Robust adaptive formation control of underactuated autonomous surface vessels based on MLP and DOB

This paper investigates the leader–follower formation problem of multiple underactuated autonomous surface vessels in the presence of model uncertainties and environmental disturbances. Specially, the formation is defined in the body-fixed coordinates of the leader vessel and velocities of the leader are unavailable to followers. A novel robust adaptive formation control scheme based on the minimal learning parameter (MLP) algorithm and the disturbance observer (DOB) is presented. To address related formation configurations and unknown velocities of the leader, adaptive programming of the virtual vessel is introduced. By the neural networks (NNs) technique, the DOB is constructed and the formation controller is developed with different MLP-based adaptive laws. Under the proposed controller, it is shown that the desired formation can be achieved only with the position and yaw angle of the leader. And formation errors are guaranteed to be semiglobal uniformly ultimately bounded. Compared with existing results, the NNs-based DOB can compensate disturbances effectively with less model information. Meanwhile, the formation controller and the DOB can share the same set of NNs with smaller computational effort, where only two parameters need to be learned online for each of them. Simulations and comparison results are provided to illustrate the effectiveness of theoretical results.