2014
1
2
2
0
Fuzzy Logic Based Life Estimation of PWM Driven Induction Motors
2
2
Pulsewidth modulated (PWM) adjustable frequency drives (AFDs) are extensively used in industries for control of induction motors. It has led to significant advantages in terms of the performance, size, and efficiency but the output voltage waveform no longer remains sinusoidal. Hence, overshoots, high rate of rise, harmonics and transients are observed in the voltage wave. They increase voltage and thermal stresses; resulting into accelerated insulation aging. This paper presents the application of fuzzy logic to life estimation of PWM driven induction motors. Insulation stress parameters are experimentally computed for wide range of switching frequency and used in fuzzy logic based life estimation algorithms. The results obtained with the fuzzy expert system show a performance approaching that attainable for the life model based on the inverse power law.
1

63
71


T. G.
Arora
Department of Electrical Engineering, Shri Randeobaba College of Engineering and Management, Nagpur, India
Department of Electrical Engineering, Shri
Iran


M. V.
Aware
Department of Electrical Engineering, Visvesvaraya National Institute of Technology, Nagpur, India.
Department of Electrical Engineering, Visvesvaraya
Iran


D. R.
Tutakne
Department of Electrical Engineering, Shri Randeobaba College of Engineering and Management, Nagpur, India
Department of Electrical Engineering, Shri
Iran
Insulation aging
Life model
Peak voltage
Thermal stress
[[1] P. Lezana, J. Rodriguez, M. A. Perez, and J. Espinoza “Input Current Harmonics in a Regenerative Multicell Inverter with SinglePhase PWM Rectifiers”, IEEE Trans. on Industrial Electronics, vol.56, pp. 408417, Feb. 2009. ##[2] D. Fabiani, A. Cavallini, and G. C. Montanari, “Aging Investigation of Motor Winding Insulation subjected to PWM Supply”, in Proc. Of IEEE Conf. on Elect. Ins. & Dielect. Phenomenon (CEIDP), pp 434457, 2009. ##[3] G. C. Montanari, A. Cavallini, and A. Caprava, “Partial Discharge activity and Aging of Power Electronics Controlled Motors”, in Proc. Of IEEE International symposium on Elect. Insulating Materials (ISEIM), pp. 168171, 2005. ##[4] G. C. Montanari and D. Fabiani, “The Effect of Nonsinusoidal Voltage on Intrinsic Aging Of Cable & Capacitor Insulating Materials” IEEE Trans. on Dielectrics & Elect. Insulation, vol.6, pp 798802, Dec1999. ##[5] T. G. Arora, M. V. Aware, and D. R. Tutakne, “Accelerated Insulation Aging in Thyristor Controlled Single Phase Induction Motors”, International Joint Journal of Conferences in Engineering, vol.1, pp 54 – 57, May2009. ##[6] Ghinello, G. Mazranti, D. Fabiani and A .Cavallini, “An Investigation of the Endurance of Capacitors Supplied by Nonsinusoidal Voltage”, Proc. Of IEEE CEIDP, Atlanta, USA, pp 723 727, Oct1998. ##[7] T. G. Arora, M. V. Aware, and D. R. Tutakne, “Accelerated Insulation Aging in Phase Angle Controlled Single Phase Induction Motors”, in Proc. IEEE International Region Ten conference (TENCON09), Nov2009. ##[8] C. Hudon, N. Amyot, T. Lebey, P. Casteian, and N. Kandev, “Testing of Lowvoltage Motor Turn Insulation Intended for Pulsewidth modulated Applications”, IEEE Trans. on Dielectrics & Elect. Insulation, vol.7, pp 111121, Dec2000. ##[9] J. P. Bellommo, P. Casteian, and T. Lebey, “The Effect of Pulsed Voltage on Dielectric Material Properties”, IEEE Trans. on Dielectrics & Elect. Insulation, vol.6, pp 2026, Feb1999. ##[10] T. G. Arora, M. V. Aware, and D. R. Tutakne, “Fuzzy Logic Application to Life Estimation of Phase Angle Controlled Induction Motors”, International IEEE Symposium on Diagnostic of Electrical Machines and Power Electronic Drives (SDEMPED09), August2009. ##[11] V. Mihaila, S. Duchesne, and D. Roger, “A Simulation Method to Predict the Turntoturn Voltage Spikes in a PWM Fed Motor Winding” IEEE Transactions on Dielectrics and Electrical Insulation, vol. 18, pp 16091615, October 2011. ##[12] S. U. Haq, S. H. Jayaram, and E. A. Cherney, “Evaluation of Medium Voltage Enameled Wire Exposed to Fast Repetitive Voltage Pulses”, IEEE Trans. on Dielectrics & Elect. Insulation, vol.1, pp 194203, Dec 2007. ##[13] A. Cavallini and G. C. Montanari, “Effect of Supply Voltage frequency on testing of Insulation System”, IEEE Trans. on Dielectrics & Elect. Insulation, vol.13, pp 111121, Dec2006. ##[14] E. Sharifi, S. H. Jayaram, and E. A. Cherney, “Analysis of Thermal Stresses in Mediumvoltage Motor Coils under Repetitive Fast Pulse and Highfrequency Voltages”, IEEE Trans. on Dielectrics & Elect. Insulation, vol.17, pp 13781384, Dec2010. ##[15] H. Okubo, N. Hayakawa, and G. C. Montanari, “Tech. Development on PD Measurement & Elect. Insulation Tech. for Low Voltage Motors Driven by Voltage Inverters”, IEEE Trans. on Dielectrics & Elect. Insulation, vol.6, pp 15161530, Dec2007. ##[16] E. Lindell, T. Bengtsson1, J. Blennow, and S. M. Gubanski, “Influence of Rise Time on Partial Discharge Extinction Voltage at Semisquare Voltage Waveforms”, IEEE Trans .on Dielectrics & Elect. Insulation, vol.17, pp 141148, Feb2010. [17] S. M Gubanski, B. Sonerud., T. Bengtsson, and J. Blennow., “Dielectric Heating in Insulating Materials Subjected to Voltage Waveforms with High Harmonic Content”, IEEE Trans. on Dielectrics and Elect. Insulation, vol. 16, pp 19261931, August2009. ##[18] A. Tzimas., S. Rowland., L. A. Dissado, M. Fu, and U.H. Nisson, “Effect of Long Time Electrical and Thermal Stresses upon the Endurance Capability of Cable Insulation Material”, IEEE Trans. on Dielectrics and Elect. Insulation, vol.6, pp14361443 Oct2009. ##[19] S. Grubic, J.M. Aller, B. Lu, and T.G. Habetler, “A Survey on Testing and Monitoring Methods for Stator Insulation Systems of Low Voltage Induction Machines Focusing on Turn Insulation Problem”, IEEE Trans. on Industrial Electronics, vol.55, pp 41274136 Dec 2008. ##[20] E.Cox; The Fuzzy System Handbook, A.P.Professional Press, Cambridge M.A., 1994. ##[21] G. C. Montanari, G. Mzzanti, and A. Cavallini, “Progress in Electrothermal Life Modeling of Electrical Insulation during the Last Decades”, IEEE Trans. on Elect. Insulation, vol.9, pp 17301741, Oct2002. ##[22] G. C. Stone, E. A. Boulter, I. Culbert, and H. Dhirani; Electrical Insulation for Rotating Machines, IEEE Press series on Power Engineering, Piscataway N J, 2004.##]
A High Efficiency LowVoltage Soft Switching DC–DC Converter for Portable Applications
2
2
This paper presents a novel control method to improve the efficiency of lowvoltage DCDC converters at light loads. Pulse Width Modulation (PWM) converters have poor efficiencies at light loads, while pulse frequency modulation (PFM) control is more efficient for the same cases. Switching losses constitute a major portion of the total power loss at light loads. To decrease the switching losses and to increase efficiency, converters based on softswitching are utilized. This paper presents the design of a softswitching DCDC buck converter in a 90nm CMOS technology. Simulation results by HSPICE shows a 21 mV output ripple on a 0.5 V output voltage for an input voltage of 1.4 V. Finally, the efficiency of 95% at a load current of 50 mA having 74 mA of current ripple is achievable.
1

73
81


P.
Amiri
Faculty of Electrical and Computer Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran.
Faculty of Electrical and Computer Engineering,
Iran


M.
Sharafi
Faculty of Electrical and Computer Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran.
Faculty of Electrical and Computer Engineering,
Iran
Synchronous buck converter
PFM control
Soft switch
DCM
[[1] S. Zhou and G. A. RinconMora, “A high efficiency, soft switching dc–dc converter with adaptive currentripple control for portable applications,” IEEE Trans. Circuits and Systems, vol. 53, no. 4, pp. 319–323, 2006. ##[2] B. Razavi, Design of Analog CMOS Integrated Circuits, Boston, MA: McGrawHill, 2001. ##[3] A. Stratakos, “HighEfficiency LowVoltage DCDC Conversion for Portable Applications,” PhD thesis, Berkeley Univ, 1999. ##[4] W. R. Liou, M. L. Yeh, and Y. L. Kuo, “A High Efficiency DualMode Buck Converter IC for Portable Applications,” IEEE Trans. Power Electronics, vol. 23, no. 2, pp. 667677, 2008. ##[5] M. Brown, Practical switching power supply design, Academic Press, 1990.G. A. RincónMora and B. Sahu, “An Accurate, LowVoltage, CMOS Switching Power Supply With Adaptive On ##Time PulseFrequency Modulation (PFM) Control,” IEEE Trans. Circuits and Systems, vol. 54, no. 2, 2007. ##[6] V. Yousefzadeh, N. Wang, and Z. P. D. Maksimovic, “A Digitally Controlled DC/DC Converter for an RF Power Amplifier,” IEEE Trans. Power Electronics, vol. 21, no. 1, pp. 164172, 2006. ##[7] Z. Bi and W. Xia, “Modeling and Simulation of DualMode DC/DC Buck Converter,” Second International Conference on Computer Modeling and Simulation, 2010. ##[8] Ch. L. Chen, W. L. Hsieh, K. J. Lai, K. H. Chen, and Ch. S. Wang, “A New PWM/PFM Control Technique for Improving Efficiency Over Wide Load Range,” 15th IEEE International Conference on Electronics, Circuits and Systems, 2008. ##[9] Ch. Ch. Wang, Ch. L. Chen, G. N. Sung, and Ch. L. Wang, “A highefficiency DC–DC buck converter for sub2_VDD power supply,” Microelectronics J, vol. 42, no. 5, pp. 709717, 2011. ##[10] D. Fernandez, “Modeling and Analysis of the Effects of PCB Parasitics on Integrated DCDC Converters,” Master of Science thesis, California Polytechnic State Univ, 2011##]
Mitigation of Switching Harmonics in Shunt Active Power Filter Based on Variable Structure Control Approach
2
2
This paper presents a novel control approach used in shunt active power filter based on variable structure control combined with Random PWM technique (RVSC) that provides robust, fast, and more favorable performance for active power filter. This control strategy is compared with two other strategies to show the effectiveness of the introduced methods; pulse width modulated proportionalintegral control (PIC), and Random Pulse Width Modulated proportionalintegral control (RPIC). The simulation results with and without the shunt active power filter in the system are presented and analyzed. The simulation results show that the RVSC controller has a better performance than other control strategies, allowing compensation of reactive power, reducing high frequency harmonics thus overcoming the problem of electromagnetic interference (EMI), reducing dc current injection below the limit specified in IEEE1547 standard, and also reducing the harmonic level below the limit specified in IEEE519 standard.
1

83
88


S.
Mohammadi
Islamic Azad University of Bojnourd, Bojnourd, Iran
Islamic Azad University of Bojnourd, Bojnourd,
Iran


H. R.
Mosaddegh
Ferdowsi University of Mashhad, Mashhad, Iran
Ferdowsi University of Mashhad, Mashhad, Iran
Iran


M.
Yousefian
Technical College of Shahid Mohammad Montazeri of Mashhad, Mashhad, Iran
Technical College of Shahid Mohammad Montazeri
Iran
Active power filter
Random PWM
Variable Structure Control THD
[[1] V. S. Bandal and P. N. Madurwar, “Performance Analysis of Shunt Active Power Filter Using Sliding Mode Control Strategies” 12th IEEE Workshop on Variable Structure Systems, VSS’12, January1214, Mumbai, 2012. ##[2] E. WiebeQuintana, J. L. DuránGómez, and P. R. AcostaCano, “DeltaSigma Integral SlidingMode Control Strategy of a ThreePhase Active Power Filter using dq Frame Theory”, Proceedings of the Electronics, Robotics and Automotive Mechanics Conference, 2007. ##[3] H. Yong and Y. Guodong, “Integral Sliding Mode Variable Structure Control for DSTATCOM”, International Conference on Measuring Technology and Mechatronics Automation, 2010. G. Chang and S. Chang, “A novel reference compensation current strategy for shunt active power filter control”, IEEE Transactions on Power Delivery, vol. 19, no. 4, pp. 1751 1758, 2004. ##[4] B. Cheng, P. Wang, and Z. Zhang, “Sliding mode control for a shunt active power filter”, 2011 Third International Conference on MeasuringTechnology and Mechatronics Automation, 3:282285, 2011. ##[5] J. Fei, T. Li，and S. Zhang, “Indirect Current Control of Active Power Filter Using Novel Sliding Mode Controller,” Control and Modeling for Power Electronics (COMPEL), 2012 IEEE 13th Workshop on, 2012. ##[6] K. Borisov, H. Ginn, and A. Trzynadlowski, “Mitigation of Electromagnetic Noise in a Shunt Active Power Filter Using Random PWM”, The 33rd Annual Conference of the IEEE Industrial Electronics Society (IECON) Nov. 58, 2007, Taipei, Taiwan, 2007. ##[8] J. Mahdavi, S. H. Kaboli, and H. A. Toliyat, “Conducted Electromagnetic Emissions in Unity Power Factor AC/DC Converters: Comparison Between PWM and RPWM Techniques,” Power Electronics Specialists Conference, PESC 99. 30th Annual IEEE, 1999. ##[9] H. C. Chen, Y. C. Chang, and J. D. Lin, “Analysis of the Optimal Voltage Spectrumin RPWM Tables,” International Conference on Power Electronics and Drives Systems, 2005. PEDS 2005. ##[10] H. Akagi, E. Watanabe, and M. Aredes, ”Instantaneous Power Theory and Applications to Power Conditioning”, edited by Mohamed E. Elicole, Hawari, ISBN: 9780470107614, 2007.##]
An Improved TimeReversalBased Target Localization for ThroughWall Microwave Imaging
2
2
Recently, time reversal (TR) method, due to its high functionality in heterogeneous media has been widely employed in microwave imaging (MI) applications. One of the applications turning into a great interest is throughwall microwave imaging (TWMI). In this paper, TR method is applied to detect and localize a target obscured by a brick wall using a numerically generated data. Regarding this, it is shown that when the signals acquired by a set of receivers are time reversed and backpropagated to the targetembedded media, finding the optimum time frame which the constituted image represents a true location of the target becomes infeasible. Indeed, there are situations pertinent to the target distance ratio that the previouslyused Maximum field method and Entropybased methods may fail to select the optimum time frame. As a result, an improved method which is based on initial reflection from the target is proposed. According to different target locations described in this research, the results show this method prevails over the shortcomings of the former methods.
1

89
97


A. B.
Gorji
Faculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran
Faculty of Electrical and Computer Engineering,
Iran


B.
Zakeri
Faculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran
Faculty of Electrical and Computer Engineering,
Iran
Finitedifference timedomain
Optimum focusing
Target distance ratio
Target initial reflection
Throughwall microwave imaging
Time reversal
[[1] M. G. Amin and F. Ahmad, (2012, Sep., 27), Throughthewall radar imaging: theory and applications, Villanova Univ., Villanova, PA. ##[2] R. Zetik, S. Crabbe, J. Krajnak, P. Peyerl, J. Sachs, and R. Thoma, “Detection and localization of person behind obstacles using Msequence throughthewall radar,” in Proc. SPIE , 2006, vol. 6201. ##[3] M. Dehmollaian and K. Sarabandi, “Refocusing through building walls using synthetic aperture radar,” IEEE Trans. Geosci. Remote Sens., vol. 46, no. 6, pp. 15891599, Jun. 2008. ##[4] K. M. Yemelyanov, N. Engheta, A. Hoorfar, and J. A. McVay, “Adaptive polarization contrast techniques for throughwall microwave imaging applications,” IEEE Trans. Geosci. Remote Sens., vol. 47, no. 5, pp. 13621374, May 2009. ##[5] F. Soldovieri, R. Solimene, and G. Prisco, “A multiarray tomographic approach for throughwall imaging,” IEEE Trans. Geosci. Remote Sens., vol. 46, no. 4, pp. 11921199, Apr. 2008. ##[6] S. Kidera, T. Sakamoto, and T. Sato, “Highresolution 3D imaging algorithm with an envelope of modified spheres for UWB throughthewall radars,” IEEE Trans. Antennas Propag., vol. 57, no. 11, pp. 35213529, Nov. 2009. ##[7] A. Ishimaru, S. Jaruwatanadilok, and Y. Kuga, “Time reversal effects in random scattering media on superresolution, shower curtain effects, and backscattering enhancement.” Radio Sci., vol. 42, 2007. ##[8] M. Fink, D. Cassereau, A. Derode, C. Prada, P. Roux, M. Tanter, J. Thomas, and F. Wu, “Timereversed acoustics,” Rep. Prog. Phys., vol. 63, pp. 1933–1995, 2000. ##[9] G. Micolau, M. Saillard, and P. Borderies, “DORT method as applied to ultrawideband signals for detection of buried objects,” IEEE Trans. Geosci. Remote Sens., vol. 41, no. 8, pp. 18131820, Aug. 2003. ##[10] H. T. Nguyen, J. B. Andersen, G. F. Pedersen, P. Kyritsi, and P. C. F. Eggers, “Time reversal in wireless communications: a measurementbased investigation,” IEEE Trans. Wireless Commun., vol. 5, no. 8, pp. 22422252, Aug. 2006. ##[11] P. Kosmas and C. M. Rappaport, “Time reversal with the FDTD method for microwave breast cancer detection,” IEEE Trans. Microw. Theory Techn., vol. 53, no. 7, pp. 23172323, Jul. 2005. ##[12] Y. Chen, E. Gunawan, K. S. Loon, S. Wang, C. B. Soh, and T. C. Putti, “Timereversal ultrawideband breast imaging: pulse design criteria considering multiple tumors with unknown tissue properties,” IEEE Trans. Antennas Propag., vol. 56, no. 9, pp. 30733077, Sep. 2008. ##[13] W. Zheng, Z. Zhao, and Z. Nie, “Application of TRM in the UWB through wall radar,” PIER,vol. 87, pp. 279296, 2008. [14] A. Cresp, I. Aliferis, M. J. Yedlin, Ch. Pichot, and J. Y. Dauvignac, “Investigation of timereversal processing for surfacepenetrating radar detection in a multipletarget configuration,” in Proc. 5th European Radar Conference, Amsterdam, The Netherland, Oct. 2008. ##[15] W. Zhang and A. Hoorfar, and L.Li, “Throughthewall target localization with time reversal MUSIC method ,” PIER, vol. 106, pp. 7589, 2010. ##[16] A. Taflove and S. C. Hagness, Computational Electrodynamics: The FiniteDifference TimeDomain Method, 3rd ed., Boston, MA: Artech House, 2005. ##[17] J. P. Stang, “A 3D active microwave imaging system for breast cancer screening ,” Ph.D. dissertation, Dept. Elec. Eng., Duke Univ., Durham, NC, 2008. ##[18] M. Yavuz and F. L. Teixeira, “Full timedomain DORT for ultrawideband electromagnetic fields in dispersive, random inhomogeneous media,” IEEE Trans. Antennas Propag., vol. 54, no. 8, pp. 23052315, Aug. 2006. ##[19] A. J. Devaney, “Time reversal imaging of obscured targets from multistatic data,” IEEE Trans. Antennas Propag., vol. 53, no. 5, pp. 16001610, May 2005. ##[20] J. F. Moura and Y. Jin, “Time reversal imaging by adaptive interference canceling,” IEEE Trans. Signal Process., vol. 56, no. 1, pp. 233247, Jan. 2006. ##[21] P. Kosmas and C. M. Rappaport, “A matchedfilter FDTDbased time reversal approach for microwave breast cancer detection,” IEEE Trans. Antennas Propag., vol. 54, no. 4, pp. 12571264, Apr. 2006. ##[22] N. Maaref, P. Millot, and X. Ferrieres, C.Pichot, and O. Picon, “Electromagnetic imaging method based on time reversal processing applied to throughthewall target localization,” PIER,vol. 1, pp. 5967, 2008. ##[23] D. Liu, J. Krolik, and L. Carin, “Electromagnetic target detection in uncertain media: timereversal and minimumvariance algorithms,” IEEE Trans. Geosci. Remote Sens., vol. 45, no. 4, pp. 934944, Apr. 2007. ##[24] I. Scott, “Developments in timereversal of electromagnetic fields using the transmissionline modeling method,” Ph.D. dissertation, School of Elec. and Electron. Eng., Univ. of Nottingham, Nottingham, UK, 2009. ##[25] D. Liu, G. Kang, L. Li, Y. Chen, S. Vasudevan, W. Joines, Q. H. Liu, J. Krolik, and L. Carin, “Electromagnetic timereversal imaging of a target in a cluttered environment,” IEEE Trans. Antennas Propag., vol. 53, no. 9, pp. 30583066, Sep. 2005. ##[26] G. Montaldo, P. Roux, A. Derode, C. Negreira, and M. Fink, “Ultrasonic shock wave generator using 1bit timereversal in a dispersive medium: application to lithotripsy,” Appl. Phys. Lett., vol.80, pp. 897–899, 2002. ##[27] C. Thajudeen, A. Hoorfar, and F. Ahmad, “Measured complex permittivity of walls with different hydration levels and the effect on power estimation of TWRI target returns,” PIER,vol. 30, pp. 177199, 2011. ##[28] M. Yavuz and F. L. Teixeira, “Frequency dispersion compensation in time reversal techniques for UWB electromagnetic waves,” IEEE Geosci. Remote Sens. Lett., vol. 2, no.2, pp. 233237, Apr. 2005. ##[29] J. D. Taylor, Ultrawideband Radar: Applications and Design, CRC Press, 2012.##]
Automatic Sleep Stages Detection Based on EEG Signals Using Combination of Classifiers
2
2
Sleep stages classification is one of the most important methods for diagnosis in psychiatry and neurology. In this paper, a combination of three kinds of classifiers are proposed which classify the EEG signal into five sleep stages including Awake, NREM (nonrapid eye movement) stage 1, NREM stage 2, NREM stage 3 and 4 (also called Slow Wave Sleep), and REM. Twentyfive all night recordings from Physionet database are used in this study. EEG signals were decomposed into the frequency subbands using wavelet packet tree (WPT) and a set of statistical features was extracted from the subbands to represent the distribution of wavelet coefficients. Then, these statistical features are used as the input to three different classifiers: (1) Logistic Linear classifier, (2) Gaussian classifier and (3) Radial Basis Function classifier. As the results show, each classifier has its own characteristics. It detects particular stages with high accuracy but, on the other hand, it has not enough success to detect the others. To overcome this problem, we tried the majority vote combination method to combine the outputs of these base classifiers to have a rather good success in detecting all sleep stages. The highest classification accuracy is obtained for Slow Wave Sleep as 81.68% in addition to the lowest classification accuracy of 43.68% for NREM stage 1. The overall accuracy is 70%.
1

99
105


R.
Kianzad
Babol Noshirvani University of Technology, Babol, Iran
Babol Noshirvani University of Technology,
Iran


H.
Montazery Kordy
Babol Noshirvani University of Technology, Babol, Iran
Babol Noshirvani University of Technology,
Iran
Sleep stages classification
EEG signals
Wavelet packets
Classifier combination
Majority voting
[[1] M. E. Tagluk, N. Sezgin, and M. Akin, “Estimation of sleep stages by an artificial neural network employing EEG, EMG and EOG,” J Med Syst, vol. 34, pp. 717–725, 2010. ##[2] A. Rechtschaffen and A. Kales, “A manual of standardized terminology, techniques and scoring systems for sleep Stages of human subjects,” US Government Printing Office, Washington, 1969. ##[3] M. Reite, D. Buysse, C. Reynolds, and W. Mendelson, “The use of polysomnography in the evaluation of insomnia,” Sleep, vol. 18, pp. 58–70, 1995. ##[4] “American Academy of Sleep Medicine Task Force. Sleep related breathing disorders in adults: Recommendations for syndrome definition and measurement techniques in clinical research,” Sleep, vol. 22(5), pp. 667–689, 1999. ##[5] G. Becq, S. Charbonnier, F. Chapotot, A. Buguet, L. Bourdon, and P. Baconnier., “Comparison between five classifiers for automatic scoring of human sleep recordings,” Studies in Computational Intelligence (SCI), vol. 4, pp. 113–127, 2005. ##[6] E. Oropesa, H. L. Cycon, and M. Jobert, “Sleep stage classification using wavelet transform and neural network,” International Computer Science Institute (ICSI), 1999. ##[7] M. Kiymik, M. Akin, and A. Subasi, “Automatic recognition of alertness level by using wavelet transform and artificial neural network,” J. Neuroscience Methods, vol. 139, pp. 231–240, 2004. ##[8] http://www.physionet.org/pn3/ucddb/ ##[9] A. Subasi, “Automatic recognition of alertness level from EEG by using neural network and wavelet coefficients,” Expert Systems with Applications, vol. 28, pp. 701–711, 2005. ##[10] C. Burros, R. Goliath, and H. Guo, “Introduction to wavelets and wavelet transforms,” Prentice Hall Pub, 1998. ##[11] S. Theodoridis and K. Koutroumbas, Pattern Recognition, 4rd ed., Elsevier, 2009, pp. 34–36. ##[12] T. Poggio and F. Girosi, “Networks for approximation and learning,” Proc. IEEE, vol. 78, no. 9, pp. 1481–1497, 1990. [13] M. N. Murty and V. S. Devi, Pattern Recognition, vol. 0, Springer: London, 2011, pp.188–206. ##[14] C. A. Shipp and L. I. Kuncheva, “Relationships between combination methods and measures of diversity in combining classifiers,” Information Fusion, vol. 3, pp. 135–148, 2002. ##[15] L. Lam and C. Y. Suen, “Application of majority voting to pattern recognition: an analysis of its behavior and performance,” IEEE Transaction on systems, man, and cyberneticsPart A: systems and humans, vol. 27, no. 5, September 1997. ##[16] P. Robert, W. Duin, and D. Tax, PRTools: A Matlab toolbox for pattern recognition, 2012, software available at http://prtools.org/software/.##]
A Comparison of Different Control Design Methods for the Linearized CSTR Temperature Model
2
2
Continuous Stirred Tank Reactor (CSTR) has particular importance in chemical industry. CSTR has usually a nonlinear behavior which makes it difficult to control. The reactor has two parameters: the concentration and temperature of mixture both of which are uncertain. This case of CSTR has large disturbance in domain. In order for disturbance rejection, a controller has to be designed. In this paper, for modeling the CSTR system, first, the PI and PID controllers are designed by two methods, the automatic with Matlab Simulink and ZieglerNichols (ZN) method. Then, reset control is replaced and tuned by their parameters. The main aim of this work is to compare the output responses (temperatures) of controllers with each other. In this work a reset controller is proposed for the thermal reactor model. Due to complexity of control of this plant, different design methods should be evaluated for disturbance rejection and input tracking. The results show that the reset controller is better than the PI controller in disturbance elimination. Finally, controller’s output response is investigated for improvement in disturbance rejection and change in the setpoint.
1

107
114


A. D.
Shakib Joo
Shahid Rajaee Teacher Training University, Lavizan, Tehran
Shahid Rajaee Teacher Training University,
Iran
Clegg Integrator
CSTR
PI+CI integrator
[[1] S. S. Ge, C. C. Hang, and T. Zhang, “Nonlinear adaptive control using neural networks and its application to CSTR systems”, Journal of Process Control, vol. 9, pp. 313–323, 1998. ##[2] C. T. Chen and C. S. Dai, “Robust controller design for a class of nonlinear uncertain chemical processes”, Journal of Process Control, vol. 11, pp.469–482, 2001. ##[3] C. T. Chen and S. T. Peng, “Intelligent process control using neural fuzzy techniques”, Journal of Process Control, vol. 9, pp. 493–503, 1999. ##[4] C. A. Smith and A. B. Corripio, “Principles and Practice of Automation Process Control”, JohnWiely, 1985. ##[5] A. Baños and A. Barreiro, “Reset control systems”, Springer London Dordrecht Heidelberg New York, advances industrial control, 2012. ##[6] A. Banos and A. Vidal, “Design of PI+CI Reset Compensators for second order plants”, IEEE International symposium on vigo, pp. 118123, 2007. ##[7] A. Vidal and A. Baños, “Reset compensation for temperature control: experimental applications on heat exchangers”, Chem. Eng. J., vol. 159(1–3), pp. 170–181, 2010. ##[8] A. Baños, F. Perez, and J. Cervera “Discretetime reset control applied to networked control systems” In 35th annual conference on IEEE industrial electronics society, pp. 2993 2998, 2009. ##[9] Y. Guo, Y. Wang, J. Zheng, and L. Xie, “Stability analysis, design and application of reset control systems with discrete time triggering conditions”, IEEE International Conference on Control and Automation, Guangzhou, China, pp. 31963201, 2007. ##[10] O. Beker, C. V. Hollot, Y. Chait, and H. Han, “Plant with integrator: an example of reset control overcoming limitations of linear systems”, IEEE Trans. Autom. Control, vol. 46, no. 11, pp. 1797–1799, 2001. ##[11] A. Baños and A. Barreiro, “DelayIndependent stability of Reset systems”, IEEE Trans. Autom. Control, vol. 52, no. 2, pp. 341346, 2009. ##[12] A. Baños, A. Barreiro, “DelayIndependent stability of Reset systems”, IEEE Trans. Autom. Control, vol. 46, pp. 216221, 2010. ##[13] A. Vidal, A. Banos, J. C. Moreno, and M. Berenguel, “PI+CI compensation with variable rest: Application on solar collector fields”, in Proc. 34th Ann. Conf. IEEE Ind. Electron. Soc., pp. 321 326, 2008. ##[14] A. Vidal and A. Banos, “Reset compensation for temperature control Experimental application on heat exchangers”, Chem. Eng. J., vol. 159, pp. 170181, 2010. ##[15] J. Carrasco and A. Banos, “Reset control of an Industrial InLine pH process”, IEEE Transaction on Control Systems Technology, vol. 20, no. 4, pp. 11001106, 2011. ##[16] H. Li, C. Du, and Y. Wang, “ Discrete time H2 optimal reset control with application to HDD trackfollowing”, Chinese Control and Decision Conference, pp. 36133617, 2009.##]