Data-Driven Tracking Controls of Multi-input Augmented Systems Based on ADP Algorithm

Abstract

The data-driven optimal tracking controls (OTC) for the unknown multi-input system are proposed in this paper, and a novel tuning law is used to update NN weights in the learning scheme. First, the formula of the OTC for the multi-input NZS game is presented. A three-layer neural network (NN) data-driven model is introduced to approximate the unknown system, and the input dynamics are obtained. Then, to solve the OTC as a regulation optimal problem, an augmentation multi-input system is constructed with the tracking error and command trajectory. Moreover, we use a reinforcement learning based data-driven NN method to online learn the optimal value functions for each input, which is directly used to calculate the optimal tracking control associated with each performance index function. The convergence of the NN weights is proved. Finally, a simulation is presented to verify the feasibility of our algorithm in this paper.