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Design of an Analog CMOS based Interval Type-2 Fuzzy Logic Controller Chip
Mamta Khosla, Rakesh Kumar Sarin, Moin Uddin
Pages - 167 - 183     |    Revised - 01-09-2011     |    Published - 05-10-2011
Volume - 2   Issue - 4    |    Publication Date - September / October 2011  Table of Contents
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KEYWORDS
Type-2 fuzzy logic Systems, interval type-2 fuzzy Logic Systems, Footprint of Uncertainty, CMOS, Current Mirror
ABSTRACT
We propose the design of an analog Interval Type-2 (IT2) fuzzy logic controller chip that is based on the realization approach of averaging of two Type-1 Fuzzy Logic Systems (T1 FLSs). The fuzzifier is realized using transconductance mode membership function generator circuits. The membership functions are made tunable by setting some reference voltages on the IC pins. The inference is realized using current mode MIN circuits. The consequents are also tunable by providing five reference current sources on chip. Defuzzification of both the T1 FLSs is based on weighted average method realized through scalar and multiplier-divider circuits. An analog current-mode averager circuit is used for obtaining the defuzzified output of the IT2 fuzzy logic controller chip. The chip is designed for two inputs, one output and nine tunable fuzzy rules and is realized in 0.18 µm technology. Cadence Virtuoso Schematic/Layout Editor has been used for the chip design and the performances of all the circuits are confirmed through the simulations carried out using Cadence Spectre tool. The proposed architecture has an operation speed of 20 MFLIPS and a power consumption of 20mW. The whole chip occupies an area of 0.32 mm2.
CITED BY (6)  
1 Akbarzadeh-T, M. R., & Bashari, M. RLS Based Adaptive IVT2 Fuzzy Controller for Uncertain Model of Inverted Pendulum.
2 Ross, O. H. M., & Cruz, R. S. (2015). Evolving Embedded Fuzzy Controllers. In Springer Handbook of Computational Intelligence (pp. 1451-1477). Springer Berlin Heidelberg.
3 Lam, H. K., Li, H., Deters, C., Secco, E. L., Wurdemann, H. A., & Althoefer, K. (2014). Control design for interval type-2 fuzzy systems under imperfect premise matching. Industrial Electronics, IEEE Transactions on, 61(2), 956-968.
4 Mesri, A., Khoei, A., & Hadidi, K. (2013, May). Hardware implementation of interval type-2 fuzzy logic controller. In Electrical Engineering (ICEE), 2013 21st Iranian Conference on (pp. 1-6). IEEE.
5 Khosla, M., Sarin, R. K., & Uddin, M. (2012). A simplified architecture for triangular quasi type-2 fuzzy logic systems. International Journal of Computational Intelligence Studies, 1(4), 349-367.Khosla, M., Sarin, R. K., & Uddin, M. (2012, July). Implementation of interval type-2 fuzzy systems with analog modules. In Control and System Graduate Researc
6 Khosla, M., Sarin, R. K., & Uddin, M. (2012, July). Implementation of interval type-2 fuzzy systems with analog modules. In Control and System Graduate Research Colloquium (ICSGRC), 2012 IEEE (pp. 136-141). IEEE.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
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6 PdfSR 
1 J.M. Mendel and R.I. John, “Type-2 Fuzzy Sets Made Simple”, IEEE Transaction on Fuzzy Systems, vol. 10, no. 2, 2002, pp. 117–127.
2 Q. Liang and J. M. Mendel, “Equalization of nonlinear time-varying channels using type-2 fuzzy adaptive filters”, IEEE Trans. Fuzzy Syst., vol. 8, no. 5, 2000, pp. 551–563.
3 Q. Liang and J. M. Mendel, “MPEG VBR video traffic modeling and classification using fuzzy technique”, IEEE Trans. Fuzzy Syst., vol. 9, no. 1, 2001, pp. 183–193.
4 H. B. Mitchell, “Pattern recognition using type-II fuzzy sets,” Inf. Sci., vol. 170, no. 2–4, 2005, pp. 409–418.
5 J. Zeng and Z. Q. Liu, “Type-2 fuzzy hidden Markov models and their application to speech recognition”, IEEE Trans. Fuzzy Syst., vol. 14, no. 3, 2006, pp. 454–467.
6 P. Melin and O. Castillo, “A new method for adaptive control of nonlinear plants using type-2 fuzzy logic and neural networks”, J. Gen. Syst., vol. 33, no. 2/3, 2004, pp. 289–304.
7 H. Hagras, “A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots”, IEEE Trans. Fuzzy Syst., vol. 12, no. 4, 2004, pp. 524–539.
8 Baturone, Barriga, Carlos Jimenez-Fernandez and Diego, R. Lopez, Microelectronic Design of Fuzzy-Logic Based Systems, CRC Press, 2000.
9 H. Eichfeld, T. Künemund, and M. Menke, “A 12b general-purpose fuzzy logic controller chip”, IEEE Trans. Fuzzy Syst., vol. 4, no. 4, 1996, pp. 460–475.
10 M.J. Patyra, J.L. Grantner and K. Koster, “Digital fuzzy logic controller: Design and implementation”, IEEE Trans. Fuzzy Syst., vol. 4, no. 4, 1996, pp. 439–459.
11 J.M. Jou, P.Y. Chen and S.F. Yang, “An adaptive fuzzy logic controller: Its VLSI architecture and applications”, IEEE Trans. Very Large Scale Integr. Syst., vol. 8, no. 1, 2000 pp. 52– 60.
12 V. Salapura, “A fuzzy RISC processor”, IEEE Trans. Fuzzy Syst., vol. 8, no. 6, 2000, pp. 781–790.
13 D. Kim, “An implementation of fuzzy logic controller on the reconfigurable FPGA system”, IEEE Trans. Ind. Electron., vol. 47, no. 3, 2000, pp. 703–715.
14 Y. Ota and B. Wilamowski, “CMOS implementation of a Voltage-mode fuzzy Min-Max Controller”, Journal of Circuits, Systems, and Computers, Vol. 6, No. 2, 1996, pp. 171-184.
15 M.F. Azeem, K.P. Govila, “Design of Analog CMOS Based Fuzzy Inference System”, IEEE International Conference on Fuzzy Systems, Vancouver, 2006, pp. 1715-1720.
16 V. F. Dinavari, A. Khoei, K. Hadidi, M. Soleimani, H. Mojarad, “Design of a Current-Mode Analog CMOS Fuzzy Logic Controller”, IEEE Eurocon, St. Petersburg, 2009, pp. 211-217.
17 J. Bulla, G. Sierra and M. Melgarejo “Implementing a Simple Microcontroller-Based Interval Type-2 Fuzzy Processor”, Proceedings of 51st Midwest Symposium on Circuits and Systems (MWSCAS), Knoxville (TN), 2008, pp. 69-72.
18 M. A. Melgarejo and C. A. Pena-Reyes, “Hardware architecture and FPGA implementation of a type-2 fuzzy system”, Proc. ACM GLSVLSI, Boston, MA, 2004, pp. 458–461.
19 M. A. Melgarejo, R. A. Garcia and C. A. Pena-Reyes, “Pro-two: A hardware based platform for real time type-2 fuzzy inference”, Proc. IEEE Int. Conf. Fuzzy Syst., vol. 2, 2004, pp. 977–982.
20 M. Melgarejo and C. A. Pena-Reyes, “Implementing interval type-2 fuzzy processors”, IEEE Comput. Intell. Mag., vol. 2, no. 1, 2007, pp. 63–71.
21 S. H. Huang and Y. R. Chen, “VLSI implementation of type-2 fuzzy inference processor”, Proc. IEEE Int. Symp. Circuits Syst., vol. 4, 2005, pp. 3307–3310.
22 H. Hagras, “A Type-2 Fuzzy Logic Controller for Autonomous Mobile Robots”, in Proceeding of IEEE FUZZ Conference, Budapest, Hungary, July 2004, pp. 965-970.
23 Q. Liang, N. N. Karnik, and J. M. Mendel, “Connection admission control in ATM networks using survey-based type-2 fuzzy logic systems”, IEEE Trans. Syst., Man, Cybern., C, Appl. Rev., vol. 30, no. 3, 2000, pp. 329–339.
24 Q. Liang and J. M. Mendel, “Overcoming time-varying co-channel interference using type-2 fuzzy adaptive filter”, IEEE Trans. Circuits and Systems II: Analog and Digital Signal Processing, vol. 47, No. 12, 2000, pp. 1419–1428.
25 Mamta Khosla, R K Sarin, Moin Uddin, Arun Khosla and Satvir Singh, Realizing Interval Type-2 Fuzzy Systems with Type-1 Fuzzy Systems. Book Chapter for Book titled Cross Disciplinary Applications of Artificial Intelligence and Pattern Recognition: Advancing Technologies, IGI Global, USA. (Accepted for Publication).
26 Roberto Sepúlveda, Oscar Castillo, Patricia Melin, Oscar Montiel, “An Efficient Computational Method to Implement Type-2 Fuzzy Logic in Control Applications”, Analysis and Design of Intelligent Systems using Soft Computing Techniques, Springer, 2007, pp. 45-52.
27 Roberto Sepúlveda, Oscar Montiel, Gabriel Lizárraga, Oscar Castillo, “Modeling and Simulation of the Defuzzification Stage of a Type-2 Fuzzy Controller Using the Xilinx System Generator and Simulink”, Evolutionary Design of Intelligent Systems in Modeling, Simulation and Control, Springer, 2009, pp. 309-325.
28 Jerry M. Mendel, “Type-2 fuzzy sets and Systems: an Overview”, IEEE Computational Intelligence Magazine, 2007, pp. 20-29.
29 Qilian Liang and Jerry M. Mendel, “Interval Type-2 Fuzzy Logic Systems: Theory and Design”, IEEE Transactions on Fuzzy Systems, Vol. 8, No. 5, 2000, pp.535-550.
30 J. M. Mendel, R. I. John, and F. Liu, “Interval Type-2 Fuzzy Logic Systems Made Simple”, IEEE Transactions on Fuzzy Systems, vol. 14, no. 6, 2006, pp. 808-821.
31 Mamta Khosla, R K Sarin, Moin Uddin and Ajay Sharma, “Analog Realization of Fuzzifier for IT2 Fuzzy Processor,” 3rd International Conference on Electronics Computer Technology- ICECT 2011, Kanyakumari, pp 239-245.
32 Y. Ota and B. Wilamowski, “Current-mode CMOS Implementation of a Fuzzy Min-Max Network”, Proc. World Congr. Neural Networks, vol. II, 1995, pp. 480 - 483.
Associate Professor Mamta Khosla
- India
khoslam@nitj.ac.in
Professor Rakesh Kumar Sarin
- India
Professor Moin Uddin
- India