Lyapunov‐based adaptive state‐space controller for liquid level control of a coupled tank system with unknown model

Abstract In this paper, a Lyapunov‐based adaptive state space controller is designed to realize liquid level control in a coupled tank system. The proposed controller is a kind of adaptive control system that combines the principles of state space representation with Lyapunov stability theory to estimate unknown parameters and control a dynamic system. This approach ensures that the system remains stable even when the exact model parameters are unknown or change over time. Moreover, in order to test the performance of the proposed controller, proportional integral (PI) + feedforward (FF) controller is also applied to the same system under the same conditions and the results are compared. The results show that the proposed controller outperforms the PI + FF controller in terms of system response parameters such as rise time, percent overshoot, and settling time. On the other hand, when the error performance metrics integral squared error (ISE), integral time‐weighted squared error (ITSE), integral time‐weighted absolute error (ITAE), and integral absolute error (IAE) are evaluated, it is seen that the same situation is also realized here, and the proposed controller follows the reference signals with lower error values in real‐time studies for different reference signals.

Authors

Institutions

Publication Details

Journal
The Canadian Journal of Chemical Engineering
Published
2025-03-05
DOI
https://doi.org/10.1002/cjce.25659
Primary Topic
Advanced Control Systems Optimization
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Lyapunov‐based adaptive state‐space controller for liquid level control of a coupled tank system with unknown model

Hayriye Tuğba Sekban, Abdullah Başçi, Armin Lotfieghlim
The Canadian Journal of Chemical Engineering
Advanced Control Systems Optimization
article

Lyapunov‐based adaptive state‐space controller for liquid level control of a coupled tank system with unknown model

Hayriye Tuğba Sekban, Abdullah Başçi, Armin Lotfieghlim
article en

Abstract

Abstract In this paper, a Lyapunov‐based adaptive state space controller is designed to realize liquid level control in a coupled tank system. The proposed controller is a kind of adaptive control system that combines the principles of state space representation with Lyapunov stability theory to estimate unknown parameters and control a dynamic system. This approach ensures that the system remains stable even when the exact model parameters are unknown or change over time. Moreover, in order to test the performance of the proposed controller, proportional integral (PI) + feedforward (FF) controller is also applied to the same system under the same conditions and the results are compared. The results show that the proposed controller outperforms the PI + FF controller in terms of system response parameters such as rise time, percent overshoot, and settling time. On the other hand, when the error performance metrics integral squared error (ISE), integral time‐weighted squared error (ITSE), integral time‐weighted absolute error (ITAE), and integral absolute error (IAE) are evaluated, it is seen that the same situation is also realized here, and the proposed controller follows the reference signals with lower error values in real‐time studies for different reference signals.

The Canadian Journal of Chemical EngineeringVol. 103(10)
Bayburt University (TR), Atatürk University (TR)
Openalex Percentile: Top 96%
Advanced Control Systems Optimization
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.