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Robustness Analysis of Flight Controllers for Fixed-Wing Unmanned Aircraft Systems Using Integral Quadratic Constraints

机译:使用积分二次约束的固定翼无人机系统飞行控制器的鲁棒性分析

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A robust analysis framework for mathematical validation of flight control systems is presented. The framework is specifically developed for the complete uncertainty characterization, quantification, and analysis of small fixed-wing unmanned aircraft systems (UAS). The analytical approach presented within is based on integral quadratic constraint (IQC) analysis and uses linear fractional transformations on uncertainties to represent system models. While IQC analysis has a sound theoretical foundation, there remains a lack of literature on applying IQCs to complex engineering systems and evaluating their effectiveness with physical data. One difficulty lies in appropriately characterizing and quantifying the uncertainties such that the resulting uncertain model is representative of the physical system while forming a computationally tractable problem that is not overly conservative. This paper addresses these challenges by applying IQC analysis tools to analyze the robustness of a UAS flight control system. Uncertainties are characterized and quantified based on mathematical models and flight test data obtained in-house for a small commercial off-the-shelf platform with a custom autopilot. Furthermore, an established gradient-based minimization routine is implemented with IQC analysis to demonstrate how IQC analysis can guide the control design process. This approach also reveals the controlled system’s sensitivities to uncertainties, thereby assisting the designer in determining how much uncertainty allowance ought to exist in certain aspects of the UAS. Finally, these methods are tested in physical flight to showcase the effectiveness of IQC analysis and assess the conservativeness of the approach. The proposed framework is also transferable to other fixed-wing UAS platforms, effectively taking IQC analysis beyond academic examples to practical application in UAS system design, control design, and airworthiness certification.
机译:提出了用于飞行控制系统数学验证的鲁棒分析框架。该框架是专为小型固定翼无人机系统(UAS)的完整不确定性表征,量化和分析而开发的。此处介绍的分析方法基于积分二次约束(IQC)分析,并对不确定性使用线性分数变换来表示系统模型。尽管IQC分析具有良好的理论基础,但仍然缺乏有关将IQC应用到复杂工程系统并利用物理数据评估其有效性的文献。一个困难在于适当地表征和量化不确定性,以使得最终的不确定性模型代表物理系统,同时形成一个不太容易保守的可计算处理的问题。本文通过应用IQC分析工具来分析UAS飞行控制系统的稳定性来应对这些挑战。根据数学模型和对内部定制的小型商业现成平台(带有自定义自动驾驶仪)的飞行测试数据,对不确定性进行特征化和量化。此外,已建立的基于梯度的最小化例程与IQC分析一起实施,以演示IQC分析如何指导控制设计过程。这种方法还揭示了受控系统对不确定性的敏感性,从而帮助设计人员确定UAS某些方面应存在多少不确定性余量。最后,在物理飞行中对这些方法进行了测试,以展示IQC分析的有效性并评估该方法的保守性。所提出的框架还可以转移到其他固定翼UAS平台,从而有效地将IQC分析超越了学术实例,应用于UAS系统设计,控制设计和适航性认证的实际应用。

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