Discrete-Time H₂ Neural Control Using Reinforcement Learning

In this article, we discuss H₂ control for unknown nonlinear systems in discrete time. A discrete-time recurrent neural network is used to model the nonlinear system, and then, the H₂ tracking control is applied based on the neural model. Since this neural H₂ control is very sensitive to the neural modeling error, we use reinforcement learning and another neural approximator to improve tracking accuracy and robustness of the controller. The stabilities of the neural identifier and the H₂ tracking control are proven. The convergence of the approach is also given. The proposed method is validated with the control of the pan and tilt robot and the surge tank.