Policy Gradient Adaptive Critic Designs for Model-Free Optimal Tracking Control With Experience Replay

A model-free optimal tracking controller is designed for discrete-time nonlinear systems through policy gradient adaptive critic designs (PGACDs) with experience replay (ER). By using system transformation, optimal tracking control problems are converted into optimal regulation problems. An off-policy PGACD algorithm is developed to minimize the iterative Q-function and improve the tracking control performance. The proposed method is realized based on the critic network and the actor network (AN), which are applied to approximate the iterative Q-function and the iterative control policy, respectively. Then, the policy gradient technique is introduced to derive a novel weight updating law of the AN explicitly by using measured system data only. The convergence of the iteration is established through theoretical analysis, and the uniform ultimate boundedness is demonstrated for the closed-loop system under the PGACD-based controller by using Lyapunov's direct method. To guarantee the stability and increase the data usage efficiency of the learning process, an ER-based learning framework is designed to improve the realizability of the proposed method. Finally, simulation results of two examples are provided to demonstrate the performance of the off-policy PGACD algorithm.

Automated summary

A model-free optimal tracking controller for discrete-time nonlinear systems is designed using policy gradient adaptive critic designs and experience replay, aiming to improve control performance.