کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
171997 458512 2016 16 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Process control using finite Markov chains with iterative clustering
ترجمه فارسی عنوان
کنترل فرآیند با استفاده از زنجیره های مارکوف محدود با خوشه بندی تکراری
کلمات کلیدی
پروسه تصمیم گیری مارکوف؛ تخمین حالت بیزی؛ کنترل فرآیند مبتنی بر مدل؛ کنترل پیش بینی مدل؛ خوشه بندی شبیه سازی فرآیند
موضوعات مرتبط
مهندسی و علوم پایه مهندسی شیمی مهندسی شیمی (عمومی)
چکیده انگلیسی


• Finite state MDP provide a viable alternative for process control.
• An iterative re-discretization is proposed based on clustering of closed-loop data.
• An efficient hierarchical modification of the k-means clustering is proposed.
• CSTR process control benchmark requirements are met by the suggested control design.

Finite state Markov Decision Processes (MDP) for process control are considered. MDP provide robust tools to perform optimization in closed-loop, and their finite state description enables an easy implementation of Bayesian state estimation. An approach to tackle the curse of dimensionality problem, yet retaining the benefits of the finite state MDP in control and estimator design, is proposed. The suggested approach uses iterative re-discretization based on clustering of closed-loop data. An efficient modification of the k-means clustering technique is proposed. The performance of the approach is demonstrated using a challenging benchmark from chemical engineering, the van der Vusse continuous stirred tank reactor control problem. It is shown that the requirements of the benchmark are met, and that the suggested iterated clustering significantly improves the performance. It is concluded that the finite state MDP approach is a viable alternative for small-to-medium scale problems of practical process control and state estimation.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers & Chemical Engineering - Volume 93, 4 October 2016, Pages 293–308
نویسندگان
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