This brief addresses the trajectory tracking control problem of a fully actuated surface vessel subjected to asymmetrically constrained input and output. The controller design process is based on the backstepping technique. An asymmetric time-varying barrier Lyapunov function is proposed to address the output constraint. To overcome the difficulty of nondifferentiable input saturation, a smooth hyperbolic tangent function is employed to approximate the asymmetric saturation function. A Nussbaum function is introduced to compensate for the saturation approximation and ensure the system stability. The command filters and auxiliary systems are integrated with the control law to avoid the complicated calculation of the derivative of the virtual control in backstepping. In addition, the bounds of uncertainties and disturbances are estimated and compensated with an adaptive algorithm. With the proposed control, the constraints will never be violated during operation, and all system states are bounded. Simulation results and comparisons with standard method illustrate the effectiveness and advantages of the proposed controller.

This paper presents a path following controller of a surface vessel with a prescribed performance in the presence of input saturation and external disturbances. Based on the three degrees-of-freedom model of the surface vessel, the designed backstepping control scheme features three functional parts, namely, guidance, attitude control, and velocity control. To guarantee that the position errors are confined within the prescribed convergence rates and maximum overshoot, a performance constrained guidance law is formulated with an error transformed function. Command filters are incorporated in the control subsections to limit the magnitude of the virtual controls and simultaneously avoid arduous computations involving their time derivatives. Subsequently, auxiliary systems that are governed by smooth switching functions are developed in an unprecedented manner to compensate for the saturation constraints on actuators. Nonlinear disturbance observers are concurrently introduced to estimate the unknown external disturbances for increasing system's robustness. It is demonstrated that under the proposed control, the prescribed transient and steady tracking performance bounds are never violated, and all closed-loop signals remain uniformly ultimately bounded, despite the presence of input saturation and disturbances. Results from a comparative simulation study illustrate the effectiveness and advantages of the proposed method.

This paper presents an error-constrained line-of-sight (ECLOS) path-following control method for a surface vessel subject to uncertainties, disturbances, and actuator saturation and faults. Based on a cascaded three degrees-of-freedom model of surface vessel, the backstepping technique is adopted as the main control framework. Error constraint of the vessel position is handled by integrating a novel tan-type barrier Lyapunov function. The proposed ECLOS method is in accordance with the classical line-of-sight method where no constraint is imposed. A nonlinear disturbance observer is developed to estimate the lumped disturbance that comprises the effects of parametric uncertainties, external environment disturbances, and actuator saturation and faults. It is proved that under the proposed control, the constrained requirements on the vessel position error are never violated and all closed-loop signals are uniformly ultimately bounded, regardless of fully actuated or under-actuated control configuration. Simulation results and comparisons illustrate the effectiveness and advantages of the proposed ECLOS path-following method.

This brief investigates the trajectory tracking problem for a model-scaled helicopter with a novel robust adaptive radial basis function neural network (RBFNN) augmenting backstepping control approach. The helicopter model is first decomposed into an approximate strict-feedback format with some unmodeled dynamics. Backstepping technique is employed as the main control framework, which is augmented by robust RBFNNs to approximate the unmodeled dynamics. Each robust RBFNN utilizes an n th-order smooth switching function to combine a conventional RBFNN with a robust control. The conventional RBFNN dominates in the neural active region, while the robust control retrieves the transient outside the active region, so that the stability range can be widened. In addition, command filters are employed to approximate derivatives of the virtual controls in the backstepping procedure. This systematic design methodology is proven to achieve ultimate boundedness of the closed-loop helicopter system. Simulations validate the effectiveness of the proposed control approach.