Fuzzy-Optimized model reference adaptive control of interacting and noninteracting processes based on MIT and Lyapunov rules
Yıl 2021,
Cilt: 5 Sayı: 4, 141 - 153, 01.10.2021
Demilade Dınakın
,
Peter Oluseyi
Öz
Various system parameter variations occur during operations in several existing process industries. These parameter variations result in process shifts, thus, requiring adequate control strategies to compensate for these alterations, which consequently maintain desired system response. A paradigm is the coupled tank systems; in such systems, the level and flow of liquid must be adequately controlled to maintain the reaction equilibrium as well as to avoid spillage or equipment damage. The model reference adaptive control (MRAC) is an adaptive control strategy that creates a control law, subject to an adaptation gain, which causes the system’s plant to continuously track a reference model until a zero tracking error is achieved. The Massachusetts Institute of Technology (MIT) and Lyapunov approaches were used to develop the adaptation mechanism, which is used to adjust the parameters in the control law. Conventionally, a fixed value is adopted as the adaptation gain; however, the adaption gain can also be determined heuristically. The fuzzy logic control was used to optimally determine the value of the adaptation gain, which thus results in the fuzzy-optimized MRAC (FOMRAC) system. Consequently, these schemes were comparatively analysed for the control of the flow and level of liquid in coupled two-tank systems, arranged in noninteracting and interacting fashions. Using MATLAB/Simulink, results depicted that the FOMRAC systems had faster settling times in comparison with the fixed adaptation gain MRAC systems. Overall, the FOMRAC system based on Lyapunov rule yielded the lowest performance indices values. In addition, the scheme completely eliminated the overshoot that resulted from the implementation of the other schemes for the control of the interacting process.
Destekleyen Kurum
National Information Technology Development Agency
Proje Numarası
NITDA/HQ/DG/135/2005/VOL.2
Teşekkür
The authors sincerely appreciate the National Information Technology Development Agency (NITDA) for the research grant provided to carry out this work through the National Information Technology Development Fund (NITDEF)
Kaynakça
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- Ayten K K, Dumlu A & Kaleli A (2018). Real-time implementation of self-tuning regulator control technique for coupled tank industrial process system. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 1-14. DOI: 10.1177/0959651818773179
- Bhuvaneswari N S, Praveena R & Divya R (2012). System identification and modeling for interacting and non-interacting tank systems using intelligent techniques. arXiv preprint, arXiv: 1208.1103.
- Changela M & Kumar A (2015). Designing a controller for two tank interacting system. International Journal of Science and Research (IJSR), 4(5), 589-593.
- Cheung J Y M (1996). A fuzzy logic model reference adaptive controller. IEE Colloquium on Adaptive Controllers in practice,1-6.
- Cheung J Y M, Cheng K W E & Kamal A S (1996). Motor speed control by using a fuzzy logic model reference adaptive controller. 6th International Conference on Power Electronics and Variable Speed Drives, 430-435.
- Coughanowr D R & LeBlanc S E (2009). Process systems analysis and control, McGraw-Hill, New York, USA. ISBN: 978–0–07–339789–4
- Damrudhar O and Tanti D K (2016). Comparative performance analysis for two tanks liquid level control system with various controllers using MATLAB. International Journal of Latest Trends in Engineering and Technology (IJLTET), 7(2), 345-352. DOI:10.21172/1.72.555
- Dinakin D D & Oluseyi P O (2018). Optimal under-frequency load curtailment via continuous load control in a single area power system using fuzzy logic, PID-fuzzy and neuro-fuzzy (ANFIS) controllers. Jordan Journal of Electrical Engineering (JJEE), 4(4), 208-223.
- Dumont G (2011). EECE 574 – Adaptive Control, Lecture notes – Model Reference Adaptive Control.
- Fellani M A & Gabaj A M (2015). PID controller design for two tanks liquid level control system using MATLAB. International Journal of Electrical and Computer Engineering (IJECE), 5(3), 436-442.
- Jain P & Nigam M J (2013). Design of a model reference adaptive controller using modified MIT rule for a second order system. Advance in Electronic and Electric Engineering, 3(4), 477-484.
- Jang L K (2017). Feedback control for liquid level in a gravity-drained multi-tank system. Chemical Engineering & Process Techniques, 3(1), 1-10.
- John J A, Jaffar N E & Francis R M (2015). Modeling and control of coupled tank liquid level system using backstepping method. International Journal of Engineering Research & Technology (IJERT), 4(6), 667-671.
- Keerth K & Sathyanarayana M S (2012). Fuzzy implementation of model reference adaptive control of DC drives. International Journal of Engineering Science and Advanced Technology (IJESAT), 2(3), 605-611.
- Lavanya M, Aravind P, Valluvan M & Caroline B E (2013). Model based control for interacting and non-interacting level process using labview. International Journal Advanced Research in Electrical, Electronics and Instrumentation Engineering, 2(7), 3174-3179.
- Mamur H, Atacak I, Korkmaz F & Bhuiyan M R A (2017). Modeling and application of a computer-controlled liquid level tank system. Computer science & Information Technology (CS & IT), pp. 97-106.
- Manohar G, Elakkiya V, Stanley P & Sudha R (2013). Neural network based level control in two tank conical interacting system. 7th International Conference on Intelligent Systems and Control (ISCO), 194-196.
- MATLAB (2016). Natick, Massachusetts: The MathWorks, Inc., version 9.0.0.341360.
- Medewar P G, Sonawane R R & Munje R K (2017). Two tank non-interacting liquid level control comparison using fuzzy and PSO controller. National Conference on Emerging Trends in Engineering & Technology (NCETET17), IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE), 1(5), 24-31.
- Mekhanet M, Mokrani L, Ameur A & Attia Y (2016). Adaptive fuzzy gain of power system stabilizer to improve the global stability. Bulletin of Electrical Engineering and Informatics, 5(4), 421-429. DOI: 10.11591/eei.v5i4.576
- Nandhinipriyanka G, Ishwarya S, Janakiraman S, Thana C S & Vaishali P (2018). Design of model reference adaptive controller for cylinder tank system. International Journal of Pure and Applied Mathematics, 118(20), 2007-2013.
- Narayan Y & Srivastava S (2013). Response of flow rate of non-interacting tanks using NCS and fuzzy controller. 2013 International Conference on Emerging Trends in Communication, Control, Signal Processing & Computing (C2SPCA), 1-4.
- Nasar A, Jaffar N E & Kochummen S A (2015). Lyapunov rule based model reference adaptive controller designs for steam turbine speed. International Journal of Electrical Engineering & Technology (IJEET), 6(7), 13-22.
- Pankaj S, Kumar J S & Nema R K (2011). Comparative analysis of MIT rule and Lyapunov rule in model reference adaptive control scheme. Innovative Systems Design and Engineering, 2(4), 154-162.
- Parvat B J, Deo S A & Kadu C B (2015). Mathematical modeling of interacting and non interacting system. International Journal of Application or Innovation in Engineering & Management (IJAIEM), 4(1), 86-92.
- Reusch B (1997). Computational Intelligence Theory and Applications. Proceedings of International Conference, 5th Fuzzy Days, Dortmund, Germany, Springer. ISBN: 978-3-540-69031-3
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- Salunkhe S M, Kurode S R & Bhole V B (2015). Robust control of liquid level in coupled tank system using smooth first order sliding modes. 2015 International Conference on Industrial Instrumentation and Control (ICIC), pp. 1647-1650.
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- Stellet J E (2011). Influence of adaptation gain and reference model parameters on system performance for model reference adaptive control. International Scholarly and Scientific Research & Innovation, 5(12), 1660-1665.
- Swarnkar P, Jain S & Nema R K (2011). Effect of adaptation gain in model reference adaptive controlled second order system. Engineering, Technoloy & Applied Science Research, 1(3), 70-75.
- Tijani A S, Shehu M A, Alsabari A M, Sambo Y A & Tanko N L (2017). Performance analysis for coupled-tank system liquid level control using MPC, PI, PI-plus-feedforward control scheme. Journal of Robotics and Automation, 1(1), 42-53.
- Zhang P (2010). Advanced Industrial Control Technology, William Andrew, New York, USA. ISBN-13:978-1-4377-7807-6
- Zadeh L A (1975). The concept of a linguistic variable and its application to approximate reasoning – I. Information Sciences, 8, 199-249. DOI: 10.1016/0020-0255(75)90036-5
Yıl 2021,
Cilt: 5 Sayı: 4, 141 - 153, 01.10.2021
Demilade Dınakın
,
Peter Oluseyi
Proje Numarası
NITDA/HQ/DG/135/2005/VOL.2
Kaynakça
- Amat M A H C, Naim S & Zakaria S (2018). Fuzzy logic approach to identify deprivation index in Peninsular Malaysia. Bulletin of Electrical Engineering and Informatics, 7(4), 601-608. DOI: 10.11591/eei.v7i4.1352
- Astrom K J & Wittenmark B (1989). Adaptive Control, Addison-Wesley, USA.
- Atchaya, G. (2017). “Review of linear and non-linear liquid level control system.” Journal of Innovative Science & Engineering Research, 2(2), 3-7.
- Ayten K K, Dumlu A & Kaleli A (2018). Real-time implementation of self-tuning regulator control technique for coupled tank industrial process system. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 1-14. DOI: 10.1177/0959651818773179
- Bhuvaneswari N S, Praveena R & Divya R (2012). System identification and modeling for interacting and non-interacting tank systems using intelligent techniques. arXiv preprint, arXiv: 1208.1103.
- Changela M & Kumar A (2015). Designing a controller for two tank interacting system. International Journal of Science and Research (IJSR), 4(5), 589-593.
- Cheung J Y M (1996). A fuzzy logic model reference adaptive controller. IEE Colloquium on Adaptive Controllers in practice,1-6.
- Cheung J Y M, Cheng K W E & Kamal A S (1996). Motor speed control by using a fuzzy logic model reference adaptive controller. 6th International Conference on Power Electronics and Variable Speed Drives, 430-435.
- Coughanowr D R & LeBlanc S E (2009). Process systems analysis and control, McGraw-Hill, New York, USA. ISBN: 978–0–07–339789–4
- Damrudhar O and Tanti D K (2016). Comparative performance analysis for two tanks liquid level control system with various controllers using MATLAB. International Journal of Latest Trends in Engineering and Technology (IJLTET), 7(2), 345-352. DOI:10.21172/1.72.555
- Dinakin D D & Oluseyi P O (2018). Optimal under-frequency load curtailment via continuous load control in a single area power system using fuzzy logic, PID-fuzzy and neuro-fuzzy (ANFIS) controllers. Jordan Journal of Electrical Engineering (JJEE), 4(4), 208-223.
- Dumont G (2011). EECE 574 – Adaptive Control, Lecture notes – Model Reference Adaptive Control.
- Fellani M A & Gabaj A M (2015). PID controller design for two tanks liquid level control system using MATLAB. International Journal of Electrical and Computer Engineering (IJECE), 5(3), 436-442.
- Jain P & Nigam M J (2013). Design of a model reference adaptive controller using modified MIT rule for a second order system. Advance in Electronic and Electric Engineering, 3(4), 477-484.
- Jang L K (2017). Feedback control for liquid level in a gravity-drained multi-tank system. Chemical Engineering & Process Techniques, 3(1), 1-10.
- John J A, Jaffar N E & Francis R M (2015). Modeling and control of coupled tank liquid level system using backstepping method. International Journal of Engineering Research & Technology (IJERT), 4(6), 667-671.
- Keerth K & Sathyanarayana M S (2012). Fuzzy implementation of model reference adaptive control of DC drives. International Journal of Engineering Science and Advanced Technology (IJESAT), 2(3), 605-611.
- Lavanya M, Aravind P, Valluvan M & Caroline B E (2013). Model based control for interacting and non-interacting level process using labview. International Journal Advanced Research in Electrical, Electronics and Instrumentation Engineering, 2(7), 3174-3179.
- Mamur H, Atacak I, Korkmaz F & Bhuiyan M R A (2017). Modeling and application of a computer-controlled liquid level tank system. Computer science & Information Technology (CS & IT), pp. 97-106.
- Manohar G, Elakkiya V, Stanley P & Sudha R (2013). Neural network based level control in two tank conical interacting system. 7th International Conference on Intelligent Systems and Control (ISCO), 194-196.
- MATLAB (2016). Natick, Massachusetts: The MathWorks, Inc., version 9.0.0.341360.
- Medewar P G, Sonawane R R & Munje R K (2017). Two tank non-interacting liquid level control comparison using fuzzy and PSO controller. National Conference on Emerging Trends in Engineering & Technology (NCETET17), IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE), 1(5), 24-31.
- Mekhanet M, Mokrani L, Ameur A & Attia Y (2016). Adaptive fuzzy gain of power system stabilizer to improve the global stability. Bulletin of Electrical Engineering and Informatics, 5(4), 421-429. DOI: 10.11591/eei.v5i4.576
- Nandhinipriyanka G, Ishwarya S, Janakiraman S, Thana C S & Vaishali P (2018). Design of model reference adaptive controller for cylinder tank system. International Journal of Pure and Applied Mathematics, 118(20), 2007-2013.
- Narayan Y & Srivastava S (2013). Response of flow rate of non-interacting tanks using NCS and fuzzy controller. 2013 International Conference on Emerging Trends in Communication, Control, Signal Processing & Computing (C2SPCA), 1-4.
- Nasar A, Jaffar N E & Kochummen S A (2015). Lyapunov rule based model reference adaptive controller designs for steam turbine speed. International Journal of Electrical Engineering & Technology (IJEET), 6(7), 13-22.
- Pankaj S, Kumar J S & Nema R K (2011). Comparative analysis of MIT rule and Lyapunov rule in model reference adaptive control scheme. Innovative Systems Design and Engineering, 2(4), 154-162.
- Parvat B J, Deo S A & Kadu C B (2015). Mathematical modeling of interacting and non interacting system. International Journal of Application or Innovation in Engineering & Management (IJAIEM), 4(1), 86-92.
- Reusch B (1997). Computational Intelligence Theory and Applications. Proceedings of International Conference, 5th Fuzzy Days, Dortmund, Germany, Springer. ISBN: 978-3-540-69031-3
- Saju S, Revathi R & Suganya K P (2014). Modeling and control of liquid level non-linear interacting and non-interacting system. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 3(3), 8003-8013.
- Salunkhe S M, Kurode S R & Bhole V B (2015). Robust control of liquid level in coupled tank system using smooth first order sliding modes. 2015 International Conference on Industrial Instrumentation and Control (ICIC), pp. 1647-1650.
- Senapati A, Maitra N, Batabyal S & Kashyap A K (2018). Control and performance analysis of three tank flow control system using linear & non-linear controller. International Journal of Innovative Research in Computer and Comunication Engineering, 6(1), 329-340. DOI: 10.15680/IJIRCCE.2018. 0601057
- Senthilkumar M & Lincon S A (2012). Design of stabilizing PI controller for coupled tank MIMO process. International Journal of Engineering Research and Development, 3(10), 47-55.
- Stellet J E (2011). Influence of adaptation gain and reference model parameters on system performance for model reference adaptive control. International Scholarly and Scientific Research & Innovation, 5(12), 1660-1665.
- Swarnkar P, Jain S & Nema R K (2011). Effect of adaptation gain in model reference adaptive controlled second order system. Engineering, Technoloy & Applied Science Research, 1(3), 70-75.
- Tijani A S, Shehu M A, Alsabari A M, Sambo Y A & Tanko N L (2017). Performance analysis for coupled-tank system liquid level control using MPC, PI, PI-plus-feedforward control scheme. Journal of Robotics and Automation, 1(1), 42-53.
- Zhang P (2010). Advanced Industrial Control Technology, William Andrew, New York, USA. ISBN-13:978-1-4377-7807-6
- Zadeh L A (1975). The concept of a linguistic variable and its application to approximate reasoning – I. Information Sciences, 8, 199-249. DOI: 10.1016/0020-0255(75)90036-5