Backstepping Sliding Mode Control Algorithm for Unmanned Aerial Vehicles Based on Fractional-Order Theory

IF 1.4 Q4 ROBOTICS
Yanzhu Zhang, Bo Zhuang, Chunhao Ma, Cheng Zhang
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引用次数: 0

Abstract

Aiming at the problems of slow convergence speed and low tracking accuracy in attitude control and position tracking of quadrotor unmanned aerial vehicles (UAVs). This paper combines the fractional-order calculus theory with the backstepping sliding mode control algorithm, using the backstepping control to compensate for the nonlinearity of the system and the fractional-order theory to eliminate the jitter brought about by the sliding mode control, and proposes a new fractional-order backstepping sliding mode control strategy for the trajectory tracking control of the quadrotor UAV. The proposed fractional-order sliding mode surface increases the control flexibility and improves the robustness and anti-interference ability of the system to some extent. The stability analysis of the system is carried out using the Lyapunov stability theory, and the results prove the stability of the proposed controller. Finally, the effectiveness and feasibility of the proposed method are verified by comparing it with the traditional backstepping sliding mode controller. The simulation results show that the fractional-order reverse-step sliding mode control algorithm proposed in this paper is significantly better than other control algorithms in terms of convergence speed and also has a certain degree of superiority in terms of error elimination.
基于分数阶理论的无人机反步进滑模控制算法
针对四旋翼无人飞行器(UAV)姿态控制和位置跟踪中收敛速度慢、跟踪精度低的问题。本文将分数阶微积分理论与反步滑模控制算法相结合,利用反步控制补偿系统的非线性,利用分数阶理论消除滑模控制带来的抖动,提出了一种新的分数阶反步滑模控制策略,用于四旋翼无人机的轨迹跟踪控制。所提出的分数阶滑动模态面增加了控制的灵活性,在一定程度上提高了系统的鲁棒性和抗干扰能力。利用 Lyapunov 稳定性理论对系统进行了稳定性分析,结果证明了所提控制器的稳定性。最后,通过与传统的反步进滑模控制器进行比较,验证了所提方法的有效性和可行性。仿真结果表明,本文提出的分数阶反向步进滑模控制算法在收敛速度方面明显优于其他控制算法,在误差消除方面也具有一定的优越性。
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来源期刊
CiteScore
3.70
自引率
5.60%
发文量
77
审稿时长
22 weeks
期刊介绍: Journal of Robotics publishes papers on all aspects automated mechanical devices, from their design and fabrication, to their testing and practical implementation. The journal welcomes submissions from the associated fields of materials science, electrical and computer engineering, and machine learning and artificial intelligence, that contribute towards advances in the technology and understanding of robotic systems.
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