Accelerated Intelligent Critic Tracking Predictive Control With Data Experience Replay for Unknown Nonlinear Systems

In this article, the accelerated intelligent critic tracking predictive control with data experience replay (AICTPC-DER) framework is constructed to address the trajectory tracking problem of the nonlinear systems with unknown dynamics. The receding optimization mechanism of model predictive control and the intelligent critic scheme are deeply integrated to realize real-time optimization of online policies. First, the time-series data of the unknown system is collected to establish a deep neural network as the prediction model. Afterward, in order to improve the efficiency of solving optimization problems online, the accelerated critic architecture with experience replay via collecting tracking error data is established based on the conventional adaptive critic designs. Simultaneously, the theoretical properties of the AICTPC-DER algorithm are comprehensively analyzed. Finally, a large number of simulation results verify the effectiveness and progressiveness of the AICTPC-DER algorithm in solving tracking problems, among which the advantages of the accelerated factor and the DEP mechanism are apparent. From the comparative experiments, it can be seen that the developed algorithm exhibits superior control performance.