Abstract
A novel sliding mode control method is proposed to achieve the trajectory tracking of the Unmanned Surface Vessel (USV) and effectively deal with the unmodeled dynamics and external unknown disturbances. First, a fixed-time fractional-order sliding mode control (FTFOSMC) strategy is proposed, combined with the fixed-time control theory and fractional-order control theory based on the sliding mode control method. The FTFOSMC strategy can improve the convergence velocity of the system, and effectively track the desired path, weakening the “chattering” effect in sliding mode control systems. Second, a fixed-time fractional-order sliding mode control strategy combined with the radial basis function neural network (RBF-FTFOSMC) was designed, which can effectively estimate the lumped uncertainties, such as the disturbance of external wind, wave, and current, and the unmodeled dynamics of the USV model. Then, the stability and effectiveness of the designed control strategy are guaranteed by the Lyapunov theory and the corresponding lemmas. Finally, a rigorous simulation experiment is designed to validate the effectiveness and stability of the proposed control strategy. The simulation results show that the control strategy can effectively achieve trajectory tracking of the USV, reduce the “chattering” phenomenon of sliding mode, and effectively estimate the lumped uncertainties.
Highlights
As a representative of the water surface intelligence equipment, the Unmanned SurfaceVessel (USV) has a high degree of autonomy and strong advantages in surface target monitoring and reconnaissance [1–4]
A novel fixed-time fractional-order sliding mode control strategy was proposed for the trajectory tracking of the Unmanned Surface Vessel (USV)
This paper proposed sliding mode control surface fractional-order sliding mode surface (FOSS) that combines fractional-order theory with sliding mode control theory
Summary
As a representative of the water surface intelligence equipment, the Unmanned Surface. Design a fixed-time fractional-order sliding-mode control (FTFOSMC) strategy based on the fixed-time theory It can improve the convergence speed and reduce the impact of “chattering” on the sliding mode surface, so that the USV can quickly converge to track the desired trajectory and on the initial state of the system. To solve the USV trajectory tracking problem, a novel fixed-time fractional-order sliding mode control (FTFOSMC) strategy is proposed and applied. 2. Considering the external disturbance and unmodeled dynamics as the lumped uncertainties, a novel fixed-time fractional-order neural network sliding mode control strategy combined with the RBF-NN (RBF-FTFOSMC) is proposed to ensure that the system can converge in fixed time and effectively realize an accurate estimation of lumped uncertainties. Where φ is a normal number and satisfies 0 < φ < 1
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