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首页> 外文期刊>Journal of guidance, control, and dynamics >Online Aerodynamic Model Identification Using a Recursive Sequential Method for Multivariate Splines
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Online Aerodynamic Model Identification Using a Recursive Sequential Method for Multivariate Splines

机译：递归序贯方法的多元样条线在线气动模型辨识

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摘要

Avoiding high computational loads is essential to online aerodynamic model identification algorithms, which are at the heart of any model-based adaptive flight-control system. Multivariate simplex B-spline methods are excellent function approximation tools for modeling the nonlinear aerodynamics of high-performance aircraft. However, the computational efficiency of the multivariate simplex B-spline method must be improved in order to enable real-time onboard applications, for example, in adaptive nonlinear flight-control systems. In this paper, a new recursive sequential identification strategy is proposed for the multivariate simplex B-spline method aimed at increasing its computational efficiency, thereby allowing its use in onboard system identification applications. The main contribution of this new method is a significant reduction of the computational load for large-scale online identification problems as compared to the existing multivariate simplex B-spline methods. The proposed method consists of two sequential steps for each time interval and makes use of a decomposition of the global problem domain into a number of subdomains, called modules. In the first step, the B-coefficients for each module are estimated using a least-squares estimator. In the second step, the local B-coefficients for each module are then smoothened into a single global B-coefficient vector using a linear minimum mean-square errors estimation. The new method is compared to existing batch and recursive multivariate simplex B-spline methods in a numerical experiment in which an aerodynamic model is recursively identified based on data from a NASA F-16 wind-tunnel model.
机译：避免高计算负荷对于在线空气动力学模型识别算法至关重要，而在线空气动力学模型识别算法是任何基于模型的自适应飞行控制系统的核心。多元单纯形B样条方法是用于对高性能飞机的非线性空气动力学建模的出色函数逼近工具。但是，必须提高多元单纯形B样条方法的计算效率，以实现实时机载应用，例如在自适应非线性飞行控制系统中。在本文中，针对多元单纯形B样条方法提出了一种新的递归顺序识别策略，旨在提高其计算效率，从而使其可用于机载系统识别应用。与现有的多元单纯形B样条方法相比，这种新方法的主要贡献是可大大减少大规模在线识别问题的计算量。所提出的方法包括每个时间间隔的两个连续步骤，并利用了将全局问题域分解为多个子域（称为模块）的方法。第一步，使用最小二乘估算器估算每个模块的B系数。在第二步中，然后使用线性最小均方误差估计将每个模块的局部B系数平滑为单个全局B系数向量。在数值实验中，将该新方法与现有的批处理和递归多元单纯形B样条方法进行了比较，在该实验中，根据NASA F-16风洞模型的数据递归确定了空气动力学模型。

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  • 来源
    《Journal of guidance, control, and dynamics》 |2013年第5期|1278-1288|共11页
  • 作者

    L. G. Sun; C. C. de Visser; Q. P. Chu; J. A. Mulder;

  • 作者单位

    Delft University of Technology, 2600 GB Delft, The Netherlands Control and Simulation Division, Faculty of AerospaceEngineering, P.O. Box 5058;

    Delft University of Technology, 2600 GB Delft, The Netherlands Control and Simulation Division, Faculty of Aerospace Engineering, P.O. Box 5058;

    Delft University of Technology, 2600 GB Delft, The Netherlands Control and Simulation Division, P.O. Box 5058;

    Delft University of Technology, 2600 GB Delft, The Netherlands Control and Simulation Division, Faculty of AerospaceEngineering, P.O. Box 5058;

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