无人艇编队预设时间多目标协同包围跟踪控制

Prescribed-time Cooperative Formation Control of Unmanned Surface Vessels for Multi-target Encirclement and Tracking

  • 摘要: 【目的】对未知时变环境干扰下的欠驱动无人艇编队(Unmanned Surface Vehicles,USVs)多目标协同包围跟踪控制问题进行了研究,提出了一种将位置控制和速度控制解耦的预设时间控制方法。【方法】在位置控制层,针对多目标位置实时变化及无人艇欠驱动问题,设计了预设时间协同包围跟踪制导律,实现了无人艇以时变缩放的包围圈跟踪多个目标形成的凸组合。在速度控制层,设计了预设时间滑模包围控制律,跟踪由制导律输出的期望速度信号。为降低未知时变环境干扰对控制系统的影响,将预设时间理论引入到径向基函数神经网络(Radial Basis Function Neural Network,RBFNN)的权值更新律中,设计了预设时间RBFNN干扰估计器对系统所受的干扰进行估计和补偿。通过Lyapunov稳定性理论分析证明,所提的控制方法能够使得系统是预设时间稳定的。【结果】仿真结果表明所提的控制方法能够实现无人艇编队的位置跟踪误差、速度跟踪误差及干扰估计误差在预设时间内收敛至零附近,所使用的干扰估计方法使得无人艇编队的纵向速度积分误差和艏向角速度积分误差分别降低了13.55%和24.46%。【结论 

    Abstract: Objectives The paper investigates the multi-objective collaborative encirclement tracking control problem of underactuated unmanned surface vehicles (USVs) under unknown time-varying environmental disturbances. A decoupled control method for position and velocity is proposed based on the prescribed-time control approach. Methods In the position control layer, considering the real-time changes in the positions of multiple targets and the underactuated nature of the USVs, a prescribed-time cooperative encirclement tracking guidance law is designed to enable the USVs to track the convex combination of multiple targets with a time-varying scaling of the encirclement. In the velocity control layer, a prescribed-time sliding mode encirclement control law is designed to track the desired velocity signal output by the guidance law. To mitigate the impact of unknown time-varying disturbances on the control system, the prescribed-time theory is incorporated into the weight update law of the Radial Basis Function Neural Network (RBFNN). A prescribed-time RBFNN disturbance estimator is designed to estimate and compensate for the disturbances experienced by the system. The Lyapunov stability theory is employed to analyze and prove that the proposed control method ensures the prescribed-time stability of the system. Results Simulation results demonstrate that the proposed method can achieve the convergence of position tracking errors, velocity tracking errors, and disturbance estimation errors to zero within the prescribed-time. The disturbance estimation method used reduces the longitudinal velocity integral absolute error and the yaw rate integral absolute error of the USV formation by 13.55% and 24.46%, respectively. Conclusions In conclusion, the proposed method is capable of stabilizing the multi-objective tracking control system of the USV formation within the prescribed-time. It also demonstrates certain advantages in estimating unknown time-varying environmental disturbances and enhancing the dynamic performance of the system.

     

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