Fuzzy Optimal Event-Triggered Control for Dynamic Positioning of Unmanned Surface Vehicle

Abstract

In this article, a fuzzy optimal event-triggered dynamic positioning control approach with a Q-learning value iteration (VI) algorithm is developed for unmanned surface vehicles (USVs) systems. The USV systems are first modeled by Takagi-Sugeno (T-S) fuzzy systems. To reduce the communication resources and controller update times, an event-triggered mechanism is designed via employing the sampled augmented systems states and triggered control input signals. Based on the developed event-triggered mechanism and Bellman optimality theory, a fuzzy optimal event-triggered control (ETC) approach is presented. Since solution of optimal control policy reduces to algebraic Riccati equations (AREs), its analytical solution is difficult to solve directly. Then, to search its approximation solution, a VI algorithm is formulated. By rigorous proof, the proposed optimal ETC scheme can assure that the USVs systems are asymptotically stable and the Q-learning algorithm is convergent. Finally, the simulation and comparisons results with previous optimal controllers verify the feasibility of the presented optimal ETC scheme.