Fuzzy-Optimized model reference adaptive control of interacting and noninteracting processes based on MIT and Lyapunov rules

Year 2021, Volume: 5 Issue: 4, 141 - 153, 01.10.2021

Demilade Dınakın , Peter Oluseyi

https://doi.org/10.31127/tuje.668840

Cited By: 4

Abstract

Various system parameter variations occur during operations in several existing process industries. These parameter variations result in process shifts, thus, requiring adequate control strategies to compensate for these alterations, which consequently maintain desired system response. A paradigm is the coupled tank systems; in such systems, the level and flow of liquid must be adequately controlled to maintain the reaction equilibrium as well as to avoid spillage or equipment damage. The model reference adaptive control (MRAC) is an adaptive control strategy that creates a control law, subject to an adaptation gain, which causes the system’s plant to continuously track a reference model until a zero tracking error is achieved. The Massachusetts Institute of Technology (MIT) and Lyapunov approaches were used to develop the adaptation mechanism, which is used to adjust the parameters in the control law. Conventionally, a fixed value is adopted as the adaptation gain; however, the adaption gain can also be determined heuristically. The fuzzy logic control was used to optimally determine the value of the adaptation gain, which thus results in the fuzzy-optimized MRAC (FOMRAC) system. Consequently, these schemes were comparatively analysed for the control of the flow and level of liquid in coupled two-tank systems, arranged in noninteracting and interacting fashions. Using MATLAB/Simulink, results depicted that the FOMRAC systems had faster settling times in comparison with the fixed adaptation gain MRAC systems. Overall, the FOMRAC system based on Lyapunov rule yielded the lowest performance indices values. In addition, the scheme completely eliminated the overshoot that resulted from the implementation of the other schemes for the control of the interacting process.

Keywords

Adaptation Gain, Fuzzy Logic Control (FLC), Lyapunov Rule, MIT Rule, Model Reference Adaptive Control (MRAC), Adaptive Control

Supporting Institution

National Information Technology Development Agency

Project Number

NITDA/HQ/DG/135/2005/VOL.2

Thanks

The authors sincerely appreciate the National Information Technology Development Agency (NITDA) for the research grant provided to carry out this work through the National Information Technology Development Fund (NITDEF)

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