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

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.

First, taking the claw-type underwater salvage robot as the research object, amore universal thruster fault model was established to describe the fault state of equipment failure, interruption, stuck, and poor contact. Second, the controller was designed by the proportional logarithmic projection analytical method. The system input signal was 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 was used to compensate for the external disturbance of the system, and the adaptive neural network was used to fit the dynamic uncertainty of the system. The system input was introduced into the event-triggered mechanism to reduce the output regulation frequency of the fault thruster.

Finally, the simulation results showed that the method adopted in this study reduced 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 realized accurate pose tracking under external disturbance and system dynamic uncertain disturbance.

It has been proven that the algorithm used in this research can still reasonably allocate power to reduce the load of a fault thruster and complete the tracking task under fault conditions.