Boundary Optimal Control for Parabolic Distributed Parameter Systems With Value Iteration
Jingyi Sun, Biao Luo, Xiaodong Xu, Chunhua Yang
Abstract
A reinforcement learning-based boundary optimal control algorithm for parabolic distributed parameter systems is developed in this article. First, a spatial Riccati-like equation and an integral optimal controller are derived in infinite-time horizon based on the principle of the variational method, which avoids the complex semigroups and operator theories. Using state data along the system trajectory, a value iteration algorithm via the Bellman optimality principle is proposed to obtain the solution of the spatial Riccati-like equation and the optimal control law. The convergence of the value iteration algorithm is proved. Subsequently, an approximation scheme based on weighted residuals is developed to implement the value iteration algorithm, where radial basis functions are chosen as the basic functions to approximate the solution of the spatial Riccati-like equation. Simulations on the diffusion-reaction process demonstrate the effectiveness of the developed method.