Neural network algorithm for stabilizing control of robustness in systems

In order to solve a class of online optimal control problems of linear discrete-time systems with uncertain parameters and to ensure perfect dynamic static behavior, a neural network approach is proposed for solving optimal stabilizing controller with robustness. From a lemma which can guarantee global asymptotic stability of the system, the form of optimal stabilizing controller with robustness is established by quadratic programming. A neural network is proposed for computing the parameters of this controller. It has global convergence property and does not use the data for training, so it is convenient for implementation with electron circuitry and satisfies the requirement for real-time control. The advanced performance of the proposed network is demonstrated through simulation.