Adaptive neural synergetic heading control for USVs with unknown dynamics and disturbances

This paper delves into the challenge of heading control for unmanned surface vehicles (USVs) in conditions with unknown dynamics and environmental disturbances. Adaptive neural synergetic controllers are introduced for the purpose of effectively steering the USV, ensuring precise heading control to track and maintain the desired angle, even in the presence of environmental disturbances such as sea winds and waves. Firstly, different forms of macro variables and evaluation constraints are introduced to design the synergetic controllers. Then, to estimate the unknown dynamics and environmental disturbances, a single input single output RBF neural network is developed. Besides, to ensure tracking at the desired angle, an adaptive neural synergetic law is derived from the Lyapunov stability theorem. Finally, the simulation results compare the control effect of heading controllers with different macro variables and evaluation constraints and verify the effectiveness of the proposed control strategy. The feasibility of the adaptive neural synergetic controller is verified through the USV experiment.