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Comparison of PI Controller, Model Reference Adaptive Controller and Fuzzy Logic Controller for Coupled Tank System


Affiliations
1 Department of Electronics and Instrumentation Engineering, St. Joseph’s College of Engineering, Chennai – 600119, Tamil Nadu, India
 

Objective: The Coupled Tank System is modeled and its level should be controlled. Methods: The conventional Proportional Integral (PI) controller, Model Reference Adaptive Controller (MRAC) and Fuzzy Logic Controller are used, to control the level of the second tank. The MRAC can alter the controller parameters in response to changes in plant and the reference model indicates properties of the desired control system. The Fuzzy Logic Controller uses a set of rules to control the plant. Findings: The simulation is done, which demonstrates that the MRAC controller delivers better response compared to the PI controller and Fuzzy Logic Controller. Applications: The Model Reference Adaptive controller can be implemented in various level control applications such as conical tank system, spherical tank system, etc.

Keywords

Coupled Tank System, Fuzzy Logic Controller, Model Reference Adaptive Controller (MRAC), PI Controller
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  • Comparison of PI Controller, Model Reference Adaptive Controller and Fuzzy Logic Controller for Coupled Tank System

Abstract Views: 152  |  PDF Views: 0

Authors

P. Gabriel Grace Keerthana
Department of Electronics and Instrumentation Engineering, St. Joseph’s College of Engineering, Chennai – 600119, Tamil Nadu, India
J. Gnanasoundharam
Department of Electronics and Instrumentation Engineering, St. Joseph’s College of Engineering, Chennai – 600119, Tamil Nadu, India

Abstract


Objective: The Coupled Tank System is modeled and its level should be controlled. Methods: The conventional Proportional Integral (PI) controller, Model Reference Adaptive Controller (MRAC) and Fuzzy Logic Controller are used, to control the level of the second tank. The MRAC can alter the controller parameters in response to changes in plant and the reference model indicates properties of the desired control system. The Fuzzy Logic Controller uses a set of rules to control the plant. Findings: The simulation is done, which demonstrates that the MRAC controller delivers better response compared to the PI controller and Fuzzy Logic Controller. Applications: The Model Reference Adaptive controller can be implemented in various level control applications such as conical tank system, spherical tank system, etc.

Keywords


Coupled Tank System, Fuzzy Logic Controller, Model Reference Adaptive Controller (MRAC), PI Controller



DOI: https://doi.org/10.17485/ijst%2F2016%2Fv9i12%2F132199