基于扩展扰动观测器的无人直升机滑模跟踪控制

IF 1.2 4区 计算机科学 Q4 AUTOMATION & CONTROL SYSTEMS
Ihsan Ullah
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引用次数: 6

摘要

本文提出了一种小型无人直升机在有界外部干扰下跟踪预定轨迹(速度和航向)的鲁棒控制技术。控制器的设计基于直升机的线性化状态空间模型。将直升机多变量动力学分为纵向、侧向动力学和航向升沉动力学两个子系统。这两个子系统之间没有强耦合,每个子系统都设计了独立的控制器。纵向-横向子系统中的外部干扰和模型失配存在于所有(匹配和不匹配)信道中。利用扩展扰动观测器估计模型失配和外部扰动为集总扰动,并设计了基于扩展扰动观测器的滑模控制器来对抗这些扰动的影响。对于头升沉子系统,由于外部干扰和模型失配只发生在匹配通道中,因此针对其对匹配不确定性不敏感的特点设计了二阶滑模控制器。在Simulink中成功地测试了控制性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sliding mode tracking control for unmanned helicopter using extended disturbance observer
This paper presents a robust control technique for small-scale unmanned helicopters to track predefined trajectories (velocities and heading) in the presence of bounded external disturbances. The controller design is based on the linearized state-space model of the helicopter. The multivariable dynamics of the helicopter is divided into two subsystems, longitudinallateral and heading-heave dynamics respectively. There is no strong coupling between these two subsystems and independent controllers are designed for each subsystem. The external disturbances and model mismatch in the longitudinal-lateral subsystem are present in all (matched and mismatched) channels. This model mismatch and external disturbances are estimated as lumped disturbances using extended disturbance observer and an extended disturbance observer based sliding mode controller is designed for it to counter the effect of these disturbances. In the case of heading-heave subsystem, external disturbances and model mismatch only occur in matched channels so a second order sliding mode controller is designed for it as it is insensitive to matched uncertainties. The control performance is successfully tested in Simulink.
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来源期刊
Archives of Control Sciences
Archives of Control Sciences Mathematics-Modeling and Simulation
CiteScore
2.40
自引率
33.30%
发文量
0
审稿时长
14 weeks
期刊介绍: Archives of Control Sciences welcomes for consideration papers on topics of significance in broadly understood control science and related areas, including: basic control theory, optimal control, optimization methods, control of complex systems, mathematical modeling of dynamic and control systems, expert and decision support systems and diverse methods of knowledge modelling and representing uncertainty (by stochastic, set-valued, fuzzy or rough set methods, etc.), robotics and flexible manufacturing systems. Related areas that are covered include information technology, parallel and distributed computations, neural networks and mathematical biomedicine, mathematical economics, applied game theory, financial engineering, business informatics and other similar fields.
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