Distributed neural predictor enhanced coordinated control of AUVs

Abstract

This article investigates an enhanced tunnel prescribed performance coordinated control problem of multiple autonomous underwater vehicles (AUVs) under initial constraints. To meet high performance requirements in complex underwater conditions, AUV control faces challenges. In order to address these, an enhanced tunnel prescribed performance (ETPP) method is proposed, which is composed of composite error scaling function (CESF) and tunnel prescribed performance (TPP). In particular, a CESF-based error transformation is performed to scale the tracking error within the TPP limits. In the guidance loop, an ETPP-based guidance law is devised to guarantee the transient and steady-state behavior of the tracking error. In the control loop, based on the distributed learning strategy with weighted average, a quantized input-based distributed neural predictor (QDNP) is proposed to estimate the unknown external disturbances. Using the antidisturbance technique, a QDNP-based quantized control law is designed to stabilize multi-AUV formations. The uniformly ultimately bounded (UUB) stability of the overall closed-loop system is established in the Lyapunov sense. Finally, simulation examples with four AUVs are provided to demonstrate the effectiveness of the proposed distributed tunnel performance-guaranteed coordinated control method.