کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
412068 679608 2015 12 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A robust safety-oriented autonomous cruise control scheme for electric vehicles based on model predictive control and online sequential extreme learning machine with a hyper-level fault tolerance-based supervisor
ترجمه فارسی عنوان
یک سیستم کنترل کروز کنترل کروز مستقل برای سیستم های برقی مبتنی بر کنترل پیش بینی مدل و دستگاه تعلیق متوالی آنلاین با راهنمایی مبتنی بر تحمل گسل سطح بالا
کلمات کلیدی
وسایل نقلیه الکتریکی، کنترل اتوماتیک کروز، کنترل پیش بینی مدل، دستگاه یادگیری افراطی آنلاین، سرپرست مبتنی بر برابری خطا در سطح بالا
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی

Highights
• A novel integrated intelligent method based on ELM is proposed for autonomous cruise control of RAV4 EV.
• Based on numerical experiment, the power of OSELM for real-time identification of the state of RAV4 EV is verified.
• The potentials of OSELM to be fused with a hyper-level fault tolerance-based supervisor is evaluated.
• It is demonstrated that linearized OSELM can be used at the heart of MPC and can outperform several well-known cruise controllers.

In this investigation, an advanced modeling method, called online sequential extreme learning machine with a hyper-level fault tolerance-based supervisor (OSELM–FTS), is utilized to develop a robust safety-oriented autonomous cruise control based on the model predictive control (MPC) technique. The resulting MPC-based cruise controller is used to improve the driving safety and reduce the energy consumption of an electric vehicle (EV). The structural flexibility of OSELM–FTS allows us to not only improve the operating features of the EV, but also develop an intelligent supervisor which can detect any operating fault and send proper commands for the adaption of the MPC controller. This introduces a degree of robustness to the devised controller, as OSELM–FTS automatically detects and filters any operating faults which may undermine the performance of the MPC controller. To ascertain the veracity of the devised controller, three well-known MPC formulations, i.e. linear MPC (LMPC) and nonlinear MPC (NMPC) and diagonal recurrent neural network MPC (DRNN-MPC), are applied to the baseline EV and their performances are compared with OSELM–FTS-MPC. To further elaborate on the computational advantages of OSELM, a well-known chunk-by-chunk incremental machine learning approach, namely selective negative correlation learning (SNCL), is taken into account. The results of the comparative study indicate that OSELM–FTS-MPC is a very promising control scheme and can be reliably used for safety-oriented autonomous cruise control of the EVs.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 151, Part 2, 5 March 2015, Pages 845–856
نویسندگان
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