李志豪, 董早鹏, 刘伟, 盛金亮, 孙蓬勃. 基于生物启发模型的欠驱动AUV协同编队终端滑模控制[J]. 中国舰船研究. DOI: 10.19693/j.issn.1673-3185.03803
引用本文: 李志豪, 董早鹏, 刘伟, 盛金亮, 孙蓬勃. 基于生物启发模型的欠驱动AUV协同编队终端滑模控制[J]. 中国舰船研究. DOI: 10.19693/j.issn.1673-3185.03803
Autonomous cooperative formation control of underactuated AUVs based on bio-inspired terminal sliding mode method[J]. Chinese Journal of Ship Research. DOI: 10.19693/j.issn.1673-3185.03803
Citation: Autonomous cooperative formation control of underactuated AUVs based on bio-inspired terminal sliding mode method[J]. Chinese Journal of Ship Research. DOI: 10.19693/j.issn.1673-3185.03803

基于生物启发模型的欠驱动AUV协同编队终端滑模控制

Autonomous cooperative formation control of underactuated AUVs based on bio-inspired terminal sliding mode method

  • 摘要: 摘 要:【目的】针对存在外界环境干扰以及模型不确定的欠驱动AUV协同编队轨迹跟踪控制问题,提出了一种基于生物启发模型的终端滑模(BFTSMC)编队控制方法。【方法】首先,利用微分跟踪器平滑领航艇的艏向角,通过领航—跟随者的编队控制方法生成跟随艇的期望轨迹;其次利用反步法推导出虚拟期望速度以镇定AUV的位置误差,并采用生物启发模型,对虚拟速度控制律进行平滑处理,减少了前期系统的抖振和对控制器的计算复杂程度;然后设计基于反正切函数的固定时间非奇异终端滑模控制器,使得整个编队系统达到更快收敛;最后为了减少外界环境干扰和模型不确定性带来的复合干扰,采用基于最小学习参数法的神经网络(MLP-RBF)进行补偿。【结果】相比于积分滑模控制,所提出的控制方法可使整个AUV编队系统更快更精确地跟踪上参考轨迹。【结论】所设计的编队控制算法能够实现对AUV的三维轨迹跟踪。

     

    Abstract: Abstract: ObjectivesIn order to achieve the cooperative formation control of underactuated autonomous underwater vehicle (AUVs) with external environmental disturbances and model uncertainties, a bio-inspired terminal sliding mode control method is proposed. MethodsFirstly, the bow angle of the leader AUV is smoothed by differential tracker and the desired trajectory for the follower AUVs are generated based on the leader–follower formation control strategy. Then, the virtual control laws of the velocity are designed in a backstepping approach and the AUVs' position errors are stabilized. At the same time, aiming at smoothing the virtual control law, alleviating the system oscillation and reducing the computational complexity of the controller, a bio-inspired model is designed. In addition, a fixed-time nonsingular terminal sliding mode is proposed for convergence faster. Finally, a radial basis function neural network (RBFNN) with minimal learning parameter (MLP) is adopted for approximating the environmental disturbances and model uncertainties. ResultsCompared with integral sliding mode control, the proposed control method can enable the AUV formation system to track the desired trajectory faster and more accurately. ConclusionsThe designed formation control algorithm can achieve the trajectory tracking of AUVs.

     

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