Cooperative Learning-Based Formation Control of Autonomous Marine Surface Vessels With Prescribed Performance

This article addresses the cooperative learning formation control problem for multiple homogeneous marine surface vessels (MSVs) subject to external time-varying disturbances and modeling uncertainties under the prescribed performance constraint. The modeling uncertainties, including hydrodynamic damping terms and unmodeled dynamics are identified/learned by the localized radial basis function neural networks (NNs) in a cooperative way. Disturbance observers are incorporated into the formation control design to compensate for the external time-varying disturbances. A novel cooperative learning formation controller is proposed, which is shown to be capable not only of fulfilling the predefined formation pattern with guaranteed prescribed performance but also of identifying/learning the associated uncertain dynamics based on the cooperative deterministic learning theory. Moreover, the learned knowledge on identified uncertain dynamics is stored in NN models with converged constant NN weights. Based on the stored knowledge, an experience-based formation controller is developed, which can improve the control performance including reduction of the computational burden, while guaranteeing prescribed performance of formation tracking errors. Simulation results demonstrate the effectiveness of the proposed formation control protocol.