Event-triggered compound learning tracking control of autonomous surface vessels in the measurement network. (15th May 2021)

• Record Type: Journal Article
• Authors: Deng, Yingjie; Zhang, Xianku; Zhao, Baigang; Zhao, Hongbiao
• Is Part Of: Ocean engineering. Volume 228(2021)
• Publication Date: 2021-05-15
• Subjects: Model-based event-triggered control (MBETC) -- Compound learning -- Autonomous surface vessels; Ocean engineering -- Periodicals; Ocean engineering; Periodicals; 620.4162
• DOI: 10.1016/j.oceaneng.2021.108817
• Languages: English
• ISSNs: 0029-8018

Abstract

Abstract: This paper investigates the tracking control of the autonomous surface vessels (ASVs) with the time-varying disturbances. While the echo state network (ESN) accounts for the unknown dynamics in the model, a model-based event-triggered control (MBETC) scheme is presented by using the compound learning technique, which combines the learning of the ESN and the estimation of the compound disturbance. Different from the existing compound learning, the proposed scheme updates the estimates of the ESN weights and the compound disturbances in an event-triggered manner, in which two novel prediction errors are involved in their update laws. The values of prediction errors are obtained by using the online-recorded data during the inter-event time. To solve the recently proposed problem of "jumps of virtual control laws" arising in the backstepping-based event-triggered control (ETC), an event-triggered adaptive model is established to generate the continuous estimates of the states and direct the control laws. By the merit of ETC, the proposed scheme can importantly reduce the communication traffic in the measurement network compared with its continuous substitutes. By the merit of online-recorded-data-based compound learning, the proposed scheme can achieve the good understanding of synthetic uncertainties. All the errors in the closed-loop system are proved to be semi-globally uniformly ultimately bounded (SGUUB). Finally, a numerical example corroborates the proposed scheme. Highlights: This paper develops an event-triggered compound learning technique. This paper develops a uniform ETC scheme in the measurement network for the ASVs. The problem of "jumps of virtual control laws" is solved by using the adaptive model.