A Parallel Quadratic Programming algorithm for Model Predictive Control

IFAC Proceedings Volumes (IFAC-PapersOnline)

Volumn 44, Issue 1 PART 1, 2011, Pages 1031-1039

  Brand, Matthew ^a   Shilpiekandula, Vijay ^a   Yao, Chen ^a   Bortoff, Scott A ^a   Nishiyama, Takehiro ^b   Yoshikawa, Shoji ^b   Iwasaki, Takashi ^b  

^a MITSUBISHI ELECTRIC RESEARCH LABORATORIES   (United States)

^b MITSUBISHI ELECTRIC CORPORATION   (Japan)

Author keywords

Model Predictive Control; Quadratic Programming; Reference tracking

Indexed keywords

ITERATIVE METHODS; MATLAB; PREDICTIVE CONTROL SYSTEMS; QUADRATIC PROGRAMMING; REAL TIME CONTROL;

FAST SOLUTIONS; PARALLEL IMPLEMENTATIONS; PARALLEL PROCESSOR; PARALLEL QUADRATIC PROGRAMMING; PARALLELIZATIONS; REAL-TIME IMPLEMENTATIONS; REFERENCE-TRACKING; SIMULATION EXAMPLE;

MODEL PREDICTIVE CONTROL;

EID: 84866744848     PISSN: 14746670     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3182/20110828-6-IT-1002.03222     Document Type: Conference Paper

References (29)

1. Bageshwar, V., Garrard, W., and Rajamani, R. (2004). Model predictive control of transitional maneuvers for adaptive cruise control vehicles. IEEE Transactions on Vehicular Technology, 53 (5), 1573-1585.
2. Benzi, M., Golub, G.H., and Liesen, J. (2005). Numerical solution of saddle point problems. Acta Numerica, 1, 1-137.
3. Bertero, M., Lanteri, H., and Zanni, L. (2007). Iterative image reconstruction: A point of view. In Proceedings of the Interdisciplinary Workshop on Mathematical Methods in Biomedical Imaging and Intensity-Modulated Radiation Therapy (IMRT).
4. Borrelli, F. (2003). Constrained Optimal Control of Linear and Hybrid Systems. Sringer, Lecture Notes in Control and Information Sciences.
5. Borrelli, F., Bemporad, A., and Morari., M. (2010). Predictive control. Springer, In Press. http://www.mpc.berkeley.edu.
6. Brand, M. and Chen, D. (2011). Parallel quadratic programming for image processing. In Reviews.
7. Camacho, E.F. and Bordons, C. (2004). Model Predictive Control. Springer.
8. Dostl, Z. (2009). Optimal Quadratic Programming Algorithms: With Applications to Variational Inequalities. Springer Publishing Company, Incorporated.
9. Eggermont, P. (1990). Multiplicative iterative algorithms for convex programming. J. Linear Algebra and Applications, 130, 25-42.
10. Ferreau, H. (2006). An online active set strategy for fast solution of parametric quadratic programs with applications to predictive engine control. Master's Thesis, University of Heidelberg.
11. Gondzio, J. and Grothey, A. (2007). Parallel interior-point solver for structured quadratic programs: Application to financial planning problems. Annals of Operations Research, 152, 319-339.
12. Gondzio, J. and Sarkissian, R. (2000). Parallel interiorpoint solver for structured linear programs. Mathematical Programming, 96, 561-584.
13. Heath, W. and Wills, A. (2007). Zames-falb multipliers for quadratic programming. IEEE Transactions on Automatic Control, 52, 1948-1951.
14. Kirk, D. and Hwu, W. (2010). Programming Massively Parallel Processing. Morgan Kaufmann.
15. Lee, D.D. and Seung, H.S. (1999). Learning the parts of objects with nonnegative matrix factorization. Nature, 401, 788-791.
16. Luo, Z.Q. and Tseng, P. (1992). On the linear convergence of descent methods for convex essentially smooth minimization. SIAM J. Control and Optimization, 30(2), 408-425.
17. Milman, R. and Davison, E.J. (2008). A fast mpc algorithm using nonfeasible active set methods. Journal of optimization theory and applications, 139, 591-616.
18. Negenborn, R.R., Schutter, B.D., and Hellendoorn, H.(2006). Multi-agent model predictive control for transportation networks: Serial versus parallel schemes. Proceedings of the 12th IFAC Symposium on Information Control Problems in Manufacturing (INCOM'2006), 339-344.
19. Qin, S.J. and Badgwell, T.A. (2003). A survey of industrial model predictive control technology. Control Engineering Practice, 11(7), 733-764.
20. Rao, C.V., Wright, S.J., and Rawlings, J.B. (2004). Application of interior point methods to model predictive control. Journal of optimization theory and applications, 90(3), 723-757.
21. Rawlings, J.B. and Mayne, D.Q. (2009). Model Predictive Control: Theory and Design. Nob Hill Publishing, LLC.
22. Richter, S., Jones, C., and Morari, M. (2009). Real-time input-constrained mpc using fast gradient methods. In Proc. IEEE Conference on Decision and Control, 7387-7393. Shanghai, China.
23. Rossiter, J.A. (2003). Model-based Predictive Control: A Practical Approach. Prentice Hall International.
24. Sha, F. et al. (2007). Multiplicative updates for nonnegative quadratic programming. Neural Computation, 19(8), 2004-2031.
25. Shepp, L. and Vardi, Y. (1982). Maximum likelihood reconstruction for emission tomography. IEEE Trans. Medical Imaging 1, 1, 113-122.
26. Stratton, J., Stone, S., and Hwu, W. (2008). Mcudal an efficient implementation of cuda kernels for multicore cpus. In Proc. 21st International Workshop on Languages and Compilers for Parallel Computing, 16-30.
27. Wang, L. (2009). Model Predictive Control System Design and Implementation using MATLAB. Springer-Verlag.
28. Wang, Y. and Boyd, S. (2010). Fast model predictive control using online optimization. IEEE Transactions on Control Systems Technology, 18(2), 267-278.
29. Zeilinger, M.N., Jones, C., and Morari, M. (2009). Realtime mpc-stability through robust design. In Proc. IEEE Conference on Decision and Control, 3980-3986. Shanghai, China.