Objective This paper presents a novel approach to the precise control of variable mass unmanned surface vehicles (USVs) during payload deployment tasks, addressing the control challenges caused by unpredictable variations in both mass and draught. The primary objective is to propose an adaptive control method that can effectively adapt to these unknown variations in mass and draught, thereby ensuring the stable and reliable operation of the USV under complex and dynamic mass conditions.
Method Firstly, regarding to the motion modeling of variable-mass USVs, this study analyzes the impact mechanism of mass variations on the hydrodynamic characteristics of the vehicle. It also analyzes how these variations, through changes in the parameters of the dynamic model, affect the vehicle’s motion state. To address the issue that current controller design models are insufficient in analytically and intuitively representing this coupling influencing process, we use the draught term as the reference variable. The progressive coupling relationships among draught and the mass term, added mass term, added moment of inertia term, and various hydrodynamic derivatives are systematically analyzed. Based on this analysis, the mathematical model for the maneuvering motion of the variable-mass USV is then constructed. Secondly, to design an effective estimation method, a super-twisting sliding mode observer is proposed for estimating the unknown draught and mass of the variable-mass USV. This method is based on an analysis of the coupling relationships between mass variations and the vehicle's motion state and control inputs, as described in the maneuvering model of the USV. Subsequently, addressing the motion control problem of variable-mass USVs under unknown mass variations, we propose an adaptive speed control strategy based on the sliding mode observer. Specifically, leveraging the maneuvering motion mathematical model of the variable-mass USV and the draught observations from the sliding mode observer, a feedback linearization method is used to design the adaptive speed control algorithm. The asymptotic stability of the proposed control algorithm is proved using the Lyapunov theory.
Results A series of simulation experiments are conducted to validate the proposed method. In the mass step-change observation experiment, the super-twisting sliding mode observer demonstrates satisfactory performance. Compared to the traditional sliding mode observer, the average observation errors of the draught and mass are significantly reduced by 43.75% and 43.76%, respectively. Furthermore, it shows rapid convergence when mass changes occur suddenly. In the continuous mass change observation experiment, the observer also performs excellently, exhibiting fast convergence and high accuracy, thus demonstrating significant advantages compared to the traditional observer. The speed control experiments reveal that the designed adaptive speed control algorithm can stably track the target speed under both mass step-change and continuous-change conditions. Although it may require slightly more adjustment time compared to the traditional Backstepping controller, it offers significant advantages in handling variations in mass and draught, achieving superior control performance. In the environmental disturbance experiment, while the adaptive control algorithm maintains stable speed control, demonstrating a certain degree of robustness, it also highlights the need for further improvement in the draught observation method to enhance its disturbance rejection capabilities.
Conclusion The control algorithm proposed in this paper is well-suited for control scenarios involving unknown mass variations, such as payload launch or agricultural dispensing operations. Future research should focus on mitigating the impact of external environmental disturbances on observation accuracy and enhancing the robustness of the observation algorithm to better handle such disturbances.
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