Design and performance analysis of quantitative feedback theory based automated robust controller : An application to uncertain autonomous wind power system

Gudimindla Hemachandra; Manjunatha Sharma K; Gudimindla Hemachandra; Manjunatha Sharma K

doi:10.3934/energy.2018.4.576

AIMS Energy

2018, Volume 6, Issue 4: 576-592. doi: 10.3934/energy.2018.4.576

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Research article Topical Sections

Design and performance analysis of quantitative feedback theory based automated robust controller : An application to uncertain autonomous wind power system

Gudimindla Hemachandra ^{1
,
,},
Manjunatha Sharma K ²

1.
Research scholar, Department of Electrical and Electronics Engineering, National Institute ofTechnology Karnataka (NITK), Surathkal 575025
2.
Associate professor, Department of Electrical and Electronics Engineering, National Institute ofTechnology Karnataka (NITK), Surathkal 575025

Received: 02 July 2018 Accepted: 10 August 2018 Published: 16 August 2018

Use of a robust controller for handling the operational uncertainties has become imperative in real time. This paper presents a modified fitness function based automated robust controller with the aid of quantitative feedback theory (QFT) using Genetic algorithm (GA). A controller exhibiting the desired decreasing modular plot and descending phase response is devised. The addition of arctangent function as one of the fitness function term is the proposed modification that facilitates in capturing the ideal controller characteristics. The proposed controller is applied to extract maximum power from a permanent magnet synchronous generator based autonomous wind power system. The step by step design guidelines for the automated QFT robust controller is deliberated in detail. The performance evaluation is carried out for step change and stochastically varying wind speed. Finally, benchmarking of the proposed controller against those available in the literature is accomplished through extensive simulations and it will be shown that the maximum power extraction along with least electromagnetic torque oscillations are achieved with the proposed fitness function based automated QFT controller.
- automatic loop shaping,
- genetic algorithm (GA),
- maximum power point tracking(MPPT),
- quantitative feedback theory (QFT),
- robust control; wind power system
Citation: Gudimindla Hemachandra, Manjunatha Sharma K. Design and performance analysis of quantitative feedback theory based automated robust controller : An application to uncertain autonomous wind power system[J]. AIMS Energy, 2018, 6(4): 576-592. doi: 10.3934/energy.2018.4.576

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Abstract

Use of a robust controller for handling the operational uncertainties has become imperative in real time. This paper presents a modified fitness function based automated robust controller with the aid of quantitative feedback theory (QFT) using Genetic algorithm (GA). A controller exhibiting the desired decreasing modular plot and descending phase response is devised. The addition of arctangent function as one of the fitness function term is the proposed modification that facilitates in capturing the ideal controller characteristics. The proposed controller is applied to extract maximum power from a permanent magnet synchronous generator based autonomous wind power system. The step by step design guidelines for the automated QFT robust controller is deliberated in detail. The performance evaluation is carried out for step change and stochastically varying wind speed. Finally, benchmarking of the proposed controller against those available in the literature is accomplished through extensive simulations and it will be shown that the maximum power extraction along with least electromagnetic torque oscillations are achieved with the proposed fitness function based automated QFT controller.

References

[1]	Horowitz IM (1963) Synthesis of feedback systems, New York: Academic Press.
[2]	Houpis CH, Rasmussen SJ, Garcia-Sanz M (2006) Quantitative Feedback Theory. Fundamentals and Applications, 2nd Edition, CRC Press.
[3]	Garcia-Sanz M, Houpis CH (2012) Wind energy systems: control engineering design, CRC Press.
[4]	Garcia-Sanz M (2017) Robust Control Engineering: practical QFT solutions, CRC Press.
[5]	Garcia-Sanz M, Eguinoa I, Barreras M, et al. (2008) Nondiagonal MIMO QFT controller design for Darwin-type spacecraft with large flimsy appendages. J Dyn Syst-T ASME 130: 011006. doi: 10.1115/1.2807067
[6]	Garcia-Sanz M, Molins C (2008) QFT Robust Control of a Vega-type Space Launcher. IEEE Mediterranean Conference on Control and Automation 16: 35–40.
[7]	Chait Y, Yaniv O (1993) Multi-Input/Single-Output computer-aided control design using the quantitative feedback theory. Int J Robust Nonlin 3: 47–54. doi: 10.1002/rnc.4590030103
[8]	Jeyasenthil R, Purohit H, Nataraj PSV (2014) Automatic loop shaping in MIMO QFT using interval consistency based optimization technique. IEEE 23rd International Symposium on Industrial Electronics (ISIE), 75–80.
[9]	Houpis CH, Sating RR (1997) MIMO QFT CAD Package (Ver.3). Int J Control 7: 533–549.
[10]	Sating RR, Horowitz IM, Houpis CH (1993) Development of a MIMO QFT CAD Package (Ver. 2), Air Force Institute of Technology. American Control Conference.
[11]	Garcia-Sanz M, Mauch A, Philippe C (2009) QFT Control Toolbox: an Interactive Object-Oriented Matlab CAD tool for Quantitative Feedback Theory. 6th IFAC Symposium on Robust Control Design 9: Haifa, Israel.
[12]	Garcia-Sanz M (2016) The QFT Control Toolbox (QFTCT) for Matlab,Version 9.45, Available from: http://cesc.case.edu.
[13]	Borghesani C, Chait Y, Yaniv O (2003) Quantitative Feedback Theory Toolbox v 2.0 - For use with MATLAB, Terasoft, 2003.
[14]	Gutman PO (1996) Qsyn - The Toolbox for Robust Control Systems Design for use with Matlab, User's Guide, Air Force Institute of Technology, El-Op Electro-Optics Industries Ltd, Rehovot, Israel.
[15]	Gera A, Horowitz IM (1992) Optimization of the loop transfer function. Int J Control 31: 389–398.
[16]	Balance DJ, Gawthrop PH (1991) Control systems design via a Quantitative Feedback Theory approach. Proc of the IEE Conference Control, Edinburgh, UK, 476–480.
[17]	Thompson DF, Nwokah ODI (1989) Stability and optimal design in quantitative feedback theory Proc ASME WAM Conference.
[18]	Bryant GF, Halikias GD (1995) Optimal loop-shaping for systems with large parameter uncertainty via linear programming. Int J Control 62: 557–568. doi: 10.1080/00207179508921556
[19]	Chait Y, Chen Q, Hollot CV (1999) Automatic loop-shaping of QFT controllers via linear programming. J Dyn Syst-T ASME 121: 351–357. doi: 10.1115/1.2802481
[20]	Garcia-Sanz M, Guillen JC (2000) Automatic Loop-shaping of QFT Robust Controllers via Genetic Algorithms 3rd IFAC Symposium on Robust Control Design, Prague, Czech Republic.
[21]	Garcia-Sanz M, Oses JA (2004) Evolutionary Algorithms for Automatic tuning of QFT Controllers. Proceedings of the 23rd IASTED International Conference Modelling, Identification and Control, Grindelwald, Switzerland.
[22]	Garcia-Sanz M, Molins C (2009) Automatic Loop-shaping of QFT Robust Controllers. National Aerospace and Electronics Conference, NAECON'09, Dayton, Ohio, USA.
[23]	Garcia-Sanz M, Molins C (2010) Automatic Loop Shaping of QFT Robust Controllers with Multiobjective Specifications via Nonlinear Quadratic Inequalities. Proceedings of the IEEE Aerospace and Electronics Conference (NAECON) 348–353.
[24]	Ali HI, Noor SBBM, Bashi SM, et al. (2012) Quantitative Feedback Theory control design using particle swarm optimization method. T I Meas Control 34: 463–476 . doi: 10.1177/0142331210397084
[25]	Katal N, Narayan S (2016) QFT Based Robust Positioning Control of the PMSM Using Automatic Loop Shaping with Teaching Learning Optimization. Model Simul Eng 2016: 1–18.
[26]	Katal N, Narayan S (2016) Optimal QFT controller and pre-filter for buck converter using flower pollination algorithm. IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), 1–6 .
[27]	Mercader P, Astrom KJ, Banos A, et al. (2017) Robust PID Design Based on QFT and Convex-Concave Optimization. IEEE T Contr Syst T 25: 441–452. doi: 10.1109/TCST.2016.2562581
[28]	Cutululis NA, Ceanga E, Hansen AD, et al. (2006) Robust multi-model control of an autonomous wind power system. Wind Energy 9: 399–419. doi: 10.1002/we.194
[29]	Huang Y, Li H, Li G, et al. (2014) The Largest Wind Energy Capture Based on Feedback Linearization Control. Unifying Electrical Engineering and Electronics Engineering, Springer 238: 1011–1018. doi: 10.1007/978-1-4614-4981-2_109
[30]	Taraft S, Rekioua D, Aouzellag D, et al. (2015) A proposed strategy for power optimization of a wind energy conversion system connected to the grid. Energ Convers Manage 101: 489–502. doi: 10.1016/j.enconman.2015.05.047
[31]	Gupta RA, Singh B, Jain BB (2015) Wind energy conversion system using PMSG. International Conference on Recent Developments in Control, Automation and Power Engineering (RDCAPE), 199–203.

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