Machine-Learning-Based Improved Smith Predictive Control for MIMO Processes

Xinlan Guo (), Mohammadamin Shirkhani () and Emad M. Ahmed
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Xinlan Guo: College of Rail Transportation, Nanjing Vocational Institute of Transport Technology, Nanjing 211188, China
Mohammadamin Shirkhani: Department of Electrical Engineering, Ilam University, Ilam 69315-516, Iran
Emad M. Ahmed: Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia

Mathematics, 2022, vol. 10, issue 19, 1-19

Abstract: Controlling time-delayed processes is one of the challenges in today’s process industries. If the multi-input/multi-output system is dynamically coupled, the delay problem becomes more critical. In this paper, a new method based on Smith’s predictive method, with the help of a type-2 fuzzy system to control the system with the mentioned features, is presented. The variability in the time delay, the existence of disturbances and the existence of structural and parametric uncertainty lead to the poor performance of the traditional Smith predictor. Even if the control system is set up correctly at the beginning of the setup, it will eventually wear out, and the above problems will appear. Therefore, computational intelligence is used here, and by updating the parameters of the control system at the same time as the system changes, the control system adapts itself to achieve the best performance. To evaluate the proposed control system, a complex process system is simulated, the results of which show the good performance of Smith’s prediction method based on a type-2 fuzzy system.

Keywords: machine learning; parameter uncertainty; Smith predictive; MIMO control (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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