Multi-Objective Optimization of PID Controller for Coupled-Tank Liquid-Level Control System Using Genetic Algorithm

Singh, Sanjay Kumar; Katal, Nitish; Modani, S. G.

doi:10.1007/978-81-322-1602-5_7

Cited by 11 publications

(6 citation statements)

References 7 publications

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“…There are numerous control policies developed for coupled tank systems. Among them, we can mention: Proportional-Integral-Derivative (PID) type controllers [7,8,9], Fuzzy control [10,11], Model Predictive Control [12,13] [20] and Active Disturbance Rejection Control [21].…”

Section: Resumémentioning

confidence: 99%

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Optimal tracking of the water level for a coupled tank system using Linear Quadratic Regulator

TEPPA-GARRAN

2023

Rev. Tekhné

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Se propone un controlador óptimo que resultará en un error de seguimiento asintótico en estado estacionario nulo. Las entradas de referencia pueden incluir escalones, rampas y otras señales persistentes. Para una señal tipo escalón, es bien conocido que se puede lograr un error de seguimiento de estado estacionario cero con un sistema de lazo abierto de tipo uno. Esta idea se formaliza en este trabajo, aumentando el modelo del sistema de tanques acoplados con un modelo interno de la entrada de referencia. Luego, a través del método LQR, los requerimientos de diseño sobre la respuesta temporal son incorporados mediante la minimización de una función cuadrática dependiente de las variables de estado y de la señal de control. Se proporcionan resultados experimentales en el sistema de tanques acoplados para ilustrar la efectividad del método.

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Section: Resumémentioning

confidence: 99%

“…3 Theorem 1: If the state feedback control law ( 12) is applied to the coupled tank system. The error signal (4) for a step reference input 𝑦 𝑟 (𝑡) = 𝑟 will tend asymptotically to zero minimizing the quadratic cost (7) for a given positive semi-definite matrix 𝑸 and parameter 𝜌 > 0.…”

Section: Coupled Tank Systemmentioning

confidence: 99%

Optimal tracking of the water level for a coupled tank system using Linear Quadratic Regulator

TEPPA-GARRAN

2023

Rev. Tekhné

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“…3(b). Tuning of the PID controller with MDG-TLBO algorithm focus on obtaining the optimal solution for the three PID gains Kp, Ki, and Kd by minimizing the objection function [25]. Here, the minimization of the integral square error (ISE) has been carried out.…”

Section: Tuning Of a Pid Controllermentioning

confidence: 99%

Tuning of a PID Controller using Modified Dynamic Group based TLBO Algorithm

Luo¹,

Guo²,

Zhao³

et al. 2017

IJCA

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This paper presents a new version of Teaching LearningBased Optimization (TLBO) algorithm to find the optimal parameters of Proportional Integral Derivative (PID) controller. The proposed algorithm is an altered version of dynamic group strategy TLBO (DGS-TLBO) and is named as modified dynamic group based TLBO (MDG-TLBO) algorithm. The proposed algorithm is tested on 12 benchmark functions to verify its efficiency over other procedures. The results show that the MDG-TLBO algorithm offers better solution quality and has better convergence rate. Finally, the proposed algorithm is tested on a three-tank liquid-level control system for the optimization of PID gains. The simulation result indicate that the proposed algorithm is an effective method in tuning of PID controllers to obtain better performance measures the error values and the time domain specifications.

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“…These types of controllers have adaptive learning and fault tolerance characteristics, while the learning algorithm is one of their disadvantages, which sometimes increases the response time of the system and needs high memory requirements in the implementation on a digital computer. Multiobjective optimization of the PID controller using a genetic algorithm is proposed is used to control the tank level of the coupled tank system (Singh, Katal, & Modani, 2012). The simulation results of the proposed methodology are compared to a general PID controller and the smoothy reference tracking is obtained.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of LMI-Based H2 Control for Vertical Nonlinear Coupled-Tank System

Esmaeili

Başçı

2021

International Journal of System Dynamics Applications

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This paper presents the mathematical design and implementation of a robust H_2 output feedback controller for the vertical nonlinear coupled-tank system. Considering the growth of the complicated chemical processes in industries in the last decades, the necessity for the controllers with high robustness and proficiency is demanded. Therefore, to overcome some deficiencies of classical controllers such as Proportional Integral (PI), the robust H_2 output feedback controller is proposed to control the liquid level of the coupled tank system benchmark. Because of the nonlinearity of the system and the interactions between two tanks, the behavior of the controller in terms of the performance and disturbance rejection is on the main scene. The Linear Matrix Inequalities (LMI) is used to derive the design procedure. The effectiveness of the proposed approach in the setpoint tracking is highlighted in comparison with the PI plus feedforward controller and the acceptable results are achieved.

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Multi-Objective Optimization of PID Controller for Coupled-Tank Liquid-Level Control System Using Genetic Algorithm

Cited by 11 publications

References 7 publications

Optimal tracking of the water level for a coupled tank system using Linear Quadratic Regulator

Optimal tracking of the water level for a coupled tank system using Linear Quadratic Regulator

Tuning of a PID Controller using Modified Dynamic Group based TLBO Algorithm

Design and Implementation of LMI-Based H2 Control for Vertical Nonlinear Coupled-Tank System

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