Noncooperative Game-Based Cooperative Maneuvering of Intelligent Surface Vehicles via Accelerated Learning-Based Neural Predictors

This paper investigates a noncooperative game-based cooperative maneuvering problem of multiple intelligent surface vehicles (ISVs) subject to uncertain nonlinearities and external disturbances. There exists a conflict between the overall objective of the swarm and own parameterized individual tasks of each ISV, resulting in a noncooperative game. A distributed cooperative maneuvering control method is proposed for multiple ISVs. Specifically, at first, action estimators are designed for each ISV based on the information of neighboring ISVs to estimate the actions of other ISVs. Then, an accelerated learning-based neural predictor is designed, where an echo state network is introduced as the identifier of uncertain nonlinearities, and adaptation laws are developed based on a high-order tuner to accelerate the convergence rate. Finally, controllers and update laws of individual variables are designed for multiple ISVs based on a distributed Nash equilibrium seeking approach, where the time derivative of the kinematic control law is obtained based on a nonlinear command filter. The control architecture is modular, leading to a decoupling of an action estimator module, a neural predictor module, and a controller module. Using the cascade system theory, the input-to-state stability of the resulting closed-loop system is guaranteed based on the input-to-state stability of three subsystems. A simulation example is provided to validate the effectiveness of the proposed noncooperative game-based distributed cooperative maneuvering control method.