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Adaptive Backstepping Sliding Mode Tracking Control for Underactuated Unmanned Surface Vehicle With Disturbances and Input Saturation

机译:自适应BackStepping滑模跟踪跟踪控制,用于干扰和输入饱和度

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摘要

This paper presents an adaptive backstepping sliding mode tracking control method for underactuated unmanned surface vehicle. The tracking controller is designed by combining backstepping and adaptive sliding mode control technology. The virtual control law is used to replace the control strategy of position error, and the position tracking control is transformed into velocity control, which can effectively avoid the input saturation and singular value problem existing in the design of traditional backstepping control law. The neural shunt model method is used to solve the differential explosion problem caused by virtual control law. The adaptive technology based on backstepping sliding mode theory is introduced to compensate the model uncertainty and time-varying disturbances of the system, and the robustness of the underactuated USV is enhanced by this method in the unknown environment. Based on the Lyapunov stability theory, it is proved that the control system error is ultimately uniformly bounded. The simulation results show that the proposed adaptive backstepping sliding mode tracking control method can achieve stable tracking in the case of system parameter uncertainty and time-varying disturbances, so it can be concluded that this method is reasonable and effective.
机译:本文介绍了一种适用于欠压无人表面车辆的自适应反向滑动模式跟踪控制方法。跟踪控制器是通过组合BackStepping和自适应滑动模式控制技术来设计的。虚拟控制法用于更换位置误差的控制策略,并且将位置跟踪控制转换为速度控制,这可以有效地避免了传统的背臂控制法设计中存在的输入饱和度和奇异值问题。神经分流模型方法用于解决虚拟控制法引起的差分爆炸问题。引入了基于反向推拉模式理论的自适应技术来补偿系统的模型不确定性和时变紊乱,并且通过该方法在未知环境中提高了欠扰的USV的鲁棒性。基于Lyapunov稳定性理论,证明了控制系统误差最终是均匀的界限。仿真结果表明,建议的自适应反向梗地滑动模式跟踪控制方法在系统参数不确定性和时变扰动的情况下,可以实现稳定的跟踪,因此可以得出结论,该方法是合理且有效的。

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