Neural network adaptive critic control with disturbance rejection

摘要

A neural-network-based adaptive critic control method is established for continuous-time input-affine uncertain nonlinear systems to achieve disturbance rejection. The present problem can be formulated as a two-player zero-sum differential game and the adaptive critic mechanism is employed to solve the minimax optimization problem. A neural network identifier is developed to reconstruct the unknown dynamical information. The optimal control law and the worst-case disturbance law are designed by introducing and training a critic neural network. The effectiveness of the present self-learning control method is also illustrated by a simulation experiment.