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Register a new userBuilding a feedforward neural network optimizer in Model Predictive Control Algorithm
بناء مؤمثل عصبوني أمامي لخوارزمية التحكم التنبؤي النموذجي
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يَعرض هذا البحث إمكانية الاستعاضة عن المؤمثل الرياضي في خوازمية التحكم التنبؤي بمؤمثل عصبوني أمامي (Feedforward Neural Network Optimizer: FNNO) و من تم تدريبه بشكل مسيق offline لتصغير تابع الكلفة. حافظنا بهذه الطريقة على نموذج النظام الذي يعد أساساً في خوارزمية التحكم التنبؤي للحصول على الدقة المطلوبة. و تم حل مسألة الأمثلة خلال زمن أسرع من زمن حلها عند استخدام خوارزميات الأمثلة التقليدية المعتمدة على الحوسبة الرقمية.
This paper presents the possibility of replacing the mathematical optimizer in the Model Predictive Control Algorithm (MPC) with a Feedforward Neural Network Optimizer (FNNO). The optimizer trained offline to reduce the cost function. This maintain the system model of the system, which is essential in MPC to get accepted accuracy. we solve optimization problem faster than the algorithms of traditional optimization, which we built, based on digital computing.
References used
Bernt M. A ˚ kesson, Hannu T. Toivonen,2006- " A Neural Network Model Predictive Controller" Journal of Process Control 16, 937–946
CAMACHO,E,2007- " Model Predictive Control. Springer, Second Edition," New York
Yunpeng Pan and Jun Wang,2008-" Two Neural Network Approaches to Model Predictive Control", American Control Conference, WeC13.5
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