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تخمین لحظهای نیروی عمودی تایرها با استفاده از مدل تلفیقی سختافزاری- نرمافزاری | ||
| نشریه مهندسی مکانیک امیرکبیر | ||
| مقاله 4، دوره 54، شماره 2، اردیبهشت 1401، صفحه 333-356 اصل مقاله (3.71 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22060/mej.2021.20055.7157 | ||
| نویسندگان | ||
| علی حسینی سالاری؛ حسین میرزایی نژاد* ؛ مجید فولادی ماهانی | ||
| دانشکده فنی و مهندسی، دانشگاه شهید باهنر کرمان،کرمان، ایران. | ||
| چکیده | ||
| بار عمودی روی تایر از عوامل تأثیرگذار بر عملکرد خودرو است. پارامترهای زیادی بر بار عمودی تایر تأثیر میگذارند، ازجمله مهمترین آنها میتوان به جرم خودرو، موقعیت مرکز جرم خودرو همچنین دینامیک و حالتهای خودرو حین مانور اشاره کرد. در این مقاله الگوریتم جدیدی جهت تخمین در لحظه مقدار نیروی عمودی لحظهای تایر توسعه دادهشده است. در گام اول، دادههای ماژول اندازهده دینامیکی و شبکه عصبی مصنوعی به کار گرفتهشده است. به منظور ایجاد داده مصنوعی و همچنین استفاده در گامهای بعدی تخمین مدل تلفیقی نرمافزاری-سخت افزاری توسعه داده شد. در این مدل یک سختافزار اندازهده جایگزین مدلسازی تایر و جاده شدهاست. الگوریتم شامل بلوک مربوط به دینامیک رول و پیچ خودرو است که با ساختار چندلایه شبکههای عصبی مصنوعی آموزش داده شدهاند. خروجی بلوکهای شبکه عصبی، توزیع بار استاتیک روی هر تایر خودرو است که توسط مدل توسعه داده شده، در هر لحظه پایش میشود. در گام دوم الگوریتم، بهمنظور تخمین مقدار انتقال بار حین مانور در خودرو، مدل تلفیقی سختافزاری-نرمافزاری بهکارگرفته شدهاست تا مقدار لحظهای بار عمودی روی هر تایرها محاسبه گردد. مقایسه نتایج بدست آمده از الگوریتم پیشنهادی و مقدار خروجی مدل مرجع اعتباردهی شده در نرمافزار کارسیم، نشاندهنده دقت قابلقبول و عملکرد مناسب این روش است. | ||
| کلیدواژهها | ||
| مدل سختافزاری-نرمافزاری؛ تخمین جرم؛ شبکه عصبی مصنوعی؛ دینامک رول و پیچ؛ نیروی عمودی تایر | ||
| عنوان مقاله [English] | ||
| Online Estimation of Tire Normal Force with Applying Hardware-Software Couple Model | ||
| نویسندگان [English] | ||
| Ali Hosseini Salari؛ Hossein Mirzaeinejad؛ majied fooladi mahani | ||
| Department of Mechanical Engineering, Shahid Bahonar University of Kerman | ||
| چکیده [English] | ||
| Tire online normal force has effects on vehicle safety and performance and dynamic control systems. It is influenced by too many parameters such as vehicle mass and center of gravity position and vehicle instantaneous dynamics states. In this paper, a new estimation algorithm is developed to estimate tires’ online normal forces during a maneuver. The proposed algorithm uses a dynamic measure module to make a hardware-software coupled model which is validated by real test data. The algorithm uses artificial neural networks advantages to estimate the vehicle mass distributions. A combination of real and model-generated data is used to train, test, and validate the artificial neural network structure. By applying two roll and pitch artificial neural network blocks, it estimates tires’ static normal forces. In this respect, the validated vehicle model instantaneously monitors the estimated values. The results show that the proposed algorithm estimates the vehicle total mass with less than 5 percent. In addition, the coupled model uses the estimated static values to estimate the tire's online normal forces with considering the measured vehicle dynamics states by dynamic module. Comparing the obtained results from the proposed method with the outputs from Carsim indicates the acceptable accuracy of this method. | ||
| کلیدواژهها [English] | ||
| Mass estimation, Artificial neural network, Roll and pitch dynamics, Online tire force | ||
| مراجع | ||
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