Abstract
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.
Highlights
With the development of advanced mechatronics and automation [1]–[4], unmanned surface vessel(USV) can undertake the oceanic tasks with the advantages of intelligence and safety, and has been widely applied in military and commercial fields in a long time [5]–[9], such as oceanic exploration and collection, maritime rescue, environmental inspection, etc
To improve the USV’s trajectory tracking performance, several control algorithms have been developed based on the linearization of the USV’s nonlinear dynamic model at specific equilibrium point, which has good performance for trajectory tracking around the specific equilibrium point
An adaptive sliding mode control design using RBFNN and disturbance-observer is proposed in this paper for nonlinear USV system with modeling uncertainties and external disturbances
Summary
With the development of advanced mechatronics and automation [1]–[4], unmanned surface vessel(USV) can undertake the oceanic tasks with the advantages of intelligence and safety, and has been widely applied in military and commercial fields in a long time [5]–[9], such as oceanic exploration and collection, maritime rescue, environmental inspection, etc. For the USV’s trajectory tracking with complex external disturbances, some trajectory control algorithms have been proposed based on nonlinear dynamic model [16]–[23], such as adaptive back-stepping control, sliding mode control, neural network-based control, model predictive control, etc. To achieve the good tracking performance, a disturbance-observer-based sliding mode control design is proposed in [16], where a disturbance observer is designed to estimate and compensate the modeling uncertainties and external disturbance. An adaptive sliding mode control algorithm using RBFNN and disturbance-observer is proposed based on nonlinear USV’s dynamic model. (d) Integrating the RBFNN approximation and disturbanceobserver, an adaptive sliding mode algorithm is developed for USV’s nonlinear dynamics, which is more suitable for any desired trajectories.
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