Neural H₂ Control Using Continuous-Time Reinforcement Learning
Adolfo Perrusquía, Wen Yu · IEEE Transactions on Cybernetics · 2020
In this article, we discuss continuous-time$\mathcal {H}_{2}$control for the unknown nonlinear system. We use differential neural networks to model the system, then apply the$\mathcal {H}_{2}$tracking control based on the neural model. Since the neural$\mathcal {H}_{2}$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$\mathcal {H}_{2}$tracking control are proven. The convergence of the approach is also given. The proposed method is validated with two benchmark control problems.