Prescribed-Time Bipartite Formation Control for Heterogeneous UGVs–UAVs Systems With Improved Extended State Observers

IF 3.2 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS
Yingjiang Zhou, Shaojun Zhou, Guoping Jiang, Cheng Hu, Chaoxu Mu
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引用次数: 0

Abstract

This article aims to address two challenging issues in heterogeneous systems composed of unmanned aerial vehicles (UAV) and unmanned ground vehicles (UGV): 1) compensating for system disturbances within prescribed-time and 2) maintain the non-zero speed of heterogeneous UAVs and UGVs system(HUUS) under complex constraints. The main difficulty lies in how to effectively compensate for external disturbances and internal unknown dynamics within prescribed-time while satisfying constraints, and simultaneously ensuring that the HUUS maintains non-zero velocity. To overcome these challenges, an improved prescribed-time extended state observer (PTESO) is proposed in this article. This observer can estimate uncertainties and disturbances, with the convergence time that can be pre-adjusted independently of initial conditions. By adopting an improved time-varying function as the PTESO gain, the transient peak phenomenon caused by excessive gain is avoided. To make the formation error converge within prescribed-time without violating error constraints, a time-varying barrier Lyapunov function is introduced in the control strategy, a prescribed-time bipartite formation control strategy based on the extended state observer is proposed. Furthermore, to address the problem that the speed tends to zero in the cross-dimensional model system, a comprehensive dynamic model of HUUS is derived, ensuring that the system velocity maintains the value. Compared with existing schemes, the proposed observer and controller demonstrate faster and more stable convergence performance. Finally, the effectiveness of the designed method is verified through simulation examples, proving the significant improvement in robustness and dynamic response capability of HUUS.

具有改进扩展状态观测器的异构ugv - uav系统的规定时间双部编队控制
本文旨在解决由无人机(UAV)和无人地面车辆(UGV)组成的异构系统中两个具有挑战性的问题:1)在规定时间内补偿系统干扰;2)在复杂约束条件下保持异构无人机和无人地面车辆系统(HUUS)的非零速度。主要难点在于如何在满足约束条件的情况下,在规定时间内有效补偿外部扰动和内部未知动力学,同时保证HUUS保持非零速度。为了克服这些挑战,本文提出了一种改进的规定时间扩展状态观测器(PTESO)。该观测器可以估计不确定性和干扰,收敛时间可以独立于初始条件进行预调整。采用改进的时变函数作为PTESO增益,避免了增益过大引起的瞬态峰值现象。为了使编队误差在不违反误差约束的情况下在规定时间内收敛,在控制策略中引入时变障碍Lyapunov函数,提出了一种基于扩展状态观测器的规定时间二部编队控制策略。此外,针对跨维模型系统中速度趋于零的问题,推导了HUUS的综合动态模型,保证了系统速度保持不变。与现有方案相比,所提出的观测器和控制器具有更快、更稳定的收敛性能。最后,通过仿真算例验证了所设计方法的有效性,证明了HUUS的鲁棒性和动态响应能力得到了显著提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
International Journal of Robust and Nonlinear Control
International Journal of Robust and Nonlinear Control 工程技术-工程:电子与电气
CiteScore
6.70
自引率
20.50%
发文量
505
审稿时长
2.7 months
期刊介绍: Papers that do not include an element of robust or nonlinear control and estimation theory will not be considered by the journal, and all papers will be expected to include significant novel content. The focus of the journal is on model based control design approaches rather than heuristic or rule based methods. Papers on neural networks will have to be of exceptional novelty to be considered for the journal.
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