A Gain-Scheduling PI Control Based on Neural Networks
Joint Authors
Tronci, Stefania
Baratti, Roberto
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-10-19
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
This paper presents a gain-scheduling design technique that relies upon neural models to approximate plant behaviour.
The controller design is based on generic model control (GMC) formalisms and linearization of the neural model of the process.
As a result, a PI controller action is obtained, where the gain depends on the state of the system and is adapted instantaneously on-line.
The algorithm is tested on a nonisothermal continuous stirred tank reactor (CSTR), considering both single-input single-output (SISO) and multi-input multi-output (MIMO) control problems.
Simulation results show that the proposed controller provides satisfactory performance during set-point changes and disturbance rejection.
American Psychological Association (APA)
Tronci, Stefania& Baratti, Roberto. 2017. A Gain-Scheduling PI Control Based on Neural Networks. Complexity،Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1143656
Modern Language Association (MLA)
Tronci, Stefania& Baratti, Roberto. A Gain-Scheduling PI Control Based on Neural Networks. Complexity No. 2017 (2017), pp.1-8.
https://search.emarefa.net/detail/BIM-1143656
American Medical Association (AMA)
Tronci, Stefania& Baratti, Roberto. A Gain-Scheduling PI Control Based on Neural Networks. Complexity. 2017. Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1143656
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1143656