1. Dalian Maritime University, 1 Linghai Road , Dalian Liaoning China 116026
2. Dalian Maritime University, #1Linghai Road, , Dalian Liaoning China 116026
3. Ling shui Road, No.1 , Dalian China 116026
4. 1 Linghai Road , Dalian China 116026
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-to-state 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.
This work was supported in part by the National Key Research and Development Program of China under Grant 2016YFC0301500, and in part by the National Natural Science Foundation of China under Grants 61673081, 51979020, 51909021,51579023, and in part by the Training Program for High-level Technical Talent in Transportation Industry under Grant 2018-030, and in part by the Innovative Talents in Universities of Liaoning Province under Grant LR2017014, and in part by High Level Talent Innovation and Entrepreneurship Program of Dalian under Grant 2016RQ036, and in part by Science and Technology Fund for Distinguished Young Scholars of Dalian under Grant 2018RJ08, and in part by the Stable Supporting Fund of Science and Technology on Underwater Vehicle Technology JCKYS2019604SXJQR-01, and in part by the Fundamental Research Funds for the Central Universities under Grant 3132019319 and in part by China Postdoctoral Science Foundation 2019M650086.
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