Data-Driven Robust Regulation of Nonlinear Systems With Mismatched Disturbances

This paper proposes a data-driven robust regulation method of input-affine nonlinear systems with mismatched disturbances. First, the relationship between the robust control problem and the optimal control problem is built, which indicates that the robust control of original systems can be the solution of the optimal control problem of the auxiliary system. Then, within the framework of adaptive dynamic programming, we present a data-driven algorithm to solve the optimal control problem. To implement the data-driven algorithm, we use two kinds of neural networks (NNs): actor NNs are used to approximate the sub-control policies of the augmented control and a critic NN is applied to estimate the value function. To learn the unknown parameters of actor and critic network weight vectors, the Monte Carlo integration method is employed. Finally, we provide a third-order benchmark model of the armature-controlled DC motor to illustrate the applicability of the developed control strategy.