Resumen
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.