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Low Phase Shift and Least Squares Optimal FIR Filter

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Automation 2019 (AUTOMATION 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 920))

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Abstract

The problem of a significant phase shift in a control loop is posing a lot of challenges to the control design. One of them is definitely the loss of performance with the increased phase shift when using a filter at the system’s output. This paper contains a description of a new FIR weights determination method focused on low-pass filter design. The primary goal of this method is to minimize the phase shift caused by the filter. The filter theoretically fits a defined polynomial to an asymmetric data set. In this case, the nearest neighbour samples are only taken from the past side of the filtered vector of signal samples. This feature allows reducing the value of the phase shift, especially for a low-frequency spectrum. Therefore, the filter can be used directly in the closed-loop control and will minimize the loss of system performance.

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References

  1. Ali, F., Jain, R., Gupta, D., et al.: Design and analysis of low pass elliptic filter. In: 2016 Second International Conference on Computational Intelligence & Communication Technology (CICT). Ghaziabad, India, pp. 449–451 (2016)

    Google Scholar 

  2. Baranowski, J., Piątek, P., Bauer, W., et al.: Bi-fractional filters, part 2: right half-plane case. In: 2014 19th International Conference on Methods and Models in Automation and Robotics (MMAR), Międzyzdroje, Poland, pp. 369–373. IEEE (2014)

    Google Scholar 

  3. Cleveland, W.S., Devlin, S.J.: Locally weighted regression: an approach to regression analysis by local fitting. J. Am. Stat. Assoc. 83, 596–610 (1988)

    Article  Google Scholar 

  4. Fatehi, A., Huang, B.: Kalman filtering approach to multi-rate information fusion in the presence of irregular sampling rate and variable measurement delay. J. Process Control 53, 15–25 (2017)

    Article  Google Scholar 

  5. Ferrara, E.: Fast implementations of LMS adaptive filters. IEEE Trans. Acoust. Speech Signal Process. 28, 474–475 (1980)

    Article  Google Scholar 

  6. Filanovsky, I.M.: Bessel-Butterworth transitional filters. In: 2014 IEEE International Symposium on Circuits and Systems (ISCAS), Melbourne, VIC, Australia, pp. 2105–2108 (2014)

    Google Scholar 

  7. Hayes, M.H.: Statistical Digital Signal Processing and Modeling. Wiley, New York (2009)

    Google Scholar 

  8. Janecki, D., Cedro, L.: Determining of signal derivatives with the use of regressive differential filters. Przegląd Elektrotechniczny 87, 253–259 (2011). (in Polish)

    Google Scholar 

  9. Kalman, R.E.: A new approach to linear filtering and prediction problems. J. Basic Eng. 82, 35–45 (1960)

    Article  Google Scholar 

  10. Karam, L.J., Mcclellan, J.H.: Complex Chebyshev approximation for FIR filter design. IEEE Trans. Circuits Syst. II Analog. Digit. Signal Process. 42, 207–216 (1995)

    Article  Google Scholar 

  11. Kaya, I.: Obtaining controller parameters for a new PI-PD Smith predictor using autotuning. J. Process Control 13, 465–472 (2003)

    Article  Google Scholar 

  12. Krämer, D., King, R.: A hybrid approach for bioprocess state estimation using NIR spectroscopy and a sigma-point Kalman filter. J. Process Control (2017, in Press)

    Google Scholar 

  13. Miadlicki, K., Pajor, M., Sakow, M.: Loader crane working area monitoring system based on LIDAR scanner. In: Advances in Manufacturing, pp. 465–474. Springer (2017)

    Google Scholar 

  14. Miądlicki, K., Pajor, M., Saków, M.: Ground plane estimation from sparse LIDAR data for loader crane sensor fusion system. In: 2017 22nd International Conference on Methods and Models in Automation and Robotics (MMAR), Międzyzdroje, Poland, pp. 717–722. IEEE (2017)

    Google Scholar 

  15. Miądlicki, K., Pajor, M., Saków, M.: Real-time ground filtration method for a loader crane environment monitoring system using sparse LIDAR data. In: 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA), pp. 207–212. IEEE (2017)

    Google Scholar 

  16. Mitra, S.K., Kuo, Y.: Digital Signal Processing: A Computer-Based Approach. McGraw-Hill Higher Education, New York (2006)

    Google Scholar 

  17. Okulski, M., Ławryńczuk, M.: A cascade PD controller for heavy self-balancing robot. In: Conference on Automation, pp. 183–192. Springer (2018)

    Google Scholar 

  18. Oppenheim, A.V.: Discrete-Time Signal Processing. Pearson Education India (1999)

    Google Scholar 

  19. Owczarek, P., Goslinski, J., Rybarczyk, D., et al.: Modeling and 3D simulation of an electro-hydraulic manipulator controlled by vision system with Kalman Filter. In: Advances in Manufacturing, pp. 375–384. Springer (2018)

    Google Scholar 

  20. Piątek, P., Baranowski, J., Zagórowska, M., et al.: Bi-fractional filters, part 1: left half-plane case. In: Advances in Modelling and Control of Non-integer-Order Systems, pp. 81–90. Springer (2015)

    Google Scholar 

  21. Psychalinos, C., Tsirimokou, G., Elwakil, A.S.: Switched-capacitor fractional-step butterworth filter design. Circuits Syst. Signal Process. 35, 1377–1393 (2016)

    Article  MathSciNet  Google Scholar 

  22. Ra, W.S., Whang, I.H.: Recursive weighted robust least squares filter for frequency estimation. In: 2006 SICE-ICASE International Joint Conference, Busan, South Korea, pp. 774–778 (2006)

    Google Scholar 

  23. Raja, G.L., Ali, A.: Smith predictor based parallel cascade control strategy for unstable and integrating processes with large time delay. J. Process Control 52, 57–65 (2017)

    Article  Google Scholar 

  24. Rodríguez, C., Normey-Rico, J., Guzmán, J., et al.: On the filtered Smith predictor with feedforward compensation. J. Process Control 41, 35–46 (2016)

    Article  Google Scholar 

  25. Rybarczyk, D., Owczarek, P., Myszkowski, A.: Development of force feedback controller for the loader crane. In: Advances in Manufacturing, pp. 345–354. Springer (2018)

    Google Scholar 

  26. Saków, M.: Real-Time and low phase shift noisy signal differential estimation dedicated to teleoperation systems, pp. 132–141. Springer International Publishing, Cham (2018)

    Google Scholar 

  27. Saków, M., Marchelek, K.: Model-free and time-constant prediction for closed-loop systems with time delay. Control Eng. Pract. 81, 1–8 (2018)

    Article  Google Scholar 

  28. Saków, M., Marchelek, K., Parus, A., et al.: Signal prediction in bilateral teleoperation with force-feedback, pp. 311–323. Springer International Publishing, Cham (2018)

    Chapter  Google Scholar 

  29. Saków, M., Miądlicki, K.: Transport delay and first order inertia time signal prediction dedicated to teleoperation, pp. 142–151. Springer International Publishing, Cham (2018)

    Google Scholar 

  30. Saków, M., Miądlicki, K., Parus, A.: Self-sensing teleoperation system based on 1-dof pneumatic manipulator. J. Autom. Mob. Robot. Intell. Syst. 11, 64–76 (2017)

    Google Scholar 

  31. Saków, M., Parus, A., Miądlicki, K.: LS filter and its implementation into the control unit of the master-slave system with force-feedback (in Polish). In: Modelowanie inżynierskie, vol. 34 (2017)

    Google Scholar 

  32. Sakow, M., Parus, A., Pajor, M., et al.: Unilateral hydraulic telemanipulation system for operation in machining work area. In: Advances in Manufacturing, pp. 415–425. Springer (2018)

    Google Scholar 

  33. Saków, M., Parus, A., Pajor, M., et al.: Nonlinear inverse modeling with signal prediction in bilateral teleoperation with force-feedback. In: 2017 22nd International Conference on Methods and Models in Automation and Robotics (MMAR), Międzyzdroje, Poland, pp. 141–146. IEEE (2017)

    Google Scholar 

  34. Schoenberg, I.J.: Spline functions and the problem of graduation. Proc. Natl. Acad. Sci. 52, 947–950 (1964)

    Article  MathSciNet  Google Scholar 

  35. Tranter, W.H., Rappaport, T.S., Kosbar, K.L., et al.: Principles of Communication Systems Simulation with Wireless Applications. Prentice Hall, Upper Saddle River (2004)

    Google Scholar 

  36. Tsirimokou, G., Psychalinos, C., Elwakil, A.S.: Digitally programmed fractional-order Chebyshev filters realizations using current-mirrors. In: 2015 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 2337–2340 (2015)

    Google Scholar 

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Authors and Affiliations

  1. Faculty of Mechanical Engineering and Mechatronics, Department of Mechatronics, West Pomeranian University of Technology, Szczecin, Poland

    Mateusz Saków

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  1. Mateusz Saków
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Correspondence to Mateusz Saków .

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Editors and Affiliations

  1. Industrial Research Institute for Automation and Measurements PIAP Al, Warsaw, Poland

    Roman Szewczyk

  2. Industrial Research Institute for Automation and Measurements PIAP Al, Warsaw, Poland

    Cezary Zieliński

  3. Industrial Research Institute for Automation and Measurements PIAP Al, Warsaw, Poland

    Małgorzata Kaliczyńska

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Saków, M. (2020). Low Phase Shift and Least Squares Optimal FIR Filter. In: Szewczyk, R., Zieliński, C., Kaliczyńska, M. (eds) Automation 2019. AUTOMATION 2019. Advances in Intelligent Systems and Computing, vol 920. Springer, Cham. https://doi.org/10.1007/978-3-030-13273-6_6

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