کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
560257 1451869 2015 14 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Real-time identification of vehicle motion-modes using neural networks
ترجمه فارسی عنوان
شناسایی زمان واقعی وسایل نقلیه با استفاده از شبکه های عصبی
کلمات کلیدی
دینامیک خودرو، شناسایی، حالت حرکت شبکه های عصبی، روش انرژی حرکت حالت
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر پردازش سیگنال
چکیده انگلیسی


• A novel neural networks based vehicle motion-mode identification method is proposed.
• Feature extraction, target classification and result interpretation are introduced.
• The effectiveness of the proposed method is validated by practical vehicle examples.
• The identification accuracy is comparable to the motion-mode energy method.

A four-wheel ground vehicle has three body-dominated motion-modes, that is, bounce, roll, and pitch motion-modes. Real-time identification of these motion-modes can make vehicle suspensions, in particular, active suspensions, target on the dominant motion-mode and apply appropriate control strategies to improve its performance with less power consumption. Recently, a motion-mode energy method (MEM) was developed to identify the vehicle body motion-modes. However, this method requires the measurement of full vehicle states and road inputs, which are not always available in practice. This paper proposes an alternative approach to identify vehicle primary motion-modes with acceptable accuracy by employing neural networks (NNs). The effectiveness of the trained NNs is verified on a 10-DOF full-car model under various types of excitation inputs. The results confirm that the proposed method is effective in determining vehicle primary motion-modes with comparable accuracy to the MEM method. Experimental data is further used to validate the proposed method.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Mechanical Systems and Signal Processing - Volumes 50–51, January 2015, Pages 632–645
نویسندگان
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