Advertisement

Signal Processing for Control

  • William S. Levine
Chapter

Abstract

Signal processing and control are closely related. In fact, many controllers can be viewed as a special kind of signal processor that converts an exogenous input signal and a feedback signal into a control signal. Because the controller exists inside of a feedback loop, it is subject to constraints and limitations that do not apply to other signal processors. A well known example is that a stable controller in series with a stable plant can, because of the feedback, result in an unstable closed-loop system. Further constraints arise because the control signal drives a physical actuator that has limited range. The complexity of the signal processing in a control system is often quite low, as is illustrated by the Proportional + Integral + Derivative (PID) controller. Model predictive control is described as an exemplar of controllers with very demanding signal processing. ABS brakes are used to illustrate the possibilities for improved controller capability created by digital signal processing. Finally, suggestions for further reading are included.

References

  1. 1.
    Astrom, K.J., Hagglund, T.: PID Controllers: Theory, Design, and Tuning (2nd edtion), International Society for Measurement and Control, Seattle, (1995)Google Scholar
  2. 2.
    Bosch: Driving Safety Systems (2nd edition), SAE International, Warrenton, (1999)Google Scholar
  3. 3.
    Camacho, E.F., Bordons, C.: Model Predictive Control, 2nd Edition, Springer, London, (2004)zbMATHGoogle Scholar
  4. 4.
    Dorf, R.C., Bishop, R. H.: Modern Control Systems, (11th edition), Pearson Prentice-Hall, Upper Saddle River, (2008)Google Scholar
  5. 5.
    Franklin, G.F., Powell, J.D., Workman, M.: Digital Control of Dynamic Systems (3rd edition), Addison-Wesley, Menlo Park (1998)zbMATHGoogle Scholar
  6. 6.
    Franklin, G.F., Powell, J.D., Emami-Naeini, A.: Feedback Control of Dynamic Systems (5th edition), Prentice-Hall, Upper Saddle River, (2005)zbMATHGoogle Scholar
  7. 7.
    Goodwin, G.C., Graebe, S.F., Salgado, M.E.: Control System Design, Prentice-Hall, Upper Saddle River, (2001)Google Scholar
  8. 8.
    Hallum, C.: The magic of the drag tire. SAE Paper 942484, Presented at SAE MSEC (1994)Google Scholar
  9. 9.
    Hristu-Varsakelis, D., Levine, W.S.: Handbook of Networked and Embedded Control Systems, Birkhauser, Boston, (2005)CrossRefGoogle Scholar
  10. 10.
    Kailath, T. Linear Systems, Prentice-Hall, Englewood Cliffs, (1980)zbMATHGoogle Scholar
  11. 11.
    Khalil, H.K. Nonlinear Systems (3rd edition), Prentice-Hall, Upper Saddle River, (2001)Google Scholar
  12. 12.
    Kwon, W. H.,Han, S.: Receding Horizon Control: Model Predictive Control for State Models. Springer, London (2005)Google Scholar
  13. 13.
    Levine, W.S. (Editor): The Control Handbook (2nd edition), CRC Press, Boca Raton (2011)Google Scholar
  14. 14.
    Looze, D. P., Freudenberg, J. S.: Tradeoffs and limitations in feedback systems. The Control Handbook, pp 537–550, CRC Press, Boca Raton(1995)Google Scholar
  15. 15.
    Lublin, L., Athans, M.: Linear quadratic regulator control. The Control Handbook, pp 635–650, CRC Press, Boca Raton (1995)Google Scholar
  16. 16.
    Maciejowski, J. M.: Predictive control with constraints. Prentice Hall, Englewood Cliffs (2002)Google Scholar
  17. 17.
    Qin, S. J., Badgwell, T. A.: A survey of model predictive control technology. Control Engineering Practice,11, pp 733–764 (2003)CrossRefGoogle Scholar
  18. 18.
    Rawlings, J. B.: Tutorial overview of model predictive control. IEEE Control Systems Magazine, 20(3) pp 38–52, (2000)CrossRefGoogle Scholar
  19. 19.
    Rugh, W.J.: Linear System Theory (2nd edition), Prentice-Hall, Upper Saddle River, (1996)zbMATHGoogle Scholar
  20. 20.
    Scokaert, P. O. M, Mayne, D. Q., Rawlings, J. B.: Suboptimal model predictive control (feasibility implies stability). IEEE Transactions on Automatic Control, 44(3) pp 648–654 (1999)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Yang, J. S., Levine, W. S.: Specification of control systems. The Control Handbook, pp 158–169, CRC Press, Boca Raton (1995)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of ECEUniversity of MarylandCollege ParkUSA

Personalised recommendations