شناسایی و تخمین ضرایب آیرودینامیکی هواپیما با استفاده از فیلتر توسعه یافته

نوع مقاله : مقاله پژوهشی

نویسنده

عضو هیات علمی / گروه خلبانی، دانشگاه افسری امام علی (ع)، تهران

چکیده

فیلتر کالمن توسعه یافته (EKF) یک الگوریتم پرکاربرد برای شناسایی پارامتر بازگشتی است و مبتنی بر تقریب مرتبه اول دینامیک سیستم است. اخیراً فیلتر کالمن بدون بو (UKF) به عنوان راه حل نظری بهتری برای فیلتر کالمن توسعه یافته در رشته فیلترینگ غیرخطی پیشنهاد شده است و در مسائل هدایت، تخمین پارامتر و تخمین دوگانه توجه زیادی به خود جلب کرده است . استفاده از فیلتر کالمن بدون بو(UKFaug) به عنوان ابزار تخمین پارامتر بازگشتی برای مدل‌سازی آیرودینامیکی نسبتاً بدون بررسی باقی مانده است از اینرو در این مقاله ضمن استفاده از این الگوریتم، عملکرد سه الگوریتم تخمین پارامتر بازگشتی برای تخمین پارامتر آیرودینامیکی از داده‌های پرواز یک هواپیما با بال ثابت با هم مقایسه شده‌است.‌ نتایج نشاندهنده این می باشد که عملکرد هر سه الگوریتم تا حدودی شبیه به هم بوده است ولی فیلتر کالمن بدون بو در برخی موارد عملکرد بهتری در مقایسه با دو روش دیگر از خود نشان می‌دهد. مقایسه صورت گرفته کمک می‌کند تا بسته به شرایط مختلف بتوان بهترین و کم هزینه ترین روش را انتخاب کرد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Aerodynamic parameter estimation from flight data applying extended and unscented kalman filter

نویسنده [English]

  • M. Abolfazl Mokhtari
Flight and Engineering Department, Imam ALi University, Tehran
چکیده [English]

Aerodynamic parameter estimation is an integral part of aerospace system design and life cycle process. Recent advances in computational power have allowed the use of online parameter estimation techniques in varied applications such as reconfigurable or adaptive control, system health monitoring, and fault tolerant control. The combined problem of state and parameter identification leads to a nonlinear filtering problem; furthermore, many aerospace systems are characterized by nonlinear models as well as noisy and biased sensor measurements. Extended Kalman filter (EKF) is a commonly used algorithm for recursive parameter identification due to its excellent filtering properties and is based on a first order approximation of the system dynamics. Recently, the unscented Kalman filter (UKF) has been proposed as a theoretically better alternative to the EKF in the field of nonlinear filtering and has received great attention in navigation, parameter estimation, and dual estimation problems. However, the use of UKF as a recursive parameter estimation tool for aerodynamic modeling is relatively unexplored. In this paper we compare the performance of three recursive parameter estimation algorithms for aerodynamic parameter estimation of two aircraft from real flight data.

کلیدواژه‌ها [English]

  • System identification
  • Kalman filtering
  • Recursive parameter estimation
  • Unscented filter
  • Estimation technique
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