Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/54608
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Type: Conference paper
Title: Double-command fuzzy control of a nonlinear CSTR
Author: Mohammadzaheri, M.
Chen, L.
Citation: Proceedings of the 3rd IEEE Conference on Industrial Electronics and Applications, 2008;. pp.659-664
Publisher: IEEE
Publisher Place: USA
Issue Date: 2008
Series/Report no.: IEEE Conference on Industrial Electronics and Applications
ISBN: 9781424417186
ISSN: 2156-2318
Conference Name: IEEE Conference on Industrial Electronics and Applications (3rd : 2008 : Singapore)
Statement of
Responsibility: 
Mohammadzaheri, M. and Lei Chen
Abstract: In this research, double-command control of a nonlinear chemical system is addressed. The system includes a stirred tank; two flows of liquid with different concentrations are entering the system through two valves and another flow is exiting the tank with a concentration between two input concentrations. The outlet concentration is usually regulated by the control of one of the valves and the flow rate of the second valve is fixed. The authors have already accomplished the single-command control of this system using neuro-predictive technique. Although, this problem is known as a good example for neuro-predicitve control, but this technique was found both ineffective (in terms of offering improper performance) and inefficient (in terms of needing heavy computations) when it is tried for double-command control (the control of both valves). Therefore, a fuzzy controller is designed to double-command control this system in the simulation environment. In order to avoid output chattering and frequent change of control command (leading frequent closing-opening of control valves, in practice) a damper rule is added to the fuzzy control system. A steady state control law is also derived from nonlinear mathematical model of the system to be added to transient control command whose increments are generated by fuzzy controller. The hybrid control system leads a very smooth change of control input which is suitable for real applications. The designed control systems offer lower error integral, control command change and processing time in comparison to single-command neuro-predictive controllers.
DOI: 10.1109/ICIEA.2008.4582597
Appears in Collections:Aurora harvest 5
Mechanical Engineering conference papers

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