This article presents a nonlinear adaptive line-of-sight path following controller for underactuated autonomous underwater vehicles in the presence of ocean currents. Firstly, a new nonsingular path following error kinematic model in the Serret–Frenet frame is developed, where a nominal course angle error is introduced to significantly simplify the guidance law design. Secondly, an adaptive line-of-sight guidance law with the introduction of the current observer is proposed to make the vehicle produce a variable sideslip angle to compensate for the drift force for any parametric curved-path path following. Benefit from the global κ -exponential convergence property of the designed current observer, the actual course angle error can be eliminated indirectly. Then, dynamic controller built on Lyapunov theory and backstepping technique guarantee the uniform global exponential stability of the yaw and relative surge velocity. In the end, stability analysis shows that the global κ -exponential stability is achieved for the closed-loop system. Simulation results demonstrate the effectiveness of the proposed control scheme.

This article presents a robust composite neural-based dynamic surface control design for the path following of unmanned marine surface vessels in the presence of nonlinearly parameterized uncertainties and unknown time-varying disturbances. Compared with the existing neural network-based dynamic surface control methods where only the tracking errors are commonly used for the neural network weight updating, the proposed scheme employs both the tracking errors and the prediction errors to construct the adaption law. Therefore, faster identification of the system dynamics and improved tracking accuracy are achieved. In particular, an outstanding advantage of the proposed neural network structure is simplicity. No matter how many neural network nodes are utilized, only one adaptive parameter that needs to be tuned online, which effectively reduces the computational burden and facilitates to implement the proposed controller in practice. The uniformly ultimate boundedness stability of the closed-loop system is established via Lyapunov analysis. Comparison studies are presented to demonstrate the effectiveness of the proposed composite neural-based dynamic surface control architecture.

In this study, a new neural observer-based dynamic surface control scheme is proposed for the path following of underactuated unmanned surface vessels in the presence of input saturation and time-varying external disturbance. The dynamic surface control technique is augmented by a robust adaptive radial basis function neural network and a nonlinear neural disturbance observer. Radial basis function neural network is employed to deal with system uncertainties, and the nonlinear neural disturbance observer is developed to compensate for the unknown compound disturbance that contains the input saturation approximation error and the external disturbance. Moreover, the stringent known boundary requirement of the unknown disturbance constraint is eliminated with the proposed nonlinear neural disturbance observer. Meanwhile, to deal with the non-smooth saturation nonlinearity, a new parametric hyperbolic tangent function approximation model with arbitrary prescribed precision is constructed, which results in the transient performance improvement for the path following control system. Stability analysis shows that all the signals in the closed-loop system are guaranteed to be ultimately bounded. Comparative simulation results further demonstrate the effectiveness of the proposed control scheme.

This paper presents a transiting target value-based control method for the path following of unmanned surface vessel (USV). The target heading angle is arranged through a transiting process, and the transited target heading angle is treated as the real desired heading angle for the heading controller. This can alleviate the overshoot problem caused by the target heading angle abrupt change during broken line path following. To eliminate the steady state error, an integral type S plane speed controller is employed. Broken line path following field experiment is carried out to validate the rationality of the designed control scheme.

For the way point-based path following control of under-actuated unmanned surface vehicle (USV), a new adaptive compensation line-of-sight (LOS) guidance law and speed solver are proposed, and the fuzzy heading control and integral S-plane speed control are designed. The path following control scheme is based on the separation control, and a speed control is added to improve the overall control structure. For adaptive compensation LOS guidance law, confirm the optimal circle radius of the adaptive LOS method first, and then the angle compensation of heading deviation of navigation is conducted for the LOS angle solved by adaptive LOS method, which is used to guide the USV to approach the desired path with the optimal improved LOS angle. Considering the rapidness of path following, a speed solver is designed according to the distance between the two path points, which can calculate the optimal desired speed of the USV in real time. The experimental results testify the effectiveness and superiority of the proposed guidance strategy, the speed solver and the control strategies.

This study proposes a nonlinear disturbance observer-based path following control scheme for unmanned surface vessel (USV) in the presence of input saturation and unknown external disturbance. The dynamic surface control (DSC) framework is augmented by a robust adaptive radial basis function neural network (RBFNN) and a nonlinear disturbance observer (NDO). The control strategy can avoid the differential explosion problem inherent from traditional backstepping approach. The boundary value of the estimated disturbances that are presented in a number of existing disturbance observers is relaxed.