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A Model Reference Adaptive Controller based Flamingo Search Algorithm for Liquid Level Control in Non-Linear Conical Tank System Cover

A Model Reference Adaptive Controller based Flamingo Search Algorithm for Liquid Level Control in Non-Linear Conical Tank System

Open Access
|Jun 2025

Figures & Tables

Fig. 1.

Structure of a CT.
Structure of a CT.

Fig. 2.

Block diagram of MRAC.
Block diagram of MRAC.

Fig. 3.

Proposed MRAC with D-PI.
Proposed MRAC with D-PI.

Fig. 4.

Simulink model of MRAC with CT.
Simulink model of MRAC with CT.

Fig. 5.

Controller output under constant set point.
Controller output under constant set point.

Fig. 6.

Performance analysis under random disturbance in constant setpoint.
Performance analysis under random disturbance in constant setpoint.

Fig. 7.

Performance analysis under load variations.
Performance analysis under load variations.

Fig. 8.

Controller output under variation in reference from 30 cm to 46 cm.
Controller output under variation in reference from 30 cm to 46 cm.

Fig. 9.

Controller response under varying liquid levels.
Controller response under varying liquid levels.

Fig. 10.

Convergence plot for optimization algorithms.
Convergence plot for optimization algorithms.

Fig. 11.

Comparison of control signals for different controllers.
Comparison of control signals for different controllers.

Control energy and peaks for different controllers_

ControllerControl energy [J]Peak value
PI41.1781.6136
PID501.5405
FOPI28.9541.2464
MRAC-PID27.6781.231
MRAC (Proposed)20.8321.002

Comparative analysis of different controllers_

ControllersPeak overshoot [%]Settling time [s]Rise time [s]ISEIAE
PI18.1740210947.732681
PID21.3242111451.032968
FOPI12.7440815743.092902
MRAC-PID7.1939117441.352863
MRAC (Proposed)0.820010338.252167

Literature review_

AuthorMethodOutcomeAdvantagesDrawbacks
Arun and Sahaya Aarti [21]MPCRise time is 262 s, overshoot is 9.3 %Lower peak and settling timeOvershoot is higher in the second tank
Kumar et al. [22]IMC-PIDITAE is 0.0661, overshoot is 7.611·10-4It can be applied to the conical transfer functionPoor robustness
Rajiv Ranjan [23]MRACSettling time is 30 sSystem performance is stable under unmodelled dynamicLonger settling time
Espitia-Cuchango et al. [24]NFACDesired response is obtained under varying system conditionsSuitable for multi-input and multi-output systemsMore settling time
Aguila-Camacho et al. [25]FOPIITAE is 1.4465·106Lower varianceMore time to settle
Balaska et al. [29]FO-MRACSampling period is 0.1 sLowest error costIncreases the noise
Patil and Agashe [30]DRLDesired response is obtained under varying system conditionsReduces the complexity and non-linearityHigher rise time
Ramanathan et al. [31]Reinforcement learning algorithmSettling time for trial 1 is 452 sReduces the non-linearity issues and settling timeLearning process slow

Comparative analysis of PID controller with different optimization techniques_

ControllersPeak overshoot [%]Settling time [s]Rise time [s]ISEIAE
PID21.3242111451.032968
PID with GA10.5237613144.172888
PID with PSO5.4430512240.932652
MRAC (Proposed)0.820010338.252167
Language: English
Page range: 100 - 109
Submitted on: Dec 26, 2023
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Accepted on: Jan 22, 2025
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Published on: Jun 17, 2025
In partnership with: Paradigm Publishing Services
Publication frequency: Volume open

© 2025 S. P. Selvaraj, R. Thiyagarajan, T Rajavenkatesan, published by Slovak Academy of Sciences, Institute of Measurement Science
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.