Abstract
This paper studies the trajectory tracking control problem of an Air Cushion Vehicle (ACV) with yaw rate error constraint, input effective parameters, model uncertainties and external wind disturbance. Firstly, based on the four-degree of freedom (DOF) vector mathematical mode of ACV, the radial basis function neural network (RBFNN) is adopted to provide the estimation of model uncertainties and external wind disturbance. Then, an adaptive Nussbaum gain-based approach is incorporated with the backstepping control scheme to handle the unknown input efficient parameters. To avoid the complicated derivative of the virtual control laws, the command filter and auxiliary systems are introduced in backstepping. Furthermore, combing a barrier Lyapunov function (BLF) with backstepping technique, a novel trajectory tracking safety controller is designed to ensure all signals of the closed-loop system are uniformly ultimately bounded, while the yaw rate error is within the pre-set safe range. Finally, the simulation results show the effectiveness of the controller scheme.
Highlights
An air-cushion vehicle (ACV), which is a high-performance ship, has a flexible skirt system at the bottom and can be lifted by an air cushion force
With backstepping technique, a novel trajectory tracking safety controller is designed to ensure all signals of the closed-loop system are uniformly bounded, while the yaw rate error is within the pre-set safe range
We can see that the surge velocity error under the Nussbaum controller can converge to near zero and remain stable with fast speed. Since both controllers introduce barrier Lyapunov function (BLF) to constrain the slew rate error, the virtual yaw rate errors are limited to a safe range, which can avoid side slip and tail swing phenomena caused by changes in the yaw rate that are too fast
Summary
An air-cushion vehicle (ACV), which is a high-performance ship, has a flexible skirt system at the bottom and can be lifted by an air cushion force. In [24], the author proposed Lyapunov functions to solve the problem of trajectory tracking control of fully actuated unmanned vessels with input and output asymmetric constraints. This paper, is the first to combine the BLF, Nussbaum function and backstepping methods to address the rudder nonlinearity and constraints of the yaw rate error. Motivated by the above analyses, a novel control method is designed to solve the trajectory tracking of ACVs under the consideration of unknown effective input parameters, the turning rate error within the safety limit and wind disturbance. The stability analysis shows that the proposed control algorithm can accurately track the set trajectory and ensure that the yaw rate error and the roll angle are within a safe range.
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