使用混合反步进和反馈线性化控制策略在复杂机动中控制四旋翼飞行器

Ali Keymasi‐Khalaji, Iman Saadat
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摘要

本文介绍了一种用于四旋翼飞行器的新型控制算法,即反步法和反馈线性化相结合的方法,以及一种不确定性估计器。其目的是开发一种能够处理各种飞行条件、补偿不确定性和干扰并有效控制高速机动的鲁棒控制算法。为此,首先使用牛顿-欧拉法推导出四旋翼飞行器动力学模型。随后,为四旋翼飞行器的内部控制层设计了反步进控制算法,并将反馈线性化方法应用于外部控制层。此外,还设计了一个估计器来减轻干扰和不确定性的影响。比较了所提出的反步进和反馈线性化组合算法和反步进方法,发现反步进和反馈线性化组合算法在不同方面都优于反步进方法。值得注意的是,组合式反步和反馈线性化算法实现了更快的轨迹跟踪,并显示出更小的稳态误差。此外,将不确定性估计器集成到反步法和反馈线性化组合算法中,可有效减轻干扰和不确定性的不利影响。本文介绍了跟踪控制的比较结果,以评估所提出的算法在各种场景和案例研究中的性能。
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Quadrotor control in complex manoeuvres using a hybrid backstepping and feedback linearisation control strategy
In this article, a novel control algorithm called the combined backstepping and feedback linearisation method, along with an uncertainty estimator, is presented for quadrotors. The objective is to develop a robust control algorithm capable of handling various flight conditions, compensating for uncertainties and disturbances, and effectively controlling high-speed manoeuvres. To accomplish this, the quadrotor dynamics model is first derived using the Newton–Euler method. Subsequently, a backstepping control algorithm is designed for the quadrotor's internal control layer, followed by the application of the feedback linearisation method to the external control layer. An estimator is also designed to mitigate the effects of disturbances and uncertainties. A comparison is made between the proposed combined backstepping and feedback linearisation algorithm and the backstepping method, revealing that the combined backstepping and feedback linearisation algorithm outperforms the backstepping method across different aspects. Notably, the combined backstepping and feedback linearisation algorithm achieves faster trajectory tracking and demonstrates fewer steady-state errors. Additionally, the integration of the uncertainty estimator into the combined backstepping and feedback linearisation algorithm effectively mitigates the detrimental effects of disturbances and uncertainties. Comparative results for tracking control are presented to evaluate the performance of the proposed algorithm across various scenarios and case studies.
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