Adaptive fuzzy neural networks cooperative formation control for unmanned surface vehicles with constraints using...

This paper investigates the adaptive fuzzy neural networks cooperative formation control problem of multiple unmanned surface vehicles with actuator faults and unknown velocity information. Firstly, a new fuzzy neural networks-based prescribed time extended state observer is designed, which can not only estimate unknown velocity from the position information of unmanned surface vehicles, but also estimate ocean disturbances and uncertainties in the system model. Considering the actual requirements of formation operation, the formation error and velocity are constrained within the prescribed safety boundary. Secondly, an adaptive cooperative formation control method is designed by defining distributed cooperative formation error and introducing log-type barrier Lyapunov function to prevent formation error and velocity from violating prescribed constraints. In addition, the designed fuzzy neural networks adaptive law and multiplicative fault adaptive law can compensate for model uncertainties and time-varying multiplicative actuator faults. This method can ensure that multiple unmanned surface vehicles form and maintain the desired formation within a prescribed time, and the formation error converges to a smaller neighborhood of the origin within the prescribed time. Finally, the effectiveness of the proposed control method is demonstrated through multiple simulation analyses.