Neural H₂ Control Using Continuous-Time Reinforcement Learning

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.