AUTHOR=Guo Gaoyang , Zhang Qiang , Zhang Yan , Tan Wenyi , Tao Zewen , Ma Sainan 

TITLE=Adaptive neural network projection analytical fault-tolerant control of underwater salvage robot with event trigger

JOURNAL=Frontiers in Neurorobotics

VOLUME=Volume 16 - 2022

YEAR=2023

URL=https://www.frontiersin.org/journals/neurorobotics/articles/10.3389/fnbot.2022.1082251

DOI=10.3389/fnbot.2022.1082251

ISSN=1662-5218

ABSTRACT=In order to solve the problem of control failure caused by system failure of deep-water salvage equipment under severe sea conditions, an event-triggered fault-tolerant control method (PEFC) based on proportional logarithmic projection analysis is proposed innovatively. Firstly, taking the claw-type underwater salvage robot as the research object, a more universal thruster fault model is established to describe the fault state of equipment failure, interruption, stuck and poor contact. Secondly, the controller is designed by the proportional logarithmic projection analytical method. The system input signal is amplified and projected as a virtual input, which replaces the original input to isolate and learn the fault factor online by the analytical algorithm. The terminal sliding mode observer is used to compensate the external disturbance of the system, and the adaptive neural network is used to fit the dynamic uncertainty of the system. The system input is introduced into the event-triggered mechanism to reduce the output regulation frequency of the fault thruster. Finally, the simulation results show that the method adopted in this paper reduces the power output by 28.95% and the update frequency of power output by 75% compared with the traditional adaptive overdrive fault-tolerant control (AOFC) method and realizes accurate pose tracking under external disturbance and system dynamic uncertain disturbance. It is proved that the algorithm in this paper can still reasonably allocate power to reduce the load of fault thruster and complete the tracking task under fault conditions.