Neural H₂ Control Using Continuous-Time Reinforcement Learning.

Abstract

In this article, we discuss continuous-time H2 control for the unknown nonlinear system. We use differential neural networks to model the system, then apply the H2 tracking control based on the neural model. Since the neural H2 control is very sensitive to the neural modeling error, we use reinforcement learning to improve the control performance. The stabilities of the neural modeling and the H2 tracking control are proven. The convergence of the approach is also given. The proposed method is validated with two benchmark control problems.