H∞ Control of Constrained-Input Nonlinear Systems with Unknown Model Based on Adaptive Dynamic Programming

An Adaptive dynamic programming(ADP) algorithm that contain online measurement and off-policy learning two phase is proposed to solve the H-infinity control problem of continuous-time nonlinear system with constrained -input and unknown model only based on online data. The model -free Hamiton-Jacobi-Isaacs(HJI) equation is derived by the policy iteration(PI) and the model-free iteration reinforcement learning(IRL) method. Three neural networks(NN) are structured, after collecting online data of system is finished, then off-policy learning method is used to approximate solve the model-free HJI equation. And the value function, control strategy and disturbance strategy are obtained by the NN. The weights of the neural network are solved by the least square method. The simulation results verify the feasibility of the algorithm.