Model Predictive Controller using Interior Point and Ant Algorithm
Chapter
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Abstract
This chapter presents an adaption of the Ant System for implementing the optimization routine of the Model Predictive Controller. A hybrid optimization scheme for Model Predictive Control (MPC) is also proposed, comprising both Primal-Dual Interior-Point (PDIP) method used in [1] and the search heuristic based Ant System optimization methods developed in this chapter.
References
- 1.B.P. Nguyen, Y. Ho, Z. Wu, C.-K. Chui, Implementation of model predictive control with modified minimal model on low-power risc microcontrollers, in Proceedings of the Third Symposium on Information and Communication Technology, ser. SoICT ’12 (ACM, 2012), pp. 165–171Google Scholar
- 2.L. Wang, Model Predictive Control System Design and Implementation Using Matlab (Springer, 2009)Google Scholar
- 3.S. Mehrotra, On the implementation of a primal-dual interior point method. Siam J. Opt. 2(4), 575–601 (1992)MathSciNetCrossRefGoogle Scholar
- 4.S.J. Wright, Primal-Dual Interior-Point Methods (SIAM, 1997)Google Scholar
- 5.Y. Ye, M.J. Todd, S. Mizuno, An o (sqrt(n)l)-iteration homogeneous and self-dual linear programming algorithm. Math. Op. Res. 19(1), 53–67 (1994)CrossRefGoogle Scholar
- 6.M. Dorigo, V. Maniezzo, A. Colorni, Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. Part B (Cybernetics) 26(1), 29–41 (1996)CrossRefGoogle Scholar
- 7.Guidelines for the use of the C language in critical systems (MISRA, Mar 2013)Google Scholar
- 8.R.N. Bergman, L.S. Phillips, C. Cobelli, Physiologic evaluation of factors controlling glucose tolerance in man measurement of insulin sensitivity and beta-cell glucose sensitivity from the response to intravenous glucose. J. Clin. Invest. 68(6), 1456–1467 (1981)CrossRefGoogle Scholar
- 9.R.N. Bergman, Minimal model: perspective from 2005. Hormone Res. Paediatr. 64(Suppl. 3), 8–15 (2006)MathSciNetGoogle Scholar
- 10.M. Fisher, A semiclosed-loop algorithm for the control of blood glucose levels in diabetics. IEEE Trans. Bio-Med. Eng. 38(1), 57–61 (1991)CrossRefGoogle Scholar
- 11.S. Lynch, B. Bequette, Model predictive control of blood glucose in type i diabetics using subcutaneous glucose measurements, in Proceedings of the 2002 American Control Conference, vol. 5 (2002), pp. 4039–4043Google Scholar
- 12.C. Dalla Man, R.A. Rizza, C. Cobelli, Meal simulation model of the glucose-insulin system. IEEE Trans. Biomed. Eng. 54(10), 1740–1749 (2007)CrossRefGoogle Scholar
- 13.C. Dalla Man, D.M. Raimondo, R.A. Rizza, C. Cobelli, GIM, simulation software of meal glucose-insulin model. J. Diabetes Sci. Technol. 1(3), 323–330 (2007)CrossRefGoogle Scholar
- 14.L. Bleris, M. Kothare, Real-time implementation of model predictive control, in 2005 American Control Conference, vol. 6 (Portland, OR, USA, Aug 2005), pp. 4166–4171Google Scholar
- 15.L.G. Bleris, M.V. Kothare, Implementation of model predictive control for glucose regulation on a general purpose microprocessor, in Proceedings of the 44th IEEE Conference on Decision and Control (Seville, Spain, Dec 2005), pp. 5162–5167Google Scholar
- 16.A.K. Abbes, F. Bouani, M. Ksouri, A microcontroller implementation of constrained model predictive control. Int. J. Electr. Electr. Eng. 5(3), 199–206 (2006)Google Scholar
- 17.L. Magni, D.M. Raimondo, C.D. Man, M. Breton, S. Patek, G. De Nicolao, C. Cobelli, B.P. Kovatchev, Evaluating the efficacy of closed-loop glucose regulation via control-variability grid analysis. J. Diabetes Sci. Technol. 2(4), 630–635 (2008)CrossRefGoogle Scholar
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