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Unmanned surface vessel(USV) has been applied in the maritime security inspection and resources exploration to execute complex works with its advantages in automation and intelligence. While the nonlinear USV working in the complex ocean environment, the good trajectory tracking performance is an important capacity. However, the nonlinearity, modeling uncertainties (e.g., modeling error and parameter variations) and external disturbance (wind, wave, current, etc) are the main difficulties, which deteriorates the control performance. To solve this issue, most existing algorithms for USV's tracking have been developed based on the linearization of the USV's nonlinear dynamic model at specific equilibrium point. However, the integrated effect of nonlinearities, modeling uncertainties and external disturbance has not been well considered, which can degrade the USV's tracking performance. Therefore, to achieve the good tracking performance for USV, a nonlinear dynamic model is strictly derived in this paper with the integrate consideration of abovementioned issues, and an adaptive sliding mode control design using RBFNN(Radial Basis Function Neural Network) and disturbance-observer is subsequently developed, where a RBFNN approximator is designed to approximate and compensate modeling uncertainties, and a disturbance-observer is designed to estimate and compensate the effect of the external disturbance. Furthermore, the global stability of the overall closed-loop system of USV are strictly guaranteed. The comparative simulation is carried out to validate the fast response, better transient performance and robustness of our proposed control design via comparing with the existing methods.

The bilateral teleoperation technique has drawn much attention with its attractive superiority to implement the tasks in hazardous environments. Transmission delays and uncertainties are the two main challenges in the nonlinear bilateral teleoperation system to guarantee stability and achieve good transparency performance (including position tracking and force feedback) simultaneously. In this paper, a radial basis function neural network (RBFNN)-based adaptive sliding mode control design is developed for the nonlinear bilateral teleoperation system with transmission delays and uncertainties. For details, the reference trajectory producer is designed in both the master and slave sides to produce the passive reference trajectories for the tracking of master/slave manipulators. The RBFNN-based adaptive sliding mode controller is designed separately for the master and slave to achieve the good tracking performance under system uncertainties. To mitigate the negative effect of transmission delays on the system's stability, a projection mapping by saturation function is applied in the master side to guarantee the boundedness of the delayed environmental torque. Thus, the global stability and the good transparency performance with both position tracking and force feedback can be simultaneously achieved for our proposed method. The comparative experiment is carried out, and the results show the significant performance improvement with our proposed control design.

Good transparency performance (e.g., position tracking and force feedback) is an important issue in the control design of teleoperation systems. The four-channel architecture has advantages in the achievement of good transparency performance for teleoperation systems. However, most of the existing four-channel architectures are used in the linear teleoperation system, which cannot be well applied to execute tasks in the increasingly complicated operation environments. Thus, designing the four-channel architecture to achieve good transparency performance for nonlinear teleoperation systems with uncertainties is still a challenging issue. In this paper, the disturbance-observer-based sliding mode control design with a novel four-channel architecture is developed for the nonlinear bilateral teleoperation system to achieve a good transparency performance in the consideration of uncertainties and external disturbance, where the operating torque, master position, slave position, and the environment torque signals are transmitted through the communication channel. The reference tracking planner is designed in both the master and slave sides to produce the passive reference trajectories, and the disturbance-observer-based sliding mode controller is subsequently designed for the master and slave robots to achieve the good position tracking performance. The good force feedback performance can be achieved by the proper selection of plan parameters in the master reference tracking planner. Thus, the good transparency performance with both position tracking and force feedback can be achieved for the nonlinear bilateral teleoperation system with this novel four-channel architecture. The comparative simulation and experiment are implemented, and the results demonstrate good transparency performance achievement with the proposed control design.

Unmanned surface vesssel (USV) has been widely applied due to its advantages in the military reconnaissance and resources exploration. Path planning is one of the critical issues for USV applications, which usually includes global and local path planning methods. However, individual global path planning algorithms may not be easy to detect the dynamic obstacles in the environment, and individual local path planning algorithms may not always guarantee the existence of the feasible solution for the complex environment. Therefore, a hybrid algorithm which effectively combines global and local path planning is proposed in this paper to overcome these drawbacks. The A* algorithm is used in the global path planning to generate a global path for USV to reach the target point. The dynamic window algorithm (DWA) is used in the local path planning to avoid the dynamic obstacles and track the global path by following the local target point which is the intersection of the global and local path planning. The weight coefficient considering sea state is added in the objective function of DWA, where the security of USV can be guaranteed by reducing the weight of velocity and increasing the weight of distance when the sea state level becomes high. Thus, USV can get a global optimal path and reach the target point in complex environment with dynamic obstacles and ocean currents via the proposed hybrid algorithm, and the comparative simulation is carried out to verify the effectiveness and advantage of the proposed method.

Unmanned surface vessel (USV) has been widely applied due to its advantages in the maritime security inspection and resources exploration. Good trajectory tracking performance is a critical issue in the control design of USV. However, most of the existing controllers are designed based on linear dynamic model or do not well consider the integrated effect nonlinearities, various uncertainties (e.g., modeling error and parameter variations) and external disturbance (wind, wave, current, etc). In this paper, a nonlinear dynamic model is established for USV with the integrate consideration of these issues, and a disturbance-observer-based sliding mode control design is subsequently proposed to achieve the good tracking performance, where the observer is to estimate and compensate the modeling uncertainties and external disturbance. Theoretically, the stability of the observer and overall closed-loop system of USV are guaranteed via the Lyapunov theorem. The comparative simulation is carried out, and the results show the fast response, better transient performance and robustness of the proposed control design.