Communication-Aware Formation Control of AUVs With Model Uncertainty and Fading Channel via Integral Reinforcement Learning

• Published in: IEEE/CAA journal of automatica sinica Vol. 10; no. 1; pp. 159 - 176
• Main Authors: Cao, Wenqiang, Yan, Jing, Yang, Xian, Luo, Xiaoyuan, Guan, Xinping
• Format: Journal Article
• Language: English
• Published: Piscataway Chinese Association of Automation (CAA) 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical Engineering,Yanshan University,Qinhuangdao 066004,China%Institute of Information Science and Engineering,Yanshan University,Qinhuangdao 066004,China%Department of Automation,Shanghai Jiao Tong University,Shanghai 200240,China
• Subjects: Autonomous underwater vehicles; Autonomous underwater vehicles (AUVs); Channel estimation; Collocation methods; Communication; communication-aware; Controllers; Design optimization; Fading; Fading channels; formation; Fractional factorial design; Machine learning; Mathematical models; Parameters; Probabilistic logic; Reinforcement learning; Signal to noise ratio; Technological innovation; Uncertainty; commu-nication-aware; formation; uncertainty; Autonomous underwater vehicles (AUVs); reinforcement learning
• DOI: 10.1109/JAS.2023.123021
• ISSN: 2329-9266

Most formation approaches of autonomous underwater vehicles (AUVs) focus on the control techniques, ignoring the influence of underwater channel. This paper is concerned with a communication-aware formation issue for AUVs, subject to model uncertainty and fading channel. An integral reinforcement learning (IRL) based estimator is designed to calculate the probabilistic channel parameters, wherein the multivariate probabilistic collocation method with orthogonal fractional factorial design (M-PCM-OFFD) is employed to evaluate the uncertain channel measurements. With the estimated signal-to-noise ratio (SNR), we employ the IRL and M-PCM-OFFD to develop a saturated formation controller for AUVs, dealing with uncertain dynamics and current parameters. For the proposed formation approach, an integrated optimization solution is presented to make a balance between formation stability and communication efficiency. Main innovations lie in three aspects: 1) Construct an integrated communication and control optimization framework; 2) Design an IRL-based channel prediction estimator; 3) Develop an IRL-based formation controller with M-PCM-OFFD. Finally, simulation results show that the formation approach can avoid local optimum estimation, improve the channel efficiency, and relax the dependence of AUV model parameters.