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Aircraft parameter estimation using Hybrid Neuro Fuzzy and Artificial Bee Colony optimization (HNFABC) algorithm

机译：使用混合神经模糊和人工蜂群优化（HNFABC）算法的飞机参数估计

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摘要

In this paper, method of aerodynamic parameter estimation of aircraft, in the presence of process and measurement noise, have been introduced with the help of relevant experiments carried out on simulated as well as real aircraft data. A merger between two recently proposed artificial intelligence based techniques one of which simulates the intelligent foraging behavior of honey bee swarm viz. Artificial Bee Colony (ABC) optimization along with well-known Adaptive Neuro-Fuzzy System (ANFIS) that simulates the working of a unit of brain viz, neuron combined with the knowledge-based decision making capability of fuzzy system have been shown to be a promisingly new approach to the problem of aerodynamic modeling and parameter estimation for both aerodynamically stable aircraft in the presence of measurement error (sensor noise). Obtained results have been compared with benchmark estimation methods viz. Least Square, Filter Error Method. Corroborating results and comparisons have been furnished highlighting the efficacy of proposed algorithm. Furthermore, it has been shown that the proposed hybrid estimation algorithm can have realizable application in extracting stability and control variables utilizing kinematics of stable aircraft even in absence of adequate information content in the data history. (C) 2017 Elsevier Masson SAS. All rights reserved.
机译：本文通过在模拟和真实飞机数据上进行的相关实验，介绍了在存在过程噪声和测量噪声的情况下飞机空气动力学参数估计的方法。两种最近提出的基于人工智能的技术的合并，其中一项模拟了蜜蜂群的智能觅食行为。人工蜂群（ABC）优化与著名的自适应神经模糊系统（ANFIS）可以模拟一个脑部视觉单元的工作，神经元与基于知识的模糊系统决策能力相结合已被证明是一种在存在测量误差（传感器噪声）的情况下，这两种具有空气动力学稳定性的飞机的空气动力学建模和参数估计问题的有希望的新方法。将获得的结果与基准估计方法进行比较。最小二乘，滤波误差法。提供了证实的结果和比较结果，突出了所提出算法的有效性。此外，已经表明，即使在数据历史中没有足够的信息内容的情况下，所提出的混合估计算法在利用稳定飞机的运动学提取稳定性和控制变量方面也可以具有可实现的应用。 （C）2017 Elsevier Masson SAS。版权所有。

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