Physics‐informed reinforcement learning for optimal control of nonlinear systems
Yujia Wang,
Zhe Wu
Abstract:This article proposes a model‐free framework to solve the optimal control problem with an infinite‐horizon performance function for nonlinear systems with input constraints. Specifically, two Physics‐Informed Neural Networks (PINNs) that incorporate the knowledge of the Lyapunov stability theorem and the convergence conditions of the policy iteration algorithm are utilized to approximate the value function and control policy, respectively. Then, a Reinforcement Learning (RL) algorithm that does not require any… Show more
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