Distributed Event-Driven Optimal Control for Networked Unmanned Surface Vehicles With Long Transmission Delays: A Memory-Based Estimation and Communication Assignment Approach
Jixiang Li, Bin Zhou, Bing Huang, Weiquan Huang
Abstract
The Internet of Things (IoT), comprising networked unmanned surface vehicles (NUSVs), enables significant advancements in distributed optimal coordination techniques for marine activities. However, constrained communication bandwidth and long transmission delays pose substantial challenges to achieving optimal operations of NUSVs. To address these challenges, this paper proposes a delay-tolerant distributed optimation method (DTDOM) based on a time assignment event-driven communication mechanism (TAEDCM). In this approach, the memory-based state estimator (MSE) is developed to provide reliable data support for implementing the TAEDCM-based DTDOM. Specifically, the MSE within DTDOM estimates neighbors’ states using historical data pairs during packet gaps, while the one within TAEDCM predicts possible neighbor estimates from locally recorded data. Additionally, a communication assignment approach (CAA) integrated into TAEDCM is proposed to allocate interaction activation slots for each vehicle, preventing channel competition caused by simultaneous data exchanges. Compared with existing methods, the proposed framework offers advantages in actively compensating the delay-induced lag errors, determining appropriate communication timings under long delays, and ensuring competition-free inter-vehicle interactions. Theoretical analysis and semi-physical simulations validate the effectiveness and superiority of proposed solution.