Robust adaptive steering control for unmanned surface vehicle with unknown control direction and input saturation

Abstract

A robust adaptive steering control method is proposed to solve the control problem of the unmanned surface vehicle (USV) with uncertainties, unknown control direction, and input saturation. In the controller design process, the adaptive fuzzy system is incorporated into dynamic surface control (DSC) to approximate the uncertainty term induced by external environmental disturbances and model parameters. Then, the Nussbaum function is used to eliminate the requirement for a priori knowledge of the control direction. Besides, to handle the input saturation, the adaptive fuzzy DSC is extended by a second-order nonlinear filter and antisaturation auxiliary function to compensate for the magnitude and rate saturation of the rudder. All signals of the closed-loop system are proven to be uniformly ultimately bounded (UUB) by Lyapunov theorem and the Lemma of Nussbaum gain, and the course error can converge to a small neighborhood of zero through choosing design parameters appropriately. Finally, simulation results and comprehensive comparisons are shown for the USV course system, which is demonstrative of the proposed controller's effectiveness and robustness. KEYWORDSdynamic surface control, input saturation, robust adaptive, unknown control direction, USV steering system 1212

Adaptive saturation compensation for strict‐feedback systems with unknown control coefficient and input saturation

“…Remark The control coefficient is required to be known in the existing work, 37 whereas the saturation dynamic filter () deserts the requirement. Furthermore, in contrast with the anti‐saturation result for the ship steering control 26 requiring known model paramters, the anti‐saturation for the strict‐feedback uncertain system with completely unknown model parameters is dealt with in this work.…”

“…The aforementioned adaptive control schemes did not handle the saturation effect such that the control performance would be destroyed in the harsh conditions due to actuator physical limitations 24‐28 . Under input saturation and disturbance uncertainty, an adaptive tracking scheme was given for the strict‐feedback uncertain systems, 29 where the saturation nonlinearity was approximated by using a hyperbolic tangent function and the adaptive control design was then implemented by using adaptive backstepping method.…”

Command filtering‐based adaptive neural network control for uncertain switched nonlinear systems using event‐triggered communication

“…Nevertheless, the above ETC schemes are not applicable when unknown control directions and input saturation are considered concurrently. It is clear that the cases of unknown control direction and input saturation exist in many engineering applications 36‐39 . In Reference 36, the authors presented an adaptive fuzzy tracking scheme for nonlinear continuous‐time systems, in which the unknown control gains were approximated by Nussbaum‐type functions, and the saturation nonlinearity was addressed by constructing an auxiliary function.…”

“…It is clear that the cases of unknown control direction and input saturation exist in many engineering applications 36‐39 . In Reference 36, the authors presented an adaptive fuzzy tracking scheme for nonlinear continuous‐time systems, in which the unknown control gains were approximated by Nussbaum‐type functions, and the saturation nonlinearity was addressed by constructing an auxiliary function. However, these works in References 36‐39 have not yet been extended to switched nonlinear systems with event‐triggered communication owing to the following two restrictions.…”

Robust Trajectory Tracking Control of Underactuated Surface Vehicles with Prescribed Performance

“…On the one hand, it is clear that a USV is an underactuated system with nonholonomic constraints that cannot be stabilized by continuous time-invariant feedback [5]. Many nonlinear control works have been conducted to solve the tracking problem of underactuated vehicles, such as Lyapanov-based techniques [6], robust adaptive control [7], the backstepping technique [8], sliding mode control [9], neural networks [10], and disturbance-observer-based control [11]. Considering external perturbations, a full state feedback control algorithm based on a backstepping scheme was presented to track a straight line in [8].…”