کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5017261 | 1466386 | 2017 | 36 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Real-time neural inverse optimal control for indoor air temperature and humidity in a direct expansion (DX) air conditioning (A/C) system
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
مهندسی مکانیک
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
A real-time neural inverse optimal control for the simultaneous control of indoor air temperature and humidity using a direct expansion (DX) air conditioning (A/C) system has been developed and the development results are reported in this paper. A recurrent high order neural network (RHONN) was used to identify the plant model of an experimental DX A/C system. Based on this model, a discrete-time inverse optimal control strategy was developed and implemented to an experimental DX A/C system for simultaneously controlling indoor air temperature and humidity. The neural network learning was on-line performed by extended Kalman filtering (EKF). This control scheme was experimentally tested via implementation in real time using an experimental DX A/C system. The obtained results for trajectory tracking illustrated the effectiveness of the proposed control scheme.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: International Journal of Refrigeration - Volume 79, July 2017, Pages 196-206
Journal: International Journal of Refrigeration - Volume 79, July 2017, Pages 196-206
نویسندگان
Flavio Muñoz, Edgar N. Sanchez, Yudong Xia, Shiming Deng,