Event-Based Finite-Time Formation Tracking Control for UAV With Bearing Measurements

IF 7.2 1区 工程技术 Q1 AUTOMATION & CONTROL SYSTEMS IEEE Transactions on Industrial Electronics Pub Date : 2024-12-19 DOI:10.1109/TIE.2024.3511141
Can Ding;Zhe Zhang;Zhiqiang Miao;Yaonan Wang
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Abstract

This article presents an adaptive finite-time event-triggered bearing-only formation controller for multiple unmanned aerial vehicles (UAVs) subjected to unknown external disturbances. Initially, a finite-time disturbance observer (FTDO) was designed to stabilize the disturbance estimation error without prior knowledge of the disturbance's upper limit. Subsequently, a dynamic surface control (DSC) strategy utilizing only bearing information was developed. This approach addresses formation tracking within finite time intervals and resolves the “differential explosion” problem commonly encountered in traditional backstepping methods by incorporating a tracking differentiator. Additionally, an event-triggered control mechanism was implemented to minimize the frequency of control updates, thereby optimizing computational efficiency. The proposed method's effectiveness and practical applicability are validated through thorough stability proofs of the closed-loop system and extensive simulations, including detailed hardware-in-the-loop (HITL) experiments.
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利用方位测量对无人飞行器进行基于事件的有限时间编队跟踪控制
针对多架无人机受到未知外部干扰的情况,提出了一种自适应有限时间事件触发纯方位编队控制器。首先,设计了一个有限时间干扰观测器(FTDO)来稳定干扰估计误差,而不需要事先知道干扰的上限。随后,提出了一种仅利用轴承信息的动态表面控制策略。该方法解决了有限时间间隔内的地层跟踪问题,并通过结合跟踪微分器解决了传统反演方法中常见的“微分爆炸”问题。此外,还实现了事件触发控制机制,以最小化控制更新的频率,从而优化计算效率。通过闭环系统的全面稳定性证明和广泛的仿真,包括详细的半实物实验,验证了该方法的有效性和实用性。
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来源期刊
IEEE Transactions on Industrial Electronics
IEEE Transactions on Industrial Electronics 工程技术-工程:电子与电气
CiteScore
16.80
自引率
9.10%
发文量
1396
审稿时长
6.3 months
期刊介绍: Journal Name: IEEE Transactions on Industrial Electronics Publication Frequency: Monthly Scope: The scope of IEEE Transactions on Industrial Electronics encompasses the following areas: Applications of electronics, controls, and communications in industrial and manufacturing systems and processes. Power electronics and drive control techniques. System control and signal processing. Fault detection and diagnosis. Power systems. Instrumentation, measurement, and testing. Modeling and simulation. Motion control. Robotics. Sensors and actuators. Implementation of neural networks, fuzzy logic, and artificial intelligence in industrial systems. Factory automation. Communication and computer networks.
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