Level Control in Horizontal Tank by Fuzzy Logic Controller

Tunyasrirut, Satean; Wangnipparnto, Santi

doi:10.1109/sice.2006.314653

Cited by 10 publications

(6 citation statements)

References 6 publications

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“…Dado un ε ∈ [0, 1] definido por el diseñador, tal que 0 < ε ≤ ξ, donde ξ es el punto fijo 10 de la operación lógica "no" (o complemento ( )), cada conjunto difuso A formulado en el paso anterior realiza una partición del universo de entrada X i en la siguiente forma:…”

Section: Codificación Trivalente De Las Regiones De Operaciónunclassified

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Sistema de Inferencia Difusa basado en Relaciones Booleanas y Kleeneanas con Combinador Convexo

Chacón

Soriano

Salazar

2018

ing.

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Context: In the design process of Fuzzy Inference Systems based on Boolean and Kleenean Relations (FIS-BKR) there is a dilemma choosing the regular kleenean extensions of a given boolean function. The set of possible kleenean extensions of a boolean function has a lattice structure under the usual partial order of functions. The fuzzy convex combination proposed by Zadeh guarantees some properties related to this order.Method: The addition of a convex combiner just before the defuzzifier offers a solution to the above situation. The ISE (Integral Squared Error) and ITSE (Integral Time-weighted Squared Error) performance indexes were used on an application for tuning a liquid level control system. Results:The tuning process carried out on the FIS-BKR controller with fuzzy convex combiner using constant coefficients, implied an improvement of the controlled system up to 1.427% for ISE index and up to 21.99% for ITSE with respect to the extreme extensions.Conclusions: New evidence of convenient characteristics of FIS-BKR controllers with fuzzy convex combiner was presented when the performance indexes ISE and ITSE were evaluated. On the other hand, although in this work parameter tuning for convex combination was done by grid search (brute force), it would be interesting to study more effective optimization methods for this purpose.

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Section: Codificación Trivalente De Las Regiones De Operaciónunclassified

“…Esta aplicación fue presentada por Espitia [9] y Salazar [20], y previamente por Lara [12], Hernández [11] y Tunyasrirut [10] donde se propusieron otras soluciones de control empleando lógica difusa.…”

Section: Aplicaciónunclassified

Sistema de Inferencia Difusa basado en Relaciones Booleanas y Kleeneanas con Combinador Convexo

Chacón

Soriano

Salazar

2018

ing.

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“…The result shows that the PID controller performs better compared to the fuzzy logic in terms of percentage overshoot. Tunyasrirut and Wangnipparnto (2007) carried out a study on the Fuzzy-PID cascade controller in controlling the water level of horizontal tank. The results of this study show that the speed of responses of the level control system with and without load interrupt in the tank are fast with the fuzzy logic controller and the PID controller with the smallest state error.…”

Section: Literature Reviewmentioning

confidence: 99%

Performance Comparison of Pid Tuning by Using Ziegler-Nichols and Particle Swarm Optimization Approaches in a Water Control System

Edaris

Abdul-Rahman

2016

JICT

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Tuning the gain of Proportional-Integral-Derivative (PID) controller in a process control system is exceptionally paramount since correct tuning would help a control process response without steady state error and overshoot. As the conventional gain tuning of PID controller, the Ziegler-Nichols (ZN) approach generally delivers an enormous overshoot; therefore, current heuristics approach, namely Particle Swarm Optimization (PSO) is utilized. A tuning problem of a single tank water level dynamic control system is presented. The best PID controller parameters are determined by using the ZN and PSO approaches. Comparisons of process time performance and the performance measurement of the system are made in order to evaluate both approaches in terms of their step response through the MATLAB/Simulink platform. The results demonstrate that the PSO approach produces promising results with lower overshoot compared to the ZN approach. It is found that the PSO approach would be advantageous for the industries related to single tank water control system for a better PID gain tuning.

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“…Model-based control techniques require the mathematical model of the process to control it, and obtaining the exact mathematical model is an ideal case, which imposes a limit to these methods. Nonlinear or linear Fuzzy-PID (Tunyasrirut and Wangnipparnto, 2007; Mahalakshmi and Poornapushpakala, 2015) for the cascaded plant has been implemented earlier, however, design complexity, time-consumption and the online computation is too large to be applied on a full scale in the current distributed control system with several obstacles such as labor-intensive tuning process and uneasy fuzzy logic rules setting, which are highly dependent on the knowledge of a process and its dynamics.…”

Section: Introductionmentioning

confidence: 99%

Active disturbance rejection-based industrial cascade control plant with cloud monitoring

Bhaskarwar¹,

Aole²,

Chile³

2021

JEDT

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Purpose The purpose of this paper is to provide benefits for companies or organizations, which deal with fewer input-outputs and wanted to control their industrial processes remotely with a robust control strategy. Design/methodology/approach In this paper, an active disturbance rejection control (ADRC) strategy is used for the two tank level process plant and it is remotely monitored with the industrial internet of things technology. The disturbances in a primary and secondary loop of the cascade process, which are affecting the overall settling time (ts) of the process, are eliminated by using the proposed, ADRC-ADRC structure in the cascade loop. The stability of the proposed controller is presented with Hurwitz’s stability criteria for selecting gains of observers. The results of the proposed controller are compared with the existing active disturbance rejection control-proportional (ADRC-P) and proportional-integral derivative-proportional (PID-P)-based controller by experimental validation. Findings It is observed that the settling time (ts) in the case of the proposed controller is improved by 60% and 55% in comparison to PID-P and ADRC-P, respectively. The level process is interfaced with an industrial controller and real-time data acquired in matrix laboratory (MATLAB), which acted as a remote monitoring platform for the cascade process. Originality/value The proposed controller is designed to provide robustness against disturbance and parameter uncertainty. This paper provides an alternate way for researchers who are using MATLAB and ThingSpeak cloud server as a tool for the implementation.

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Level Control in Horizontal Tank by Fuzzy Logic Controller

Cited by 10 publications

References 6 publications

Sistema de Inferencia Difusa basado en Relaciones Booleanas y Kleeneanas con Combinador Convexo

Sistema de Inferencia Difusa basado en Relaciones Booleanas y Kleeneanas con Combinador Convexo

Performance Comparison of Pid Tuning by Using Ziegler-Nichols and Particle Swarm Optimization Approaches in a Water Control System

Active disturbance rejection-based industrial cascade control plant with cloud monitoring

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