Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

Model predictive control for deeply pipelined field-programmable gate array implementation: algorithms and circuitry

Model predictive control for deeply pipelined field-programmable gate array implementation: algorithms and circuitry

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Add to cart
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)
Add to cart

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Control Theory & Applications — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

© The Institution of Engineering and Technology
Published

Model predictive control (MPC) is an optimisation-based scheme that imposes a real-time constraint on computing the solution of a quadratic programming (QP) problem. The implementation of MPC in fast embedded systems presents new technological challenges. In this paper we present a parameterised field-programmable gate array implementation of a customised QP solver for optimal control of linear processes with constraints, which can achieve substantial acceleration over a general purpose microprocessor, especially as the size of the optimisation problem grows. The focus is on exploiting the structure and accelerating the computational bottleneck in a primal-dual interior-point method. We then introduce a new MPC formulation that can take advantage of the novel computational opportunities, in the form of parallel computational channels, offered by the proposed pipelined architecture to improve performance even further. This highlights the importance of the interaction between the control theory and digital system design communities for the success of MPC in fast embedded systems.

Inspec keywords: predictive control; field programmable gate arrays; pipeline processing; quadratic programming

Other keywords: real-time constraint; pipelined architecture; quadratic programming problem; deeply pipelined field-programmable gate array implementation; linear processes; optimisation-based scheme; model predictive control; primal-dual interior-point method; digital system design; optimal control; parallel computational channels; general purpose microprocessor; fast embedded systems; customised QP solver

Subjects: Logic and switching circuits; Optimal control; Parallel architecture; Multiprocessing systems

References

    1. 1)
    2. 2)
      • Ling, K.-V., Maciejowski, J.M., Wu, B.F.: `Multiplexed model predictive control', Proc. 16th IFAC World Congress, July 2005, Prague, Czech Republic.
    3. 3)
    4. 4)
      • Richards, A.G., Ling, K.-V., Maciejowski, J.M.: `Robust multiplexed model predictive control', Proc. European Control Conf., July 2007, Kos, Greece, p. 441–446.
    5. 5)
      • Bleris, L.G., Vouzis, P.D., Arnold, M.G., Kothare, M.V.: `A co-processor FPGA platform for the implementation of real-time model predictive control', Proc. American Control Conf., June 2006, Minneapolis, USA, p. 1912–1917.
    6. 6)
      • Wu, C.-H., Memik, S.O., Mehrotra, S.: `FPGA implementation of the interior-point algorithm with application to collision detection', Proc. 17th IEEE Symp. on Field Programmable Custom Computing Machines, April 2009, Napa, CA, USA, p. 295–298.
    7. 7)
      • J.M. Maciejowski . (2002) Predictive control with constraints.
    8. 8)
      • Knagge, G., Wills, A., Mills, A., Ninnes, B.: `ASIC and FPGA implementation strategies for model predictive control', Proc. European Control Conf., August 2009, Budapest, Hungary.
    9. 9)
    10. 10)
      • Koh, S.L.: `Solving interior point method on a FPGA', 2009, Master's, Nanyang Technological University, Singapore.
    11. 11)
      • L.D. Re , L. Glielmo , C. Guardiola , I. Kolmanovsky . (2010) Automotive model predictive control: models, methods and applications, ser. Lecture Notes in Control and Information Sciences.
    12. 12)
    13. 13)
    14. 14)
      • B. Fisher . (1996) Polynomial based iteration methods for symmetric linear systems.
    15. 15)
      • Shahzad, A., Kerrigan, E.C., Constantinides, G.A.: `A fast well-conditioned interior point method for predictive control', Proc. 49th IEEE Conf. on Decision and Control, December 2010, Atlanta, Georgia, USA, p. 508–513.
    16. 16)
    17. 17)
      • Vahidi, A., Stefanopoulou, A.G., Peng, H.: `Model predictive control for starvation prevention in a hybrid fuel cell system', Proc. American Control Conf., June 2004, Boston, MA, USA, p. 834–839.
    18. 18)
      • G.F. Franklin , J.D. Powell , M. Workman . (1997) Digital control of dynamic systems.
    19. 19)
      • J. Nocedal , S.J. Wright . (1999) Numerical optimization.
    20. 20)
      • S. Wright . (1997) Primal-dual interior-point methods.
    21. 21)
      • K.-V. Ling , W.K. Ho , B.F. Wu , A. Lo , H. Yan . Multiplexed MPC for multi-zone thermal processing in semiconductor manufacturing. IEEE Trans. Control Syst. Technol. , 6 , 1371 - 1380
    22. 22)
      • Wright, S.J.: `Applying new optimization algorithms to model predictive control', Proc. Int. Conf. Chemical Process Control, January 1996, Tahoe City, CA, USA, p. 147–155.
    23. 23)
    24. 24)
    25. 25)
      • S. Boyd , L. Vandenberghe . (2004) Convex optimization.
    26. 26)
      • Bayliss, S., Bouganis, C.S., Constantinides, G.A.: `An FPGA implementation of the Simplex algorithm', Proc. Int. IEEE Conf. on Field Programmable Technology, December 2006, Bangkok, Thailand, p. 49–55.
    27. 27)
      • K.-V. Ling , J.M. Maciejowski , A.G. Richards , B.F. Wu . Multiplexed model predictive control.
    28. 28)
      • Lau, M.S., Yue, S.P., Ling, K.-V., Maciejowski, J.M.: `A comparison of interior point and active set methods for FPGA implementation of model predictive control', Proc. European Control Conf., August 2009, Budapest, Hungary, p. 156–160.
    29. 29)
    30. 30)
      • Ling, K.-V., Wu, B.F., Maciejowski, J.M.: `Embedded model predictive control (MPC) using a FPGA', Proc. 17th IFAC World Congress, July 2008, Seoul, Korea, p. 15250–15255.
    31. 31)
      • Lopes, A.R., Constantinides, G.A.: `A high throughput FPGA-based floating-point conjugate gradient implementation', Proc. 4th Int. Workshop on Applied Reconfigurable Computing, March 2008, London, UK, p. 75–86.
    32. 32)
    33. 33)
      • Core Generator guide. Xilinx. [Online]. 2010, available at: http://www.xilinx.com/itp/xilinx6/books/docs/cgn/cgn.pdf.
    34. 34)
      • Boland, D., Constantinides, G.A.: `An FPGA-based implementation of the MINRES algorithm', Proc. Int. Conf. on Field Programmable Logic and Applications, September 2008, Heidelberg, Germany, p. 379–384.
    35. 35)
      • Virtex-6 family overview. Xilinx. [Online]. 2010, available at: http://www.xilinx.com/support/documentation/data sheets/ds150.pdf.
    36. 36)
      • Boland, D., Constantinides, G.A.: `Optimising memory bandwidth use for matrix–vector multiplication in iterative methods', Proc. Int. Symp. on Applied Reconfigurable Computing, March 2010, Bangkok, Thailand, p. 169–181.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cta.2010.0441
Loading

Related content

content/journals/10.1049/iet-cta.2010.0441
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address