Adaptive Composite Fixed-Time RL-Optimized Control for Nonlinear Systems and Its Application to Intelligent Ship Autopilot

Abstract

In the article, an adaptive fixed-time reinforcement learning (RL) optimized control policy is given for nonlinear systems. Radial basis function neural networks (RBFNNs) are exploited to fit uncertain nonlinearities appeared in the considered systems and RL is applied under the critic-actor architecture by using RBFNNs. Specifically, a novel fixed-time smooth estimation system is proposed to improve the estimating performance of RBFNNs. The introduction of the hyperbolic tangent function effectively avoids the singularity problem of the derivative of the virtual controller. The stability analysis shows that the tracking error inclines to an adjustable region near the origin in a fixed-time interval and the boundedness of all signals is obtained. Finally, the intelligent ship autopilot is simulated to prove the utilizability of the obtained control way.