High-gain-observer based adaptive output-feedback formation control for underactuated unmanned surface vessels with input saturation and uncertainties

Abstract

An adaptive output feedback formation control strategy based on high gain observer has been developed to solve the problem of underactuated surface vessels formation control with uncertain dynamics, ocean environment disturbance and input saturation. In this strategy, a high gain observer that only depends on position information is used to estimate the unmeasurable velocity, and in order to solve the ‘complex explosion problem’ in the conventional backstepping control algorithm, a first-order low-pass filter is adopted to obtain the derivative of the virtual control signal. In addition, adaptive neural networks (NNs) and minimum learning parameters (MLPs) algorithm are used to approximate environmental disturbances and uncertain dynamics, while reducing online update parameters. Stability analysis proved that all signals in closed-loop are uniformly ultimately bounded and the formation tracking errors are arbitrarily small. Simulation results demonstrate the effectiveness of the controller.