Event-triggered neural network control of autonomous surface vehicles over wireless network  

作　　者：Mingao LV Dan WANG Zhouhua PENG Lu LIU Haoliang WANG 

机构地区：[1]School of Marine Electrical Engineering,Dalian Maritime University,Dalian 116026,China [2]School of Marine Engineering,Dalian Maritime University,Dalian 116026,China

出　　处：《Science China(Information Sciences)》2020年第5期57-70,共14页中国科学（信息科学）（英文版）

基　　金：National Natural Science Foundation of China(Grant Nos.61673081,51979020,51909021,51579023);Training Program for High-level Technical Talent in Transportation Industry(Grant No.2018-030);Innovative Talents in Universities of Liaoning Province(Grant No.LR2017014);Science and Technology Fund for Distinguished Young Scholars of Dalian(Grant No.2018RJ08);Stable Supporting Fund of Science and Technology on Underwater Vehicle Technology(Grant No.JCKYS2019604SXJQR-01);Fundamental Research Funds for the Central Universities(Grant No.3132019319);China Postdoctoral Science Foundation(Grant No.2019M650086)。

摘　　要：In this paper, an event-triggered neural network control method is proposed for autonomous surface vehicles subject to uncertainties and input constraints over wireless network. An event-triggered mechanism with three logic rules is employed to determine the wireless data transmission of states and control inputs. An event-driven neural network is applied to approximate the uncertainties using aperiodic sampled states. In addition, a predictor is employed to update the weights of neural network. An event-based bounded kinetic control law is applied to address the actuator constraints. The advantage of the proposed event-triggered neural network control approach is that the network traffic can be reduced while guaranteeing system stability and speed following performance. The closed-loop control system is proved to be input-tostate stable via cascade theory. The Zeno behavior can be avoided via the proposed event-triggered neural network control approach. A simulation example is provided to demonstrate the effectiveness of the proposed event-triggered neural network control approach for autonomous surface vehicles.

关 键 词：event-triggered control aperiodic sampling AUTONOMOUS surface VEHICLES NEURAL NETWORK ACTUATOR CONSTRAINT 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] U664.82[自动化与计算机技术—控制科学与工程]