Multivariable Control and Object-Oriented Modeling

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control and Monitoring".

Deadline for manuscript submissions: closed (30 September 2019) | Viewed by 4017

Special Issue Editors


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Guest Editor
Department of Electrical Engineering and Automatic Control, University of Cordoba, Campus de Rabanales, 14071 Cordoba, Spain
Interests: multivariable control; PID controllers; object-oriented modeling and process control

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Guest Editor
Department of Mechanical Engineering, University of Cordoba, Campus of Rabanales, Leonardo da Vinci Building, Cordoba, Spain
Interests: wireless control; control of HVAC systems; object-oriented modeling and process control

Special Issue Information

Dear Colleagues,

Most natural or artificial processes are complex systems that involve different domains of nature and multiple interrelated variables. This makes their modeling and control a difficult task. Object-oriented modeling and simulation is one of the main tools to analyze and assess the behavior of such complex systems. Object-oriented modeling languages ​​allow multidomain modeling: That is, the components corresponding to physical objects of different domains can be described and connected as in many real applications involving mechanical, hydraulic, thermal elements, etc. These tools are widely used to assist the design, control, and improvement of several types of systems. On the other hand, when it is desired to control such complex processes, complicated interactions often appear between the variables to be controlled and the variables to be manipulated. This makes the traditional control system design by means of monovariable PID controllers more difficult or inefficient. In these cases, it is convenient to consider multivariable control methodologies.

This Special Issue on “Multivariable Control and Object-Oriented Modeling” focuses on new developments and applications of object-oriented modeling of multivariable complex systems and multivariable control methodologies. Topics include but are not limited to:

  • Developments of object-oriented modeling;
  • Applications of object-oriented modeling;
  • Development of multivariable control methodologies;
  • Analysis of interaction of multivariable processes;
  • Application of multivariable control strategies, such as multivariable PID control, decoupling control, multivariable MPC, decentralized control, multivariable robust control, and so on.

Prof. Dr. Juan Garrido Jurado
Prof. Dr. Mario Luis Ruz Ruiz
Guest Editors

Manuscript Submission Information

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Keywords

  • object-oriented modeling
  • multivariable control
  • decoupling control
  • process control
  • interaction measures

Published Papers (1 paper)

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Research

19 pages, 3520 KiB  
Article
Multivariable System Identification Method Based on Continuous Action Reinforcement Learning Automata
by Meiying Jiang and Qibing Jin
Processes 2019, 7(8), 546; https://0-doi-org.brum.beds.ac.uk/10.3390/pr7080546 - 17 Aug 2019
Cited by 5 | Viewed by 3587
Abstract
In this work, a closed-loop identification method based on a reinforcement learning algorithm is proposed for multiple-input multiple-output (MIMO) systems. This method could be an attractive alternative solution to the problem that the current frequency-domain identification algorithms are usually dependent on the attenuation [...] Read more.
In this work, a closed-loop identification method based on a reinforcement learning algorithm is proposed for multiple-input multiple-output (MIMO) systems. This method could be an attractive alternative solution to the problem that the current frequency-domain identification algorithms are usually dependent on the attenuation factor. With this method, after continuously interacting with the environment, the optimal attenuation factor can be identified by the continuous action reinforcement learning automata (CARLA), and then the corresponding parameters could be estimated in the end. Moreover, the proposed method could be applied to time-varying systems online due to its online learning ability. The simulation results suggest that the presented approach can meet the requirement of identification accuracy in both square and non-square systems. Full article
(This article belongs to the Special Issue Multivariable Control and Object-Oriented Modeling)
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