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Logarithmic Arithmetic for Low-Power Adaptive Control Systems

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Abstract

To reduce the power dissipation in adaptive control systems, we propose replacing the exact arithmetic hardware units with approximate ones. As a case study, an adaptive control system for object tracking based on the Kalman filter is implemented in FPGA. A thorough analysis of the Kalman filter’s circuitry for real-world object tracks acquired by an aviation radar system proved that adaptive control systems can successfully compensate for the calculation errors introduced by the approximate arithmetic units. The main contributions of this paper are that the introduction of the approximate arithmetic circuits to the adaptive control system (1) preserves the required accuracy and (2) significantly reduces the power dissipation and the size of the adaptive system’s circuitry.

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References

  1. K. Abed, R. Siferd, CMOS VLSI implementation of a low-power logarithmic converter. IEEE Trans. Comput. 52(11), 1421–1433 (2003). doi:10.1109/TC.2003.1244940

    Article  Google Scholar 

  2. K. Abed, R. Siferd, VLSI implementation of a low-power antilogarithmic converter. IEEE Trans. Comput. 52(9), 1221–1228 (2003). doi:10.1109/TC.2003.1228517

    Article  Google Scholar 

  3. F. Auger, Z. Lou, B. Feuvrie, F. Li, Multiplier-free divide, square root, and log algorithms (DSP tips and tricks). Sig. Process. Mag. IEEE 28(4), 122–126 (2011). doi:10.1109/MSP.2011.941101

    Article  Google Scholar 

  4. Z. Babić, A. Avramović, P. Bulić, An iterative logarithmic multiplier. Microprocess. Microsyst. 35(1), 23–33 (2011). doi:10.1016/j.micpro.2010.07.001

    Article  Google Scholar 

  5. I. Baturone, F.J. Moreno-Velo, S. Sanchez-Solano, A. Ollero, Automatic design of fuzzy controllers for car-like autonomous robots. IEEE Trans. Fuzzy Syst. 12(4), 447–465 (2004). doi:10.1109/TFUZZ.2004.832532

    Article  Google Scholar 

  6. D. Chen, B. Zhou, Z. Guo, P. Nilsson, Design and implementation of reciprocal unit, in Circuits and Systems, 2005. 48th Midwest Symposium on, vol. 2 (2005), pp. 1318–1321. doi:10.1109/MWSCAS.2005.1594352

  7. S. Dixit, D. Nagaria, Design and analysis of cascaded lms adaptive filters for noise cancellation. Circuits Syst. Signal Process. (2016). doi:10.1007/s00034-016-0332-5

  8. J.P. Dubois, J.S. Daba, M. Naderm, E.C. Ferkh, GSM position tracking using a Kalman filter. World Acad. Sci. Eng. Technol. 68, 1610–1619 (2012)

    Google Scholar 

  9. Y. Gao, Y.J. Liu, Adaptive fuzzy optimal control using direct heuristic dynamic programming for chaotic discrete-time system. J. Vib. Control 22(2), 595–603 (2016). doi:10.1177/1077546314534286

    Article  MathSciNet  Google Scholar 

  10. M.S. Grewal, A.P. Andrews, Kalman Filtering: Theory and Practice Using MATLAB, 3rd edn. (Wiley, Hoboken, 2011)

    MATH  Google Scholar 

  11. X.V. Ha, C. Ha, J. Lee, Trajectory estimation of a tracked mobile robot using the sigma-point Kalman filter with an IMU and optical encoder, in ICIC (1) Lecture Notes in Computer Science, vol. 7399, ed. by D.S. Huang, C. Jiang, V. Bevilacqua, J.C.F. García (Springer, Berlin, 2012), pp. 415–422

    Google Scholar 

  12. A. Habegger, A. Stahel, J. Goette, M. Jacomet, An efficient hardware implementation for a reciprocal unit. In: Fifth IEEE International Symposium on Electronic Design, Test and Application, 2010. DELTA ’10 (2010), pp. 183–187. doi:10.1109/DELTA.2010.65

  13. E. Hall, D. Lynch, S. Dwyer, Generation of products and quotients using approximate binary logarithms for digital filtering applications. IEEE Trans. Comput. C–19(2), 97–105 (1970). doi:10.1109/T-C.1970.222874

    Article  MATH  Google Scholar 

  14. S. Haykin, Kalman Filtering and Neural Networks, 2nd edn. (Wiley, Hoboken, 2001)

    Book  Google Scholar 

  15. W. He, W. Ge, Y. Li, Y.J. Liu, C. Yang, C. Sun, Model identification and control design for a humanoid robot. IEEE Trans. Syst. Man Cybern. Syst. PP(99), 1–13 (2016). doi:10.1109/TSMC.2016.2557227

    Google Scholar 

  16. S.F. Hsiao, C.S. Wen, M.Y. Tsai, Low-cost design of reciprocal function units using shared multipliers and adders for polynomial approximation and Newton Raphson interpolation. in 2010 International Symposium on Next-Generation Electronics (ISNE) (2010) pp. 40–43. doi:10.1109/ISNE.2010.5669204

  17. S. Khan, I. Naseem, R. Togneri, M. Bennamoun, A novel adaptive kernel for the rbf neural networks. Circuits Syst. Signal Process. (2016). doi:10.1007/s00034-016-0375-7

  18. A.K. Kohli, D.S. Kapoor, Adaptive filtering techniques using cyclic prefix in OFDM systems for multipath fading channel prediction. Circuits Syst. Signal Process. 35(10), 3595–3618 (2016). doi:10.1007/s00034-015-0214-2

    Article  MathSciNet  MATH  Google Scholar 

  19. U. Kucukkabak, A. Akkas, Design and implementation of reciprocal unit using table look-up and Newton–Raphson iteration. in Digital System Design, 2004. DSD 2004. Euromicro Symposium on (2004), pp. 249–253. doi:10.1109/DSD.2004.1333284

  20. G. Lai, Z. Liu, Y. Zhang, C.L.P. Chen, S. Xie, Y.J. Liu, Fuzzy adaptive inverse compensation method to tracking control of uncertain nonlinear systems with generalized actuator dead zone. IEEE Trans. Fuzzy Syst. PP(99), 1–1 (2016). doi:10.1109/TFUZZ.2016.2554152

    Google Scholar 

  21. M. Lastras, B. Parhami, A logarithmic approach to energy-efficient GPU arithmetic for mobile devices. in Signals, Systems and Computers (ASILOMAR), 2013 Conference Record of the Forty Seventh Asilomar Conference on (2013) pp. 1–4

  22. D. Lee, A. Gaffar, R. Cheung, O. Mencer, W. Luk, G. Constantinides, Accuracy-guaranteed bit-width optimization. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 25(10), 1990–2000 (2006). doi:10.1109/TCAD.2006.873887

    Article  Google Scholar 

  23. T.H.S. Li, C.Y. Chen, K.C. Lim, Combination of fuzzy logic control and back propagation neural networks for the autonomous driving control of car-like mobile robot systems. in SICE Annual Conference 2010, Proceedings of (2010), pp. 2071–2076

  24. Y. Liu, S. Tong, Barrier Lyapunov functions-based adaptive control for a class of nonlinear pure-feedback systems with full state constraints. Automatica 64, 70–75 (2016). doi:10.1016/j.automatica.2015.10.034

    Article  MathSciNet  MATH  Google Scholar 

  25. Y.J. Liu, Y. Gao, S. Tong, Y. Li, Fuzzy approximation-based adaptive backstepping optimal control for a class of nonlinear discrete-time systems with dead-zone. IEEE Trans. Fuzzy Syst. 24(1), 16–28 (2016). doi:10.1109/TFUZZ.2015.2418000

    Article  Google Scholar 

  26. Y.J. Liu, S. Tong, Adaptive fuzzy control for a class of nonlinear discrete-time systems with backlash. IEEE Trans. Fuzzy Syst. 22(5), 1359–1365 (2014). doi:10.1109/TFUZZ.2013.2286837

    Article  Google Scholar 

  27. Y.J. Liu, S. Tong, Adaptive fuzzy identification and control for a class of nonlinear pure-feedback MIMO systems with unknown dead zones. IEEE Trans. Fuzzy Syst. 23(5), 1387–1398 (2015). doi:10.1109/TFUZZ.2014.2360954

    Article  Google Scholar 

  28. U. LotriČ, P. BuliC̀, Applicability of approximate multipliers in hardware neural networks. Neurocomputing 96, 57–65 (2012). doi:10.1016/j.neucom.2011.09.039

    Article  Google Scholar 

  29. V. Mahalingam, N. Ranganathan, Improving accuracy in Mitchell’s logarithmic multiplication using operand decomposition. IEEE Trans. Comput. 55(12), 1523–1535 (2006). doi:10.1109/TC.2006.198

    Article  Google Scholar 

  30. J.N. Mitchell, Computer multiplication and division using binary logarithms. IRE Trans. Electron. Comput. EC–11(4), 512–517 (1962). doi:10.1109/TEC.1962.5219391

    Article  MathSciNet  MATH  Google Scholar 

  31. M. Monajati, S.M. Fakhraie, E. Kabir, Approximate arithmetic for low-power image median filtering. Circuits Syst. Signal Process. 34(10), 3191–3219 (2015). doi:10.1007/s00034-015-9997-4

    Article  Google Scholar 

  32. E. Monmasson, L. Idkhajine, M. Cirstea, I. Bahri, A. Tisan, Mw Naouar, FPGAs in industrial control applications. IEEE Trans. Industr. Inf. 7(2), 224–243 (2011). doi:10.1109/TII.2011.2123908

    Article  Google Scholar 

  33. L. Murali, D. Chitra, T. Manigandan, B. Sharanya, An efficient adaptive filter architecture for improving the seizure detection in EEG signal. Circuits Syst. Signal Process. 35(8), 2914–2931 (2016). doi:10.1007/s00034-015-0178-2

    Article  Google Scholar 

  34. J.E. Naranjo, C. Gonzalez, R. Garcia, T. de Pedro, Lane-change fuzzy control in autonomous vehicles for the overtaking maneuver. IEEE Trans. Intell. Transp. Syst. 9(3), 438–450 (2008). doi:10.1109/TITS.2008.922880

    Article  Google Scholar 

  35. V. Paliouras, T. Stouraitis, Low-power properties of the logarithmic number system, in Computer Arithmetic, 2001. Proceedings. 15th IEEE Symposium on (2001) pp. 229–236. doi:10.1109/ARITH.2001.930124

  36. V. Paliouras, T. Stouraitis, Signal activity and power consumption reduction using the logarithmic number system, in Circuits and Systems, 2001. ISCAS 2001. The 2001 IEEE International Symposium on, vol. 2 (2001), pp. 653–656. doi:10.1109/ISCAS.2001.921155

  37. B. Robinson, D. Hernandez-Garduno, M. Saquib, Fixed and floating-point implementations of linear adaptive techniques for predicting physiological hand tremor in microsurgery. IEEE J. Sel. Topics Signal Process. 4(3), 659–667 (2010). doi:10.1109/JSTSP.2010.2048240

    Article  Google Scholar 

  38. O. Rosen, A. Medvedev, T. Wigren, Parallelization of the Kalman filter on multicore computational platforms. Control Eng. Pract. 21(9), 1188–1194 (2013). doi:10.1016/j.conengprac.2013.03.008

    Article  Google Scholar 

  39. I.W. Sandberg, Robust Kalman filter design for discrete time-delay systems. Circuits Syst. Signal Process. 21(3), 337–343 (2002)

    Article  MathSciNet  Google Scholar 

  40. Z. Shen, Y. Yu, T. Huang, Normalized subband adaptive filter algorithm with combined step size for acoustic echo cancellation. Circuits Syst. Signal Process. (2016). doi:10.1007/s00034-016-0429-x

  41. S. So, A.E.W. George, R. Ghosh, K.K. Paliwal, Kalman filter with sensitivity tuning for improved noise reduction in speech. Circuits Syst. Signal Process. (2016). doi:10.1007/s00034-016-0363-y

  42. T. Stouraitis, V. Paliouras, Considering the alternatives in low-power design. IEEE Circuits Devices Mag. 17(4), 22–29 (2001). doi:10.1109/101.950050

    Article  Google Scholar 

  43. L. Tamas, G. Lazea, R. Robotin, C. Marcu, S. Herle, Z. Szekely, State estimation based on Kalman filtering techniques in navigation, in Automation, Quality and Testing, Robotics, 2008. AQTR 2008. IEEE International Conference on, vol. 2 (2008), pp. 147–152. doi:10.1109/AQTR.2008.4588811

  44. A.A. Vidal, V.G. Tavares, J.C. Príncipe, An adaptive signal processing framework for PV power maximization. Circuits Syst. Signal Process. 34(9), 2973–2992 (2015). doi:10.1007/s00034-015-9972-0

    Article  MathSciNet  Google Scholar 

  45. P. Vouzis, M. Kothare, L. Bleris, M. Arnold, A system-on-a-chip implementation for embedded real-time model predictive control. IEEE Trans. Control Syst. Technol. 17(5), 1006–1017 (2009). doi:10.1109/TCST.2008.2004503

    Article  Google Scholar 

  46. C. Wu, X. Wang, Y. Guo, Q. Fu, Y. Yan, Robust uncertainty control of the simplified Kalman filter for acoustic echo cancelation. Circuits Syst. Signal Process. 35(12), 4584–4595 (2016). doi:10.1007/s00034-016-0263-1

    Article  MathSciNet  Google Scholar 

  47. S.W. Xu, P.L. Shui, X.Y. Yan, Y.H. Cao, Combined adaptive normalized matched filter detection of moving target in sea clutter. Circuits Syst. Signal Process. (2016). doi:10.1007/s00034-016-0413-5

  48. L. Yu, S. Fei, L. Sun, J. Huang, Y. Zhu, Fuzzy approximation of a novel nonlinear adaptive switching controller design. Circuits Syst. Signal Process. 34(2), 377–391 (2015). doi:10.1007/s00034-014-9866-6

    Article  MathSciNet  MATH  Google Scholar 

  49. W.J. Zhang, S.Y. Wang, Y.L. Feng, Novel simplex Kalman filters. Circuits Syst. Signal Process. (2016). doi:10.1007/s00034-016-0323-6

  50. X. Zhao, Y. Yin, H. Yang, R. Li, Adaptive control for a class of switched linear systems using state-dependent switching. Circuits Syst. Signal Process. 34(11), 3681–3695 (2015). doi:10.1007/s00034-015-0029-1

    Article  MathSciNet  MATH  Google Scholar 

  51. X. Zhu, Y.C. Soh, L. Xie, Robust Kalman filter design for discrete time-delay systems. Circuits Syst. Signal Process. 21(3), 319–335 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  52. Y. Zou, M. Sheng, N. Zhong, S. Xu, A generalized Kalman filter for 2D discrete systems. Circuits Syst. Signal Process. 23(5), 351–364 (2004)

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgements

This research was supported by Slovenian Research Agency under Grants P2-0359 (National research program Pervasive computing) and P2-0241 (National research program Synergetics of complex systems and processes), and by Slovenian Research Agency and Ministry of Civil Affairs, Bosnia and Herzegovina, under Grant BI-BA/16-17-029.

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Correspondence to Patricio Bulić.

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Lotrič, U., Bulić, P. Logarithmic Arithmetic for Low-Power Adaptive Control Systems. Circuits Syst Signal Process 36, 3564–3584 (2017). https://doi.org/10.1007/s00034-016-0486-1

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