(社会学)
贝尔曼方程
控制Lyapunov函数
理论(学习稳定性)
功能(生物学)
过程(计算)
非线性系统
计算机科学
人工神经网络
最优控制
数学优化
Lyapunov重新设计
控制(管理)
数学
人工智能
机器学习
物理
量子力学
进化生物学
生物
操作系统
摘要
Abstract This article develops a safe reinforcement learning (SRL) algorithm for optimal control of nonlinear systems with input constraints. First, we design a novel performance index function by taking advantage of control Lyapunov‐barrier functions (CLBF) with inherent safety and stability properties to ensure closed‐loop stability and safety during operation under the optimal control policy. Additionally, since it is challenging to represent the CLBF‐based value function as an explicit function of process states, neural networks (NNs) are used to approximate the value function using the process operational data that indicate safe and unsafe operations. Theoretical results on the stability, safety, and optimality of the SRL algorithm are developed, accounting for the approximation error of the NN‐based value function. Finally, the efficacy of the proposed safe optimal control scheme is shown using an application to a chemical process example.
科研通智能强力驱动
Strongly Powered by AbleSci AI