Neural Adaptive Quantitative Prescribed Performance Sectionalized Event-Triggered Control for Autonomous Underwater Vehicles

In this paper, we study the quantitative design paradigm (QDP) of tracing control for a class of second-order system and further extend this to solve the trajectory tracking problem of autonomous underwater vehicles. The key merit of QDP is the capability of assigning some quantitative indices (e.g., overshoot and convergence time). To pursue performance enhancement with regard to tracking performance and bandwidth saving, a sectionalized event-triggered mechanism (SETM) incorporated with prespecified convergence time is proposed. To recognize the peculiarities of the lumped disturbances, an estimation-triggered neural network (ETNN) is designed via a property indicator, such that the disturbances that deteriorate the performance of the closed-loop system will be compensated and beneficial disturbances will be reserved otherwise, enabling less energy consumption without sacrificing the tracking performance. Theoretical analysis and simulation results are provided and analyzed, validating the performance and efficiency of the proposed solution.