Optimal Design of PID Controller for the analysis of Two TANK System Using Metaheuristic Optimization Algorithm
Two surge interactive and non-interactive tank systems are taken as examples of multi-level tank system. Due to the dynamic level changes of the two-tank system, various control techniques are intended to regulate the level by controlling the liquid inflow quantity. In addition to that, disturbance...
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| Published in | Journal of electrical engineering & technology Vol. 17; no. 1; pp. 627 - 640 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Singapore
Springer Singapore
01.01.2022
대한전기학회 |
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| Online Access | Get full text |
| ISSN | 1975-0102 2093-7423 |
| DOI | 10.1007/s42835-021-00891-6 |
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| Abstract | Two surge interactive and non-interactive tank systems are taken as examples of multi-level tank system. Due to the dynamic level changes of the two-tank system, various control techniques are intended to regulate the level by controlling the liquid inflow quantity. In addition to that, disturbance effect is considered to get better step response for the tuning of Proportional Integral Derivative (PID) controller by using meta-heuristic algorithm. In this paper, Proportional Integral Derivative (PID) controller design analysis is carried out by using Feed Forward (FF) control, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Bubble Net Whale Optimization Algorithms (BNWOA). BNWOA is used to tune the PID controller to reduce constrains of two tank system and obtain the optimal control is proposed. The transfer function of the two-tank system with step input for various control algorithms such as GA, PSO and BNWOA are observed using MATLAB Simulink and M-script. From the analysis, better performance such as reduced constrains and optimal control can be obtained from BNWOA. Then steady state analysis is made and the simulation results are presented at the end. |
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| AbstractList | Two surge interactive and non-interactive tank systems are taken as examples of multi-level tank system. Due to the dynamic level changes of the two-tank system, various control techniques are intended to regulate the level by controlling the liquid inflow quantity. In addition to that, disturbance effect is considered to get better step response for the tuning of Proportional Integral Derivative (PID) controller by using meta-heuristic algorithm. In this paper, Proportional Integral Derivative (PID) controller design analysis is carried out by using Feed Forward (FF) control, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Bubble Net Whale Optimization Algorithms (BNWOA). BNWOA is used to tune the PID controller to reduce constrains of two tank system and obtain the optimal control is proposed. The transfer function of the two-tank system with step input for various control algorithms such as GA, PSO and BNWOA are observed using MATLAB Simulink and M-script. From the analysis, better performance such as reduced constrains and optimal control can be obtained from BNWOA. Then steady state analysis is made and the simulation results are presented at the end. Two surge interactive and non-interactive tank systems are taken as examples of multi-level tank system. Due to the dynamic level changes of the two-tank system, various control techniques are intended to regulate the level by controlling the liquid infl ow quantity. In addition to that, disturbance eff ect is considered to get better step response for the tuning of Proportional Integral Derivative (PID) controller by using meta-heuristic algorithm. In this paper, Proportional Integral Derivative (PID) controller design analysis is carried out by using Feed Forward (FF) control, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Bubble Net Whale Optimization Algorithms (BNWOA). BNWOA is used to tune the PID controller to reduce constrains of two tank system and obtain the optimal control is proposed. The transfer function of the two-tank system with step input for various control algorithms such as GA, PSO and BNWOA are observed using MATLAB Simulink and M-script. From the analysis, better performance such as reduced constrains and optimal control can be obtained from BNWOA. Then steady state analysis is made and the simulation results are presented at the end. KCI Citation Count: 0 |
| Author | Amuthambigaiyin Sundari, K. Maruthupandi, P. |
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| Cites_doi | 10.1109/AQTR.2016.7501360 10.1109/PC.2013.6581410 10.1016/j.asej.2019.07.004 10.1109/ICCCEEE46830.2019.9070896 10.1109/ICACCCT.2012.6320799 10.1049/iet-smt.2017.0015 10.31254/jsir.2016.5207 10.1109/IHMSC.2012.180 10.1051/matecconf/201713900157 10.1109/I4Tech48345.2020.9102644 10.1109/ICSCC.2019.8843624 10.1109/IPACT.2017.8244929 10.1109/ICIIECS.2015.7193213 10.1109/ICCRE.2016.7476135 10.1109/ICACCCT.2014.7019439 |
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| Keywords | PID controller Interactive and non-interactive tank Meta-heuristic algorithm Bubble net whale optimisation algorithm (BNWOA) |
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| References | Zhong1 M, Long W (2017) Whale optimization algorithm with nonlinear control parameter. In:MATEC web of conference Srinivasan K, Sindhiya D (2016) Design of fuzzy based model predictive controller for conical tank system. In: IEEE International conference on control and robotics engineering (ICCRE). https://doi.org/10.1109/ICCRE.2016.7476135 DavisIJishnuCPWilliamRLevel control of two conical tank non-interacting system using PID and fuzzy logicInt J Innov Res Electr Electron, Instrum Control Eng2017543542 UvaisMSarkarPRThree tank interacting system level control using modern AI techniquesInt J Res Dev Appl Sci Eng20169115 NagammaiSLathaSDesign of optimal controllers for a three tank processJ Sci Innov Res2016526570 Singh AK, Kumar S (2014) Comparing the performance analysis of three tank level control system using feedback and feedforward -feedback configuration. In: IEEE International conference on advanced communication control and computing technologies. https://doi.org/10.1109/ICACCCT.2014.7019439 RaniLTDeepaNArulselviSModeling and intelligent control of two-tank interacting level processInt J Recent Technol Eng (IJRTE)2014313036 Ravi R, Thyagarajan T, Puviyarasi B (2012) Centralised neuro controller for two conical tank interacting level system. In: IEEE International conference on advanced communication control and computing technologies. https://doi.org/10.1109/ICACCCT.2012.6320799 MosaadaAMAttiaaMAAbdelazizAYWhale optimization algorithm to tune PID and PIDA controllers on AVR systemAin Shams Eng J201910475576710.1016/j.asej.2019.07.004 Mukherjee A, Chakraborty N, Das BK (2017) Whale optimization algorithm: an implementation to design low-pass FIR Filter. In: Innovations in power and advanced computing technologies (i-PACT). https://doi.org/10.1109/IPACT.2017.8244929 MedewarPGSonawaneRRMunjeRKTwo tank non-interacting liquid level control comparison using fuzzy and PSO controllerIOSR J Electri Electron Eng201712431 Abdalla SA, Mohamed AIM, Ali EA, Nawari MO (2019) Level control of horizontal cylindrical tank. In: IEEE International conference on computer, control, electrical, and electronics engineering, international conference on computer, control. https://doi.org/10.1109/ICCCEEE46830.2019.9070896 Maxim A, Ionescu C, De Keyser R (2016) Modelling and identification of a coupled sextuple water tank system. In: IEEE International conference on automation, quality and testing, robotics (AQTR). https://doi.org/10.1109/AQTR.2016.7501360 Lakshmanaprabu SK, Banu US, Karthik D (2015) Real-time implementation of multi-loop internal model controller for two interacting conical tank process. In: IEEE International conference on innovations in information, embedded and communication systems. https://doi.org/10.1109/ICIIECS.2015.7193213 Zhou K, Yan B, Jiang Y, Huang J (2012) Double-tank liquid level control based on genetic algorithm. In: International conference on intelligent human-machine systems and cybernetics, 4th IEEE International conference on intelligent human-machine systems and cybernetics, pp. 354–357.https://doi.org/10.1109/IHMSC.2012.180 PrasadDMukherjeeAShankarGMukherjeeVApplication of chaotic whale optimisation algorithm for transient stability constrained optimal power flowIET Sci Meas Technol20171181002101310.1049/iet-smt.2017.0015 Vasičkaninová A, Bakošová M, Karšaiová M, Kmeťová J (2013) Methods for controller tuning for unstable systems. In: IEEE International conference on process control (PC), pp. 208–213. https://doi.org/10.1109/PC.2013.6581410 LeBlancSECoughanowrDRProcess systems analysis and control20093New YorkMcGraw Hill publishers Wong WK, Ming CI (2019) A review on metaheuristic algorithms: recent trends, benchmarking and applications. In: 7th International conference on smart computing & communications (ICSCC).https://doi.org/10.1109/ICSCC.2019.8843624 Shah P, Hanwate S (2020) Modelling and simulation of quadruple tank system using SBL-PI controller. In: International conference on industry 4.0 technology.https://doi.org/10.1109/I4Tech48345.2020.9102644 Kumar AA, Kumar SG (2018) Application of whale optimization algorithm for tuning of a PID controller for a drilling machine. In: Second international conference on advancements in automation, robotics and sensing (ICAARS).https://hal.archives-ouvertes.fr/hal-02314429/document 891_CR20 AM Mosaada (891_CR21) 2019; 10 891_CR11 891_CR8 S Nagammai (891_CR10) 2016; 5 891_CR7 891_CR6 891_CR5 891_CR1 M Uvais (891_CR9) 2016; 9 SE LeBlanc (891_CR12) 2009 D Prasad (891_CR19) 2017; 11 LT Rani (891_CR2) 2014; 3 I Davis (891_CR4) 2017; 5 891_CR17 891_CR18 PG Medewar (891_CR3) 2017; 1 891_CR13 891_CR14 891_CR15 891_CR16 |
| References_xml | – reference: MosaadaAMAttiaaMAAbdelazizAYWhale optimization algorithm to tune PID and PIDA controllers on AVR systemAin Shams Eng J201910475576710.1016/j.asej.2019.07.004 – reference: DavisIJishnuCPWilliamRLevel control of two conical tank non-interacting system using PID and fuzzy logicInt J Innov Res Electr Electron, Instrum Control Eng2017543542 – reference: Zhong1 M, Long W (2017) Whale optimization algorithm with nonlinear control parameter. In:MATEC web of conference – reference: Kumar AA, Kumar SG (2018) Application of whale optimization algorithm for tuning of a PID controller for a drilling machine. In: Second international conference on advancements in automation, robotics and sensing (ICAARS).https://hal.archives-ouvertes.fr/hal-02314429/document – reference: Wong WK, Ming CI (2019) A review on metaheuristic algorithms: recent trends, benchmarking and applications. In: 7th International conference on smart computing & communications (ICSCC).https://doi.org/10.1109/ICSCC.2019.8843624 – reference: PrasadDMukherjeeAShankarGMukherjeeVApplication of chaotic whale optimisation algorithm for transient stability constrained optimal power flowIET Sci Meas Technol20171181002101310.1049/iet-smt.2017.0015 – reference: Lakshmanaprabu SK, Banu US, Karthik D (2015) Real-time implementation of multi-loop internal model controller for two interacting conical tank process. In: IEEE International conference on innovations in information, embedded and communication systems. https://doi.org/10.1109/ICIIECS.2015.7193213 – reference: Vasičkaninová A, Bakošová M, Karšaiová M, Kmeťová J (2013) Methods for controller tuning for unstable systems. In: IEEE International conference on process control (PC), pp. 208–213. https://doi.org/10.1109/PC.2013.6581410 – reference: UvaisMSarkarPRThree tank interacting system level control using modern AI techniquesInt J Res Dev Appl Sci Eng20169115 – reference: MedewarPGSonawaneRRMunjeRKTwo tank non-interacting liquid level control comparison using fuzzy and PSO controllerIOSR J Electri Electron Eng201712431 – reference: Maxim A, Ionescu C, De Keyser R (2016) Modelling and identification of a coupled sextuple water tank system. In: IEEE International conference on automation, quality and testing, robotics (AQTR). https://doi.org/10.1109/AQTR.2016.7501360 – reference: Srinivasan K, Sindhiya D (2016) Design of fuzzy based model predictive controller for conical tank system. In: IEEE International conference on control and robotics engineering (ICCRE). https://doi.org/10.1109/ICCRE.2016.7476135 – reference: RaniLTDeepaNArulselviSModeling and intelligent control of two-tank interacting level processInt J Recent Technol Eng (IJRTE)2014313036 – reference: NagammaiSLathaSDesign of optimal controllers for a three tank processJ Sci Innov Res2016526570 – reference: Shah P, Hanwate S (2020) Modelling and simulation of quadruple tank system using SBL-PI controller. In: International conference on industry 4.0 technology.https://doi.org/10.1109/I4Tech48345.2020.9102644 – reference: Zhou K, Yan B, Jiang Y, Huang J (2012) Double-tank liquid level control based on genetic algorithm. In: International conference on intelligent human-machine systems and cybernetics, 4th IEEE International conference on intelligent human-machine systems and cybernetics, pp. 354–357.https://doi.org/10.1109/IHMSC.2012.180 – reference: Mukherjee A, Chakraborty N, Das BK (2017) Whale optimization algorithm: an implementation to design low-pass FIR Filter. In: Innovations in power and advanced computing technologies (i-PACT). https://doi.org/10.1109/IPACT.2017.8244929 – reference: LeBlancSECoughanowrDRProcess systems analysis and control20093New YorkMcGraw Hill publishers – reference: Abdalla SA, Mohamed AIM, Ali EA, Nawari MO (2019) Level control of horizontal cylindrical tank. In: IEEE International conference on computer, control, electrical, and electronics engineering, international conference on computer, control. https://doi.org/10.1109/ICCCEEE46830.2019.9070896 – reference: Singh AK, Kumar S (2014) Comparing the performance analysis of three tank level control system using feedback and feedforward -feedback configuration. In: IEEE International conference on advanced communication control and computing technologies. https://doi.org/10.1109/ICACCCT.2014.7019439 – reference: Ravi R, Thyagarajan T, Puviyarasi B (2012) Centralised neuro controller for two conical tank interacting level system. 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| Title | Optimal Design of PID Controller for the analysis of Two TANK System Using Metaheuristic Optimization Algorithm |
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