Neural Model Predictive Control for Industrial Processes
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URL
Journal Title
Journal ISSN
Volume Title
Perustieteiden korkeakoulu |
Master's thesis
Authors
Date
2020-08-18
Department
Major/Subject
Computer Science
Mcode
SCI3042
Degree programme
Master’s Programme in Computer, Communication and Information Sciences
Language
en
Pages
110+1
Series
Abstract
Model Predictive Control can be used to optimize industrial processes such that they maintain high production efficiency whilst staying within their operational constraints. This thesis presents an end-to-end data-driven method for implementing Model Predictive Control on a well-known industrial process control simulator using both Linear and Neural Network Models. Implementing the controller does not require a detailed understanding of the underlying process. Particular methods for estimating the quality of collected data and models are presented and a comparison between Linear and Neural Network Models is made. The process of implementing Model Predictive Control using these two models and gradient based planning is presented, along with methods to address their shortcomings. A series of quantitative benchmarks is presented showing the performance of each model during Model Predictive Control in a variety of different situations and the reasons behind differing performance are discussed.Description
Supervisor
Ilin, AlexanderThesis advisor
Berglund, MathiasKeywords
deep learning, mpc, model predictive control, process control