Visible to the public Self-Triggered Tracking Control of Underactuated Surface Vessels with Stochastic Noise

TitleSelf-Triggered Tracking Control of Underactuated Surface Vessels with Stochastic Noise
Publication TypeConference Paper
Year of Publication2021
AuthorsDeng, Yingjie, Zhao, Dingxuan, Liu, Tao
Conference Name2021 International Conference on Security, Pattern Analysis, and Cybernetics(SPAC)
Date PublishedJune 2021
PublisherIEEE
ISBN Number978-1-6654-4322-7
KeywordsAdaptation models, adaptive filtering, control design, Jumps of virtual control laws, Metrics, Noise measurement, Numerical models, pubcrawl, resilience, Resiliency, Scalability, Self-triggered control (STC), Sensors, Stability analysis, Stochastic noise, Stochastic processes, Underactuated surface vessels (USVs)
AbstractThis note studies self-triggered tracking control of underactuated surface vessels considering both unknown model dynamics and stochastic noise, where the measured states in the sensors are intermittently transmitted to the controller decided by the triggering condition. While the multi-layer neural network (NN) serves to approximate the unknown model dynamics, a self-triggered adaptive neural model is fabricated to direct the design of control laws. This setup successfully solves the ``jumps of virtual control laws'' problem, which occurs when combining the event-triggered control (ETC) with the backstepping method, seeing [1]–[4]. Moreover, the adaptive model can act as the filter of states, such that the complicated analysis and control design to eliminate the detrimental influence of stochastic noise is no longer needed. Released from the continuous monitoring of the controller, the devised triggering condition is located in the sensors and designed to meet the requirement of stability. All the estimation errors and the tracking errors are proved to be exponentially mean-square (EMS) bounded. Finally, a numerical experiment is conducted to corroborate the proposed strategy.
URLhttps://ieeexplore.ieee.org/document/9539942
DOI10.1109/SPAC53836.2021.9539942
Citation Keydeng_self-triggered_2021