四旋翼飞行器的超扭转自适应控制。

IF 6.5 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Claudio Rosales, Carlos Vacca Sisterna, Francisco Rossomando, Daniel Gandolfo, Carlos Soria, Ricardo Carelli
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引用次数: 0

摘要

本文提出了一种针对无人机的鲁棒自适应控制器设计方法。所提出的控制体系将超扭转滑模控制器(ST-SMC)与自适应律相结合,以确保在模型不确定性和外部干扰下的高性能轨迹跟踪。ST-SMC有效地减轻了抖振现象,这是滑模控制方案中的一个常见问题,同时保持了对系统非线性的鲁棒性。自适应组件实时动态更新控制参数,以补偿未建模的动力学和参数变化。联合方法协同提高跟踪精度和整体系统稳定性。基于李雅普诺夫理论的严密稳定性分析保证了控制误差的收敛性和有界性。通过传感器增强环境下的实验室飞行实验,验证了所提方法的有效性。结果表明,两种控制方案相互补充,在存在参数不确定性的情况下实现了高性能的鲁棒策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Super-twisting adaptive control of a quadrotor
This paper proposes a novel methodology for the design of robust and adaptive controllers tailored to unmanned aerial vehicles (UAVs). The proposed control architecture integrates a Super-Twisting Sliding Mode Controller (ST-SMC) with an adaptive law to ensure high-performance trajectory tracking under model uncertainties and external disturbances. The ST-SMC effectively mitigates the chattering phenomenon, a common issue in sliding mode control schemes, while maintaining robustness to system nonlinearities. The adaptive component dynamically updates the control parameters in real time to compensate for unmodeled dynamics and parameter variations. The combined approach synergistically improves tracking accuracy and overall system stability. A rigorous stability analysis based on Lyapunov theory guarantees the convergence and boundedness of the control errors. The effectiveness of the proposed method is validated through experimental laboratory flights in a sensor-enhanced environment. The results demonstrate that the two control schemes complement each other, achieving a robust strategy with high performance in the presence of parametric uncertainties.
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来源期刊
ISA transactions
ISA transactions 工程技术-工程:综合
CiteScore
11.70
自引率
12.30%
发文量
824
审稿时长
4.4 months
期刊介绍: ISA Transactions serves as a platform for showcasing advancements in measurement and automation, catering to both industrial practitioners and applied researchers. It covers a wide array of topics within measurement, including sensors, signal processing, data analysis, and fault detection, supported by techniques such as artificial intelligence and communication systems. Automation topics encompass control strategies, modelling, system reliability, and maintenance, alongside optimization and human-machine interaction. The journal targets research and development professionals in control systems, process instrumentation, and automation from academia and industry.
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