Eingebettete Optimierung in der Regelungstechnik – Grundlagen und Herausforderungen

Literatur

1. M. Abdollahpouri, G. Takács and B. Rohal’-Ilkiv. Real-time moving horizon estimation for a vibrating active cantilever. Mechanical Systems and Signal Processing, 86:1–15, 2017.10.1016/j.ymssp.2016.09.028Search in Google Scholar

2. A.V. Aho, M.S. Lam, R. Sethi and J.D. Ullman. Compilers: Principles, Techniques, & Tools. Addison Wesley, 2nd edition, 2006.Search in Google Scholar

3. A. Airan, M. Bhushan and S. Bhartiya. Linear machine solution to point location problem. IEEE Transactions on Automatic Control, 62(3):1403–1410, 2017.10.1109/TAC.2016.2573201Search in Google Scholar

4. B.M. Åkesson, H.T. Toivonen, J.B. Waller and R.H. Nyström. Neural network approximation of a nonlinear model predictive controller applied to a pH neutralization process. Computers & Chemical Engineering, 29(2):323–335, 2005.10.1016/j.compchemeng.2004.09.023Search in Google Scholar

5. T. Albin, D. Ritter, D. Abel, N. Liberda, R. Quirynen and M. Diehl. Nonlinear MPC for a two-stage turbocharged gasoline engine airpath. In Proc. 54th IEEE Conference on Decision and Control (CDC), S. 849–856, 2015.10.1109/CDC.2015.7402335Search in Google Scholar

6. T. Albin, D. Ritter, N. Liberda, R. Quirynen and M. Diehl. In-vehicle realization of nonlinear MPC for gasoline two-stage turbocharging airpath control. IEEE Transactions on Control Systems Technology, 26(5):1606–1618, 2018.10.1109/TCST.2017.2724020Search in Google Scholar

7. A. Alessio and A. Bemporad. A survey on explicit model predictive control. In L. Magni, D.M. Raimondo and F. Allgöwer, editors, Nonlinear Model Predictive Control, S. 345–369. Springer, Berlin, 2009.10.1007/978-3-642-01094-1_29Search in Google Scholar

8. G. Allaire. Numerical Analysis and Optimization. Oxford University Press, Oxford, 2007.Search in Google Scholar

9. J. Andersson, J. Åkesson and M. Diehl. CasADi: A symbolic package for automatic differentiation and optimal control. In Recent Advances in Algorithmic Differentiation, S. 297–307, 2012.10.1007/978-3-642-30023-3_27Search in Google Scholar

10. A. Aswani, H. Gonzalez, S. Sastry and C. Tomlin. Provably safe and robust learning-based model predictive control. Automatica, 49(5):1216–1226, 2013.10.1016/j.automatica.2013.02.003Search in Google Scholar

11. M. Athans and P.L. Falb. Optimal Control. McGraw-Hill, New York, 1966.Search in Google Scholar

12. H. Attouch, J. Bolte, P. Redont and A. Soubeyran. Proximal alternating minimization and projection methods for nonconvex problems: An approach based on the Kurdyka-Lojasiewicz inequality. Mathematics of Operations Research, 35(2):438–457, 2010.10.1287/moor.1100.0449Search in Google Scholar

13. T. Bächle, K. Graichen, M. Buchholz and K. Dietmayer. Model predictive control allocation in electric vehicle drive trains. In Proc. 4th IFAC Workshop on Engine and Powertrain Control, Simulation and Modeling (E-COSM 2015), S. 335–340, 2015.10.1016/j.ifacol.2015.10.048Search in Google Scholar

14. T.A. Badgwell and S.J. Qin. Model-predictive control in practice. In Encyclopedia of Systems and Control, S. 756–760, 2015.10.1007/978-1-4471-5058-9_8Search in Google Scholar

15. S. Bak, D.K. Chivukula, O. Adekunle, M. Sun, M. Caccamo and L. Sha. The system-level simplex architecture for improved real-time embedded system safety. In Proc. 15th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS 2009), S. 99–107, 2009.10.1109/RTAS.2009.20Search in Google Scholar

16. M. Baotic. An efficient algorithm for multi-parametric quadratic programming. Technical report, ETH Zürich, 2002. ATU02-05.Search in Google Scholar

17. M. Baotic, F. Borrelli, A. Bemporad and M. Morari. Efficient on-line computation of constrained optimal control. SIAM Journal on Control and Optimization, 47(5):2470–2489, 2008.10.1137/060659314Search in Google Scholar

18. G. Batista, G. Takács and B. Rohal’-Ilkiv. Application aspects of active-set quadratic programming in real-time embedded model predictive vibration control. In Proc. 20th IFAC World Congress, S. 11625–11631, 2017.Search in Google Scholar

19. A. Bemporad. Hybrid Toolbox – User’s Guide, 2004. http://cse.lab.imtlucca.it/ bemporad/hybrid/toolbox.Search in Google Scholar

20. A. Bemporad, D. Bernardini, M. Livshiz and B. Pattipati. Supervisory model predictive control of a powertrain with a continuously variable transmission. In In WCX: SAE World Congress Experience, 2018.10.4271/2018-01-0860Search in Google Scholar

21. A. Bemporad, D. Bernardini, R. Long and J. Verdejo. Model predictive control of turbocharged gasoline engines for mass production. In In WCX: SAE World Congress Experience, 2018.10.4271/2018-01-0875Search in Google Scholar

22. A. Bemporad, F. Borrelli and M. Morari. Model predictive control based on linear programming – the explicit solution. IEEE Transactions on Automatic Control, 47(12):1974–1985, 2002.10.1109/TAC.2002.805688Search in Google Scholar

23. A. Bemporad, S. Di Cairano, E. Henriksson and K.H. Johansson. Hybrid model predictive control based on wireless sensor feedback: An experimental study. International Journal of Robust and Nonlinear Control: IFAC-Affiliated Journal, 20(2):209–225, 2010.10.1002/rnc.1451Search in Google Scholar

24. A. Bemporad, M. Morari, V. Dua and E.N. Pistikopoulos. The explicit solution of model predictive control via multiparametric programming. In Proc. American Control Conference (ACC), S. 872–876, 2000.10.1109/ACC.2000.876624Search in Google Scholar

25. A. Bemporad, M. Morari, V. Dua and E.N. Pistikopoulos. The explicit linear quadratic regulator for constrained systems. Automatica, 38:3–20, 2002.10.1016/S0005-1098(01)00174-1Search in Google Scholar

26. A. Bemporad, N.L. Ricker and M. Morari. Model Predictive Control Toolbox User’s Guide. The Mathworks Inc., 2018.Search in Google Scholar

27. D.P. Bertsekas. Constrained Optimization and Lagrange Multiplier Methods. Athena Scientific, Belmont, Massachusetts, USA, 1996.Search in Google Scholar

28. A. Bestler and K. Graichen. Distributed model predictive control for continuous-time nonlinear systems based on suboptimal ADMM. Optimal Control Applications and Methods (im Druck), 2018. ArXiv Preprint: https://arxiv.org/abs/1706.09599.10.1002/oca.2459Search in Google Scholar

29. J. Bethge, M. Morabito, J. Matschek and R. Findeisen. Multi-mode learning supported model predictive control with guarantees. In Proc. 6th IFAC Conference on Nonlinear Model Predictive Control (NMPC), 2018.10.1016/j.ifacol.2018.11.037Search in Google Scholar

30. E.G. Birgin and J.M. Martinez. Improving ultimate convergence of an augmented Lagrangian method. Optimization Methods and Software, 23(2):177–195, 2008.10.1080/10556780701577730Search in Google Scholar

31. T.I. Bø and T.A. Johansen. Dynamic safety constraints by scenario based economic model predictive control. In Proc. 19th IFAC World Congress, S. 9412–9418, 2014.10.3182/20140824-6-ZA-1003.00582Search in Google Scholar

32. F. Borrelli, M. Baotic, J. Pekar and G. Stewart. On the computation of linear model predictive control laws. Automatica, 46(6):1035–1041, 2010.10.1016/j.automatica.2010.02.031Search in Google Scholar

33. S. Boyd, N. Parikh, E. Chu, B. Peleato and J. Eckstein. Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends in Machine Learning, 3(1):1–122, 2011.10.1561/9781601984616Search in Google Scholar

34. S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press, Cambridge, UK, 2004.10.1017/CBO9780511804441Search in Google Scholar

35. J. Bryzek. Roadmap for the Trillion Sensor Universe. iNEMI Spring Member Meeting and Webinar, Berkeley, 2014.Search in Google Scholar

36. J. Bryzek. Trillion sensors: Foundation for abundance, exponential organizations, internet of everything and health. Sensor Magazin, 5:6–10, 2014.Search in Google Scholar

37. S. Di Cairano, D. Yanakiev, A. Bemporad, I.V. Kolmanovsky and D. Hrovat. Model predictive idle speed control: Design, analysis, and experimental evaluation. IEEE Transactions Control Systems Technology, 20(1):84–97, 2012.10.1109/TCST.2011.2112361Search in Google Scholar

38. L. Cavagnari, L. Magni and R. Scattolini. Neural network implementation of nonlinear receding-horizon control. Neural Computing & Applications, 8:86–92, 1999.10.1007/s005210050010Search in Google Scholar

39. G. Cimini and A. Bemporad. Exact complexity certification of active-set methods for quadratic programming. IEEE Transactions on Automatic Control, 62(12):6094–6109, 2017.10.1109/TAC.2017.2696742Search in Google Scholar

40. G. Cimini, A. Bemporad and D. Bernardini. ODYS QP Solver. ODYS S.r.l., 2017.Search in Google Scholar

41. A.R. Conn, G.I.M. Gould and P.L. Toint. LANCELOT. A Fortran package for large-scale nonlinear optimization (Release A). Springer, 1992.10.1007/978-3-662-12211-2Search in Google Scholar

42. D. Munoz de la Pena, T. Alamo, A. Bemporad and E.F. Camacho. A dynamic programming approach for determining the explicit solution of linear MPC controllers. In Proc. 43rd IEEE Conference on Decision and Control (CDC), S. 2479–2484, 2004.10.1109/CDC.2004.1428785Search in Google Scholar

43. M. Diehl, H.G. Bock, J.-P. Schlöder, R. Findeisen, Z. Nagy and F. Allgöwer. Real-time optimization and nonlinear model predictive control of processes governed by differential-algebraic equations. Journal of Process Control, 12(4):577–585, 2002.10.1016/S0959-1524(01)00023-3Search in Google Scholar

44. M. Diehl, R. Findeisen, F. Allgöwer, H.G. Bock and J.P. Schlöder. Nominal stability of real-time iteration scheme for nonlinear model predictive control. IEE Proceedings Control Theory and Applications, 152(3):296–308, 2005.10.1049/ip-cta:20040008Search in Google Scholar

45. A. Domahidi, E. Chu and S. Boyd. ECOS: An SOCP solver for embedded systems. In Proc. 2013 European Control Conference (ECC), S. 3071–3076, 2013.10.23919/ECC.2013.6669541Search in Google Scholar

46. A. Domahidi, A. Zgraggen, M.N. Zeilinger, M. Morari and C.N. Jones. Efficient interior point methods for multistage problems arising in receding horizon control. In Proc. 51th IEEE Conference on Decision and Control (CDC), S. 668–674, 2012.10.1109/CDC.2012.6426855Search in Google Scholar

47. L.F. Domínguez and E.N. Pistikopoulos. A novel mp-NLP algorithm for explicit/multi-parametric NMPC. Proc. of the 8th IFAC Symposium on Nonlinear Control Systems, 43(14):539–544, 2010.10.3182/20100901-3-IT-2016.00073Search in Google Scholar

48. A. Draeger, S. Engell and H. Ranke. Model predictive control using neural networks. IEEE Control Systems, 15(5):61–66, 1995.10.1109/37.466261Search in Google Scholar

49. V. Duchaine, S. Bouchard and C.M. Gosselin. Computationally efficient predictive robot control. IEEE/ASME Transactions on Mechatronics, 12(5):570–578, 2007.10.1109/TMECH.2007.905722Search in Google Scholar

50. T. Englert and K. Graichen. Model predictive torque control of PMSMs for high performance applications. Control Engineering Practice, 81(12):43–54, 2018.10.1016/j.conengprac.2018.08.023Search in Google Scholar

51. T. Englert, A. Völz, F. Mesmer, S. Rhein and K. Graichen. A software framework for embedded nonlinear model predictive control using a gradient-based augmented Lagrangian approach (GRAMPC). Optimization and Engineering (submitted), 2018. Preprint: arXiv:1805.01633.10.1007/s11081-018-9417-2Search in Google Scholar

52. F. Farokhi, I. Shames and K.H. Johansson. Distributed MPC via dual decomposition and alternative direction method of multipliers. In J.M. Maestro and R.R. Negenborn, editors, Distributed Model Predictive Control Made Easy, S. 115–131. Springer, Dordrecht, 2014.10.1007/978-94-007-7006-5_7Search in Google Scholar

53. T. Faulwasser, B. Kern, P. Varutti and R. Findeisen. Prädiktive Regelung nichtlinearer Systeme unter asynchronen Mess- und Stellsignalen. at–Automatisierungstechnik, 57(6):279–286, 2009.10.1524/auto.2009.0776Search in Google Scholar

54. C. Feller and C. Ebenbauer. A stabilizing iteration scheme for model predictive control based on relaxed barrier functions. Automatica, 80:328–339, 2017.10.1016/j.automatica.2017.02.001Search in Google Scholar

55. C. Feller, T.A. Johansen and S. Olaru. An improved algorithm for combinatorial multi-parametric quadratic programming. Automatica, 45(5):1370–1376, 2013.10.1016/j.automatica.2013.02.022Search in Google Scholar

56. H.J. Ferreau, S. Almér, R. Verschueren, M. Diehl, D. Frick, A. Domahidi, J.L. Jerez, G. Stathopoulos and C. Jones. Embedded optimization methods for industrial automatic control. In Proc. 20th IFAC World Congress, S. 13736–13751, 2017.Search in Google Scholar

57. H.J. Ferreau, H.G. Bock and M. Diehl. An online active set strategy to overcome the limitations of explicit MPC. International Journal of Robust and Nonlinear Control, 18(8):816–830, 2008.10.1002/rnc.1251Search in Google Scholar

58. H.J. Ferreau, C. Kirches, A. Potschka, H.G. Bock and M. Diehl. qpOASES: A parametric active-set algorithm for quadratic programming. Mathematical Programming Computation, 6(4):327–363, 2014.10.1007/s12532-014-0071-1Search in Google Scholar

59. A.V. Fiacco. Introduction to Sensitivity and Stability Analysis in Nonlinear Programming. Academic Press, 1983.Search in Google Scholar

60. P.K. Findeisen. Moving horizon state estimation of discrete time systems. Master’s thesis, University of Wisconsin–Madison, 1997.Search in Google Scholar

61. R. Findeisen. Nonlinear Model Predictive Control: A Sampled Data Feedback Perspective. VDI-Verlag, 2005.Search in Google Scholar

62. R. Findeisen and F. Allgöwer. An introduction to nonlinear model predictive control. In Proc. 21st Benelux Meeting on Systems and Control, volume 11, S. 119–141, 2002.Search in Google Scholar

63. R. Findeisen and F. Allgöwer. Computational delay in nonlinear model predictive control. In Proc. International Symposium on Advanced Control of Chemical Processes (ADCHEM), S. 427–432, 2004.10.1016/S1474-6670(17)38769-4Search in Google Scholar

64. R. Findeisen, L. Grüne, J. Pannek and P. Varutti. Robustness of prediction based delay compensation for nonlinear systems. In Proc. 18th IFAC World Congress, S. 203–208, 2011.10.3182/20110828-6-IT-1002.03090Search in Google Scholar

65. R. Findeisen, L. Imsland, F. Allgöwer and B.A. Foss. State and output feedback nonlinear model predictive control: An overview. European Journal of Control, 9(2–3):190–206, 2003.10.3166/ejc.9.190-206Search in Google Scholar

66. R. Findeisen and P. Varutti. Stabilizing nonlinear predictive control over nondeterministic communication networks. In L. Magni, D.M. Raimondo and F. Allgöwer, editors, Nonlinear Model Predictive Control, S. 167–179. Springer, Berlin, 2009.10.1007/978-3-642-01094-1_13Search in Google Scholar

67. A. Forsgren, P.E. Gill and M.H. Wright. Interior methods for nonlinear optimization. SIAM Review, 44(4):525–597, 2002.10.1137/S0036144502414942Search in Google Scholar

68. M. Fortin and R. Glowinski, editors. Augmented Lagrangian Methods: Applications to the Solution of Boundary-Value Problems. North-Holland, Amsterdam, 1983.Search in Google Scholar

69. G. Frison, H.B. Sørensen, B. Dammann and J.B. Jørgensen. High-performance small-scale solvers for linear model predictive control. In Proc. European Control Conference (ECC), S. 128–133, 2014.10.1109/ECC.2014.6862490Search in Google Scholar

70. D. Gagliardi, T. Othsuka and L. del Re. Direct C/GMRES control of the air path of a Diesel engine. In Proc. 19th IFAC World Congress, S. 3000–3005, 2014.10.3182/20140824-6-ZA-1003.02481Search in Google Scholar

71. T. Geyer, G. Papafotiou and M. Morari. Model predictive direct torque control – Part I: Concept, algorithm, and analysis. IEEE Transactions on Industrial Electronics, 56(6):1894–1905, 2009.10.1109/TIE.2008.2007030Search in Google Scholar

72. M. Gharbi, C. Feller and C. Ebenbauer. A first step toward moving horizon state estimation based on relaxed logarithmic barrier functions. In Proc. 56th IEEE Conference on Decision and Control (CDC), S. 2188–2194, Dez. 2017.10.1109/CDC.2017.8263969Search in Google Scholar

73. H.G. Giesseler, M. Kopf, P. Varutti, T. Faulwasser and R. Findeisen. Model predictive control for gust load alleviation. In Proc. 4th IFAC Conference on Nonlinear Model Predictive Control (NMPC), S. 27–32, 2012.10.3182/20120823-5-NL-3013.00049Search in Google Scholar

74. P. Giselsson and S. Boyd. Linear convergence and metric selection in Douglas-Rachford splitting and ADMM. IEEE Transactions on Automatic Control, 62(2):532–544, 2017.10.1109/TAC.2016.2564160Search in Google Scholar

75. G. Goodwin, M. Seron and J. De Doná. Constrained control and estimation: an optimisation approach. Springer, 2006.10.1007/b138145Search in Google Scholar

76. K. Graichen and A. Kugi. Stability and incremental improvement of suboptimal MPC without terminal constraints. IEEE Transactions on Automatic Control, 55(11):2576–2580, 2010.10.1109/TAC.2010.2057912Search in Google Scholar

77. M. Grant and S. Boyd. CVX: Matlab software for disciplined convex programming, version 2.1. http://cvxr.com/cvx, 2014.Search in Google Scholar

78. L. Grüne and J. Pannek. Analysis of unconstrained NMPC schemes with incomplete optimization. In Proc. 8th IFAC Symposium on Nonlinear Control Systems (NOLCOS), S. 238–243, 2010.10.3182/20100901-3-IT-2016.00124Search in Google Scholar

79. L. Grüne and J. Pannek. Nonlinear Model Predictive Control. Springer, 2017.10.1007/978-3-319-46024-6Search in Google Scholar

80. J. Guddat, G. Guerra Vazquez and H.Th. Jongen Parametric Optimization: Singularities, Pathfollowing and Jumps. John Wiley, New York, 1990.10.1007/978-3-663-12160-2Search in Google Scholar

81. M. Gulan, G. Takács, N.A. Nguyen, S. Olaru, P. Rodriguez-Ayerbe and B. Rohal’-Ilkiv. Embedded linear model predictive control for 8-bit microcontrollers via convex lifting. IFAC-PapersOnLine, 50(1):10697–10704, 2017. 20th IFAC World Congress.10.1016/j.ifacol.2017.08.2220Search in Google Scholar

82. A. Gupta, S. Bhartiya and P.S.V. Nataraj. A novel approach to multiparametric quadratic programming. Automatica, 47(9):2112–2117, 2011.10.1016/j.automatica.2011.06.019Search in Google Scholar

83. W. Hager. Multiplier methods for nonlinear optimal control. SIAM Journal on Numerical Analysis, 27(4):1061–1080, 1990.10.1137/0727063Search in Google Scholar

84. K. Harder, M. Buchholz, J. Niemeyer, J. Remele and K. Graichen. Nonlinear MPC with emission control for a real-world heavy-duty diesel engine. In Proc. 2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM 2017), S. 1768–1773, 2017.10.1109/AIM.2017.8014274Search in Google Scholar

85. K. Harder, M. Buchholz, J. Niemeyer, J. Remele and K. Graichen. A real-time nonlinear MPC scheme with emission constraints for heavy-duty diesel engines. In Proc. 2017 American Control Conference (ACC), S. 240–245, 2017.10.23919/ACC.2017.7962960Search in Google Scholar

86. R.F. Hartl, S.P. Sethi and R.G. Vickson. A survey of the maximum principles for optimal control problems with state constraints. SIAM Review, 37:181–218, 1995.10.1137/1037043Search in Google Scholar

87. S. Hentzelt and K. Graichen. Experimental results for distributed model predictive control applied to a water distribution system. In Proc. 2014 IEEE Multi-Conference on Systems and Control (MSC), S. 1100–1106, 2014.Search in Google Scholar

88. M. Herceg, C.N. Jones, M. Kvasnica and M. Morari. Enumeration-based approach to solving parametric linear complementarity problems. Automatica, 62:243–248, 2015.10.1016/j.automatica.2015.09.019Search in Google Scholar

89. M. Herceg, M. Kvasnica, C.N. Jones and M. Morari. Multi-parametric toolbox 3.0. In Proc. 2013 European Control Conference (ECC), S. 502–510, 2013.10.23919/ECC.2013.6669862Search in Google Scholar

90. M. Hertneck, J. Köhler, S. Trimpe and F. Allgöwer. Learning an approximate model predictive controller with guarantees. IEEE Control Systems Letters, 2(3):543–548, 2018.10.1109/LCSYS.2018.2843682Search in Google Scholar

91. M. Honek, M. Kvasnica, A. Szucs, P. Simoncic, M. Fikar and B. Rohal’-Ilkiv. A low-complexity explicit MPC controller for AFR control. Control Engineering Practice, 42:118–127, 2015.10.1016/j.conengprac.2015.05.009Search in Google Scholar

92. J.J. Hopfield and D.W. Tank. “Neural” computation of decisions in optimization problems. Biological Cybernetics, 52(3):141–152, 1985.10.1007/BF00339943Search in Google Scholar PubMed

93. B. Houska, H.J. Ferreau and M. Diehl. ACADO Toolkit – An Open Source Framework for Automatic Control and Dynamic Optimization. Optimal Control Applications and Methods, 32:289–312, 2011.10.1002/oca.939Search in Google Scholar

94. D. Hrovat, S. Di Cairano, H.E. Tseng and I.V. Kolmanovsky. The development of model predictive control in automotive industry: A survey. In Proc. 2012 IEEE International Conference on Control Applications (CCA), S. 295–302, 2012.10.1109/CCA.2012.6402735Search in Google Scholar

95. J.-Q. Huang and F.L. Lewis. Neural-network predictive control for nonlinear dynamic systems with time-delay. IEEE Transactions on Neural Networks, 14(2):377–389, 2003.10.1109/TNN.2003.809424Search in Google Scholar PubMed

96. B. Huyck, L. Callebaut, F. Logist, H.J. Ferreau, M. Diehl, J. De Brabanter, J. Van Impe and B. De Moor. Implementation and experimental validation of classic MPC on programmable logic controllers. In Proc. 20th Mediterranean Conference on Control & Automation (MED), S. 679–684, 2012.10.1109/MED.2012.6265716Search in Google Scholar

97. B. Huyck, H.J. Ferreau, M. Diehl, J. De Brabanter, J. Van Impe, B. De Moor and F. Logist. Towards online model predictive control on a programmable logic controller: practical considerations. Mathematical Problems in Engineering, 2012. Article ID 912603.10.1155/2012/912603Search in Google Scholar

98. J.L. Jerez, P.J. Goulart, S. Richter, G.A. Constantinides, E.C. Kerrigan and M. Morari. Embedded predictive control on an FPGA using the fast gradient method. In Proc. 2013 European Control Conference (ECC), S. 3614–3620, Juli 2013.10.23919/ECC.2013.6669598Search in Google Scholar

99. J.L. Jerez, P.J. Goulart, S. Richter, G.A. Constantinides, E.C. Kerrigan and M. Morari. Embedded online optimization for model predictive control at megahertz rates. IEEE Transactions on Automatic Control, 59(12):3238–3251, 2014.10.1109/TAC.2014.2351991Search in Google Scholar

100. T.A. Johansen. Approximate explicit receding horizon control of constrained nonlinear systems. Automatica, 40(2):293–300, 2004.10.1016/j.automatica.2003.09.021Search in Google Scholar

101. T.A. Johansen. Toward dependable embedded model predictive control. IEEE Systems Journal, 11(2):1208–1219, 2017.10.1109/JSYST.2014.2368129Search in Google Scholar

102. T.A. Johansen, I. Petersen and O. Slupphaug. On explicit suboptimal LQR with state and input constraints. In Proc. 39th IEEE Conference on Decision and Control (CDC), S. 662–667, 2000.Search in Google Scholar

103. T.A. Johansen. On multi-parametric nonlinear programming and explicit nonlinear model predictive control. In Proc. 41st Conference on Decision and Control (CDC), S. 2768–2773, 2002.Search in Google Scholar

104. C.N. Jones and M. Morari. Polytopic approximation of explicit model predictive controllers. IEEE Transactions on Automatic Control, 55(11):2542–2553, 2010.10.1109/TAC.2010.2047437Search in Google Scholar

105. M. Jost, G. Pannocchia and M. Mönnigmann. Fast calculation of tube-based model predictive control laws for constrained linear systems with bounded disturbance. In Proc. 54th IEEE Conference on Decision and Control (CDC), S. 3651–3656, 2015.10.1109/CDC.2015.7402785Search in Google Scholar

106. M. Jost, G. Pannocchia and M. Mönnigmann. Online constraint removal: accelerating MPC with a Lyapunov function. Automatica, 57:164–169, 2015.10.1016/j.automatica.2015.04.014Search in Google Scholar

107. M. Jost, G. Pannocchia and M. Mönnigmann. Constraint removal in linear MPC: an improved criterion and complexity analysis. In Proc. 2016 European Control Conference (ECC), S. 752–757, 2016.10.1109/ECC.2016.7810379Search in Google Scholar

108. M. Jost, G. Pannocchia and M. Mönnigmann. Accelerating linear model predictive control by constraint removal. European Journal of Control, 35:42–49, 2017.10.1016/j.ejcon.2017.02.003Search in Google Scholar

109. M. Jost, M. Schulze Darup and M. Mönnigmann. Optimal and suboptimal event-triggering in linear model predictive control. In Proc. European Control Conference (ECC), S. 1153–1158, 2015.10.1109/ECC.2015.7330695Search in Google Scholar

110. R.E. Kalman. Contributions to the theory of optimal control. Boletin de la Sociedad Matematica Mexicana, 5:102–119, 1960.Search in Google Scholar

111. M. Kang, T. Shen and X. Jiao. Continuation/GMRES method based bonlinear model predictive control for IC engines. In Proc. 19th IFAC World Congress, S. 5697–5702, 2014.10.3182/20140824-6-ZA-1003.00488Search in Google Scholar

112. B. Käpernick and K. Graichen. The gradient based nonlinear model predictive control software GRAMPC. In Proc. 2014 European Control Conference (ECC), S. 1170–1175, 2014.10.1109/ECC.2014.6862353Search in Google Scholar

113. B. Käpernick and K. Graichen. Nichtlineare modellprädiktive Regelung auf SPS. atp – Automatisierungstechnische Praxis, 56:38–46, 2014.10.17560/atp.v56i03.460Search in Google Scholar

114. B. Käpernick and K. Graichen. Nonlinear model predictive control based on constraint transformation. Optimal Control Applications and Methods, 37(4):807–828, 2016.10.1002/oca.2215Search in Google Scholar

115. B. Karg and S. Lucia. Deep learning-based embedded mixedinteger model predictive control. In Proc. of the European Control Conference, S. 2075–2080, 2018.10.23919/ECC.2018.8550234Search in Google Scholar

116. M. Kastsian, M. Jost and M. Mönnigmann. Ansätze zur Berechnung und schnellen Auswertung expliziter modellprädiktiver Regelgesetze. at–Automatisierungstechnik, 60(8):433–441, 2012.10.1524/auto.2012.1020Search in Google Scholar

117. B. Khusainov, E.C. Kerrigan, A. Suardi and G.A. Constantinidis. Nonlinear predictive control on a heterogeneous computing plattform. In Proc. 20th IFAC World Congress, IFAC PapersOnLine 50-1, S. 11877–11882, 2017.10.1016/j.ifacol.2017.08.1413Search in Google Scholar

118. N. Kirsch, N. Alibeji and N. Sharma. Nonlinear model predictive control of functional electrical stimulation. Control Engineering Practice, 58:319–331, 2017.10.1016/j.conengprac.2016.03.005Search in Google Scholar

119. M. Klauco, M. Kaluz and M. Kvasnica. Real-time implementation of an explicit MPC-based reference governor for control of a magnetic levitation system. Control Engineering Practice, 60:99–105, 2017.10.1016/j.conengprac.2017.01.001Search in Google Scholar

120. M. Kögel and R. Findeisen. Cooperative distributed MPC using the alternating direction multiplier method. In Proc. 8th IFAC Symposium on Advanced Control of Chemical Processes (ADCHEM), S. 445–450, Juli 2012.10.3182/20120710-4-SG-2026.00159Search in Google Scholar

121. K. König and M. Mönnigmann. Regional MPC with active set updates. In Proc. 20th IFAC World Congress, S. 12365–12370, 2017.Search in Google Scholar

122. K. König and M. Mönnigmann. Regionale prädiktive Regelung – Modellprädiktive Regelung mittels stückweise definiertem Riccati-Regler. at–Automatisierungstechnik, 65(12):808–821, 2017.10.1515/auto-2017-0073Search in Google Scholar

123. S.L. De Oliveira Kothare and M. Morari. Contractive model predictive control for constrained nonlinear systems. IEEE Transactions on Automatic Control, 45(6):1053–1071, 2000.10.1109/9.863592Search in Google Scholar

124. D. Kouzoupis, H.J. Ferreau, H. Peyrl and M. Diehl. First-order methods in embedded nonlinear model predictive control. In Proc. 2015 European Control Conference (ECC), S. 2617–2622, 2015.10.1109/ECC.2015.7330932Search in Google Scholar

125. D.K.M. Kufoalor, V. Aaker, T.A. Johansen, L. Imsland and G.O. Eikrem. Automatically generated embedded model predictive control: Moving an industrial PC-based MPC to an embedded platform. Optimal Control Applications and Methods, 36(5):705–727, 2015.10.1002/oca.2182Search in Google Scholar

126. D.K.M. Kufoalor, S. Richter, L. Imsland, T.A. Johansen, M. Morari and G.O. Eikrem. Embedded model predictive control on a PLC using a primal-dual first-order method for a subsea separation process. In Proc. 22nd Mediterranean Conference on Control and Automation (MED), S. 368–373, 2014.10.1109/MED.2014.6961399Search in Google Scholar

127. M. Kvasnica, J. Loefberg, M. Herceg, L. Cirka and M. Fikar. Low-complexity polynomial approximation of explicit MPC via linear programming. In Proc. American Control Conference (ACC), S. 4713–4718, 2010.10.1109/ACC.2010.5531092Search in Google Scholar

128. M. Kvasnica, J. Lofberg and M. Fikar. Stabilizing polynomial approximation of explicit MPC. Automatica, 47(10):2292–2297, 2011.10.1016/j.automatica.2011.08.023Search in Google Scholar

129. M. Kvasnica, B. Takács, J. Holaza and S. Di Cairano. On region-free explicit model predictive control. In Proc. 54th IEEE Conference on Decision and Control (CDC), S. 3669–3674, 2015.10.1109/CDC.2015.7402788Search in Google Scholar

130. B. Käpernick and K. Graichen. The gradient based nonlinear model predictive control software GRAMPC. In Proc. 2014 European Control Conference (ECC), S. 1170–1175, 2014.10.1109/ECC.2014.6862353Search in Google Scholar

131. M. Lau, S.P. Yue, K.V. Ling and J.M. Maciejowski. A comparison of interior point and active set methods for FPGA implementation of model predictive control. In Proc. European Control Conference (ECC), S. 156–161, 2009.10.23919/ECC.2009.7074396Search in Google Scholar

132. J. Levinson, J. Askeland, J. Becker, J. Dolson, D. Held, S. Kammel, J.Z. Kolter, D. Langer, O. Pink, V. Pratt, et al. Towards fully autonomous driving: Systems and algorithms. In Proc. 2011 Intelligent Vehicles Symposium (IV), S. 163–168, 2011.10.1109/IVS.2011.5940562Search in Google Scholar

133. D. Limon, I. Alvarado, T. Alamo and E.F. Camacho. Robust tube-based MPC for tracking of constrained linear systems with additive disturbances. Journal of Process Control, 20(3):248–260, 2010.10.1016/j.jprocont.2009.11.007Search in Google Scholar

134. B. Lincoln and A. Rantzer. Relaxing dynamic programming. IEEE Transaction on Automatic Control, 51(8):1249–1260, 2006.10.1109/TAC.2006.878720Search in Google Scholar

135. J. Lofberg. YALMIP: A toolbox for modeling and optimization in MATLAB. In Proc. 2004 IEEE International Symposium on Computer Aided Control Systems Design, S. 284–289, 2004.Search in Google Scholar

136. S. Lucia, M. Kögel, P. Zometa, D. Quevedo and R. Findeisen. Predictive control, embedded cyberphysical systems and systems of systems – A perspective. Annual Reviews in Control, 41:193–207, 2016.10.1016/j.arcontrol.2016.04.002Search in Google Scholar

137. S. Lucia, D. Navarro, O. Lucia, P. Zometa and R. Findeisen. Optimized FPGA implementation of model predictive control for embedded systems using high level synthesis tool. IEEE Transactions on Industrial Informatics, 14(1):137–145, 2018.10.1109/TII.2017.2719940Search in Google Scholar

138. S. Lucia, P. Rumschinski, A.J. Krener and R. Findeisen. Improved design of nonlinear model predictive controllers. In Proc. 5th IFAC Conference on Nonlinear Model Predictive Control (NMPC), S. 254–259, 2015.10.1016/j.ifacol.2015.11.292Search in Google Scholar

139. J.M. Maestre and R.R. Negenborn. Distributed Model Predictive Control Made Easy. Springer Publishing Company, 2013.10.1007/978-94-007-7006-5Search in Google Scholar

140. M. Maiworm, D. Limon, J.M. Manzano and R. Findeisen. Stability of gaussian process learning based output feedback model predictive control. In Proc. 6th IFAC Conference on Nonlinear Model Predictive Control (NMPC), 2018.10.1016/j.ifacol.2018.11.047Search in Google Scholar

141. K. Malanowski. Stability of Solutions to Convex Problems of Optimization. Springer, 1987.10.1007/BFb0039570Search in Google Scholar

142. J. Mattingley and S. Boyd. CVXGEN: A code generator for embedded convex optimization. Optimization and Engineering, 13(1):1–27, 2012.10.1007/s11081-011-9176-9Search in Google Scholar

143. D.Q. Mayne, S.V. Raković, R. Findeisen and F. Allgöwer. Robust output feedback model predictive control of constrained linear systems. Automatica, 42(7):1217–1222, 2006.10.1016/j.automatica.2006.03.005Search in Google Scholar

144. F. Mesmer, S. Hentzelt, T. Szabo and K. Graichen. Optimal control of dual-clutch transmissions with multiple groups. In Proc. 2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM 2017), S. 1749–1754, 2017.10.1109/AIM.2017.8014271Search in Google Scholar

145. F. Mesmer, T. Szabo and K. Graichen. Embedded nonlinear model predictive control of dual-clutch transmissions with multiple groups on a shrinking horizon. IEEE Transactions on Control Systems Technology (im Druck), 2018.10.1109/AIM.2017.8014271Search in Google Scholar

146. M. Milanese and A. Vicino. Information-based complexity and nonparametric worst-case system identification. Journal of Complexity, 9(4):427–446, 1993.10.1006/jcom.1993.1028Search in Google Scholar

147. M. Mönnigmann and M. Jost. Vertex based calculation of explicit MPC laws. In Proc. 2012 American Control Conference (ACC), S. 423–428, 2012.10.1109/ACC.2012.6315197Search in Google Scholar

148. M. Mönnigmann and M. Kastsian. Fast explicit model predictive control with multiway trees. In Proc. 18th IFAC World Congress, S. 1356–1361, 2011.10.3182/20110828-6-IT-1002.00686Search in Google Scholar

149. M. Mönnigmann, J. Otten and M. Jost. Nonlinear MPC defines implicit regional optimal control laws. In Proc. 5th IFAC Conference on Nonlinear Model Predictive Control (NMPC), S. 142–147, 2015.10.1016/j.ifacol.2015.11.274Search in Google Scholar

150. M. Müller and F. Allgöwer. Economic and distributed model predictive control: Recent developments in optimization-based control. SICE Journal of Control, Measurement, and System Integration, 10(2):39–52, 2017.10.9746/jcmsi.10.39Search in Google Scholar

151. I. Necoara. Computational compexity certification for dual gradient method: Application to embedded MPC. Systems & Control Letters, 81:49–56, 2015.10.1016/j.sysconle.2015.04.011Search in Google Scholar

152. I. Necoara, L. Ferranti and T. Keviczky. An adaptive constraint tightening approach to linear model predictive control based on approximation algorithms for optimization. Optimal Control Applications and Methods, 36(5):648–666, 2015.10.1002/oca.2121Search in Google Scholar

153. R.R. Negenborn and J.M. Maestre. Distributed model predictive control: An overview and roadmap of future research opportunities. IEEE Control Systems Magazine, 34(4):87–97, 2014.10.1109/MCS.2014.2320397Search in Google Scholar

154. Y. Nesterov. A method for solving a convex programming problem with convergence rate 1/k2. Soviet Mathematics Doklady, 27(2):372–376, 1983.Search in Google Scholar

155. Y. Nesterov. Introductory lectures on convex optimization: A basic course. In P.M. Paradalos and D.W. Hearn, editors, Applied Optimization, volume 87 of Applied Optimization. Kluwer Academic Publishers, Norwell, MA, USA, 2004.10.1007/978-1-4419-8853-9_3Search in Google Scholar

156. K.C. Ng, L. Wang and I.A. Peake. Safety-critical multi-core software architecture for model predictive control. In Proc. 2011 Australian Control Conference (AUCC), S. 434–439, 2011.Search in Google Scholar

157. N.A. Nguyen, M. Gulan, S. Olaru and P. Rodriguez-Ayerbe. Convex lifting: Theory and control applications. IEEE Transactions on Automatic Control, 63(5):1243–1258, 2018.10.1109/TAC.2017.2737234Search in Google Scholar

158. J. Nocedal and S.J. Wright. Numerical Optimization. Springer, New York, 2006.Search in Google Scholar

159. P. Nuzzo, A.L. Sangiovanni-Vincentelli, D. Bresolin, L. Geretti and T. Villa. A platform-based design methodology with contracts and related tools for the design of cyber-physical systems. Proceedings of the IEEE, 103(11):2104–2132, 2015.10.1109/JPROC.2015.2453253Search in Google Scholar

160. R. Oberdieck, N.A. Diangelakis, I. Nascu, M.M. Papathanasiou, M. Sun, S. Auraamidou and E.N. Pistikopoulos. On multi-parametric programming and its applications in process systems engineering. Chemical Engineering Research & Design, 116:61–82, 2016.10.1016/j.cherd.2016.09.034Search in Google Scholar

161. T. Ohtsuka. A continuation/GMRES method for fast computation of nonlinear receding horizon control. Automatica, 40(4):563–574, 2004.10.1016/j.automatica.2003.11.005Search in Google Scholar

162. T. Ohtsuka. A tutorial on C/GMRES and automatic code generation for nonlinear model predictive control. In Proc. 2015 European Control Conference (ECC), S. 73–86, 2015.10.1109/ECC.2015.7330528Search in Google Scholar

163. J. Oravec, Y. Jiang, B. Houska and M. Kvasnica. Parallel explicit MPC for hardware with limited memory. IFAC-PapersOnLine, 50(1):3301–3306, 2017.10.1016/j.ifacol.2017.08.506Search in Google Scholar

164. G. Pannocchia, J.B. Rawlings and S.J. Wright. Conditions under which suboptimal nonlinear MPC is inherently robust. Systems & Control Letters, 60(9):747–755, 2011.10.1016/j.sysconle.2011.05.013Search in Google Scholar

165. G. Pannocchia, J.B. Rawlings and S.J. Wright. Fast, large-scale model predictive control by partial enumeration. Automatica, 43(5):852–860, 2007.10.1016/j.automatica.2006.10.019Search in Google Scholar

166. G. Pannocchia, S.J. Wright and J.B. Rawlings. Partial enumeration MPC: Robust stability results and application to an unstable CSTR. Journal of Process Control, 21(10):1459–1466, 2011.10.1016/j.jprocont.2011.06.010Search in Google Scholar

167. P. Patrinos and H. Sarimveis. A new algorithm for solving convex parametric quadratic programs based on graphical derivatives of solution mappings. Automatica, 46:1405–1418, 2010.10.1016/j.automatica.2010.06.008Search in Google Scholar

168. G. Pin and Th. Parisini. Networked predictive control of uncertain constrained nonlinear systems: Recursive feasibility and input-to-state stability analysis. IEEE Transactions on Automatic Control, 56(1):72–87, 2011.10.1109/TAC.2010.2051091Search in Google Scholar

169. S.J. Qin and T.A. Badgwell. A survey of industrial model predictive control technology. Control Engineering Practice, 11(7):733–764, 2003.10.1016/S0967-0661(02)00186-7Search in Google Scholar

170. D.M. Raimondo, O. Huber, M. Schulze Darup, M. Mönnigmann and M. Morari. Constrained time-optimal control for nonlinear systems: a fast explicit approximation. In Proc. 4th IFAC Conference on Nonlinear Model Predictive Control (NMPC), S. 113–118, 2012.10.3182/20120823-5-NL-3013.00073Search in Google Scholar

171. D.M. Raimondo, S. Riverso, C. Jones and M. Morari. A robust explicit nonlinear MPC controller with input-to-state stability. In Proc. 18th IFAC World Congress, S. 9284–9289, 2011.10.3182/20110828-6-IT-1002.03495Search in Google Scholar

172. A. Rao. A survey of numerical methods for optimal control. Advances in the Astronautical Sciences, 135, 2010.Search in Google Scholar

173. C.V. Rao, S.J. Wright and J.B. Rawlings. Application of interior-point methods to model predictive control. Journal of Optimization Theory and Applications, 98(3):723–757, 1998.10.1023/A:1021711402723Search in Google Scholar

174. J.B. Rawlings. Moving horizon estimation. In Encyclopedia of Systems and Control, S. 1–7, 2013.10.1007/978-1-4471-5102-9_4-1Search in Google Scholar

175. J.B. Rawlings, D.Q. Mayne and M. Diehl. Model Predictive Control: Theory, Computation, and Design. Nob Hill Publishing, 2017.Search in Google Scholar

176. J.B. Rawlings, G. Pannocchia, S.J. Wright and C.N. Bates. On the inherent robustness of suboptimal model predictive control. In Proc. 2013 SIAM Conference on Control and its Applications, S. 1–28, 2013.Search in Google Scholar

177. A. Richards and J. How. Decentralized model predictive control of cooperating UAVs. In Proc. 43th Conference on Decision and Control (CDC), volume 4, S. 4286–4291, 2004.10.1109/CDC.2004.1429425Search in Google Scholar

178. S. Richter, C.N. Jones and M. Morari. Real-time input-constrained MPC using fast gradient methods. In Proc. 48th IEEE Conference on Decision and Control (CDC), S. 7387–7393, 2009.10.1109/CDC.2009.5400619Search in Google Scholar

179. U. Rosolia, S. De Bruyne and A.G. Alleyne. Autonomous vehicle control: A nonconvex approach for obstacle avoidance. IEEE Transactions on Control Systems Technology, 25(2):469–484, 2017.10.1109/TCST.2016.2569468Search in Google Scholar

180. U. Rosolia, X. Zhang and F. Borrelli. Data-driven predictive control for autonomous systems. Annual Review of Control, Robotics, and Autonomous Systems, 1:259–286, 2018.10.1146/annurev-control-060117-105215Search in Google Scholar

181. A. Sangiovanni-Vincentelli, W. Damm and R. Passerone. Taming Dr. Frankenstein: Contract-based design for cyber-physical systems. European Journal of Control, 18(3):217–238, 2012.10.3166/ejc.18.217-238Search in Google Scholar

182. K. Schittkowski and C. Zillober. Sequential convex programming methods. In K. Marti and P. Kall, editors, Stochastic Programming: Numerical Techniques and Engineering Applications, S. 123–141. Springer, 1995.10.1007/978-3-642-88272-2_8Search in Google Scholar

183. M. Schulze Darup, R. Dyrska, K. König and M. Mönnigmann. A novel event-triggered robust MPC scheme for linear systems. In Proc. 56th IEEE Conference on Decision and Control (CDC), S. 2941–2946, 2017.10.1109/CDC.2017.8264087Search in Google Scholar

184. M. Schulze Darup, M. Jost, G. Pannocchia and M. Mönnigmann. On the maximal controller gain in linear MPC. In Proc. 20th IFAC World Congress, IFAC PapersOnLine 50-1, S. 9218–9223, 2017.10.1016/j.ifacol.2017.08.1279Search in Google Scholar

185. M. Schulze Darup and M. Mönnigmann. Approximate explicit NMPC with guaranteed stability ensured by a simple auxiliary controller. In Proc. 2012 IEEE Multi-Conference on Systems and Control (MSC), S. 270–275, 2012.Search in Google Scholar

186. M. Schulze Darup and M. Mönnigmann. Low complexity suboptimal explicit NMPC. In Proc.4th IFAC Conference on Nonlinear Model Predictive Control (NMPC), S. 406–411, 2012.10.3182/20120823-5-NL-3013.00080Search in Google Scholar

187. M. Schulze Darup and M. Mönnigmann. A stabilizing control scheme for linear systems on controlled invariant sets. Systems & Control Letters, 79:8–14, 2015.10.1016/j.sysconle.2015.02.008Search in Google Scholar

188. P.O.M. Scokaert, D.Q. Mayne and J.B. Rawlings. Suboptimal model predictive control (feasibility implies stability). IEEE Transactions on Automatic Control, 44(3):648–654, 1999.10.1109/9.751369Search in Google Scholar

189. M. Seron, J.A. De Dona and G.C. Goodwin. Global analytical model predictive control with input constraints. In Proc. 39th IEEE Conference on Decision and Control (CDC), S. 154–159, 2000.Search in Google Scholar

190. M. Seron, G. Goodwin and J. DeDona. Characterisation of receding horizon control for constrained systems. Asian Journal of Control, 5:271–286, 2003.10.1111/j.1934-6093.2003.tb00118.xSearch in Google Scholar

191. D. Seto, B.H. Krogh, L. Sha and A. Chutinan. Dynamic control system upgrade using the simplex architecture. IEEE Control Systems, 18(4):72–80, 1998.10.1109/37.710880Search in Google Scholar

192. A. Shahzad and P.J. Goulart. A new hot-start interior-point method for model predictive control. In Proc. 18th IFAC World Congress, S. 2470–2475, 2011.10.3182/20110828-6-IT-1002.02817Search in Google Scholar

193. H.A. Shukla, B. Khusainov, E.C. Kerrigan and C.N. Jones. Software and hardware code generation for predictive control using splitting methods. In Proc. 20th IFAC World Congress, IFAC PapersOnLine 50-1, S. 14386–14391, 2017.10.1016/j.ifacol.2017.08.2025Search in Google Scholar

194. J.A. Stankovic. Research directions for the internet of things. IEEE Internet of Things Journal, 1(1):3–9, 2014.10.1109/JIOT.2014.2312291Search in Google Scholar

195. T. Stastny and R. Siegwart. Nonlinear model predictive guidance for fixed-wing uavs using identified control augmented dynamics. In 2018 International Conference on Unmanned Aircraft Systems (ICUAS), S. 432–442, 2018.10.1109/ICUAS.2018.8453377Search in Google Scholar

196. A. Suardi, E.C. Kerrigan and G.A. Constantinides. Fast FPGA prototyping toolbox for embedded optimization. In Proc. European Control Conference, S. 2589–2594, 2015.10.1109/ECC.2015.7330928Search in Google Scholar

197. A. Suardi, S. Longo, E.C. Kerrigan and G.A. Constantinides. Explicit MPC: Hard constraint satisfaction under low precision arithmetic. Control Engineering Practice, 47:60–69, 2016.10.1016/j.conengprac.2015.12.005Search in Google Scholar

198. S. Summers, C.N. Jones, J. Lygeros and M. Morari. A multiresolution approximation method for fast explicit model predictive control. IEEE Transactions on Automatic Control, 56(11):2530–2541, 2011.10.1109/TAC.2011.2146990Search in Google Scholar

199. S. Summers, D. Raimondo, C. Jones, J. Lygeros and M. Morari. Fast explicit nonlinear model predictive control via multiresolution function approximation with guaranteed stability. In Proc. 8th IFAC Symposium on Nonlinear Control Systems (NOLCOS), S. 533–538, 2010.10.3182/20100901-3-IT-2016.00275Search in Google Scholar

200. I. Sutskever, O. Vinyals and Q.V. Le. Sequence to sequence learning with neural networks. In Advances in neural information processing systems, S. 3104–3112, 2014.Search in Google Scholar

201. A. Szuecs, M. Kvasnica and M. Fikar. A memory-efficient representation of explicit MPC solutions. In Proc. 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC), S. 1916–1921, 2011.10.1109/CDC.2011.6160585Search in Google Scholar

202. S. Tajeddin. Automatic Code Generation of Real-Time Nonlinear Model Predictive Control for Plug-in Hybrid Electric Vehicle Intelligent Cruise Controllers. PhD thesis, University of Waterloo, Ontario, Kanada, 2016.Search in Google Scholar

203. G. Takács, P. Zometa and R. Findeisen. Efficiency and performance of embedded model predictive control for active vibration attenuation. In European Control Conference (ECC), S. 1334–1340. IEEE, 2016.10.1109/ECC.2016.7810474Search in Google Scholar

204. G. Takács, P. Zometa and R. Findeisen. Embedded model predictive vibration control on low-end 8-bit microcontrollers via automatic code generation. In ICSV 23: Proceedings of the 23rd International Congress on Sound and Vibrattion, volume 266, 2016.Search in Google Scholar

205. D.W. Tank and J.J. Hopfield. Simple “neural” optimization networks: An A/D converter, signal decision circuit, and a linear programming circuit. IEEE Transactions on Circuits and Systems, 33(5):533–541, 1986.10.1109/TCS.1986.1085953Search in Google Scholar

206. P. Tondel, T.A. Johansen and A. Bemporad. Computation and approximation of piecewise affine control laws via binary search trees. In Proc. 41st IEEE Conference on Decision and Control (CDC), S. 3144–3149, 2002.Search in Google Scholar

207. P. Tondel, T.A. Johansen and A. Bemporad. An algorithm for multi-parametric quadratic programming and explicit MPC solutions. Automatica, 39(3):489–497, 2003.10.1016/S0005-1098(02)00250-9Search in Google Scholar

208. P. Tondel, T.A. Johansen and A. Bemporad. Evaluation of piecewise affine control via binary search tree. Automatica, 39(5):945–950, 2003.10.1016/S0005-1098(02)00308-4Search in Google Scholar

209. P. Tøndel, T.A. Johansen and A. Bemporad. Further results on multiparametric quadratic programming. In Proc. 42nd IEEE Conference on Decision and Control (CDC), S. 3173–3178, 2003.Search in Google Scholar

210. Q. Tran-Dinh, C. Savorgnan and M. Diehl. Adjoint-based predictor-corrector sequential convex programming for parametric nonlinear optimization. SIAM Journal on Optimization, 22(4):1258–1284, 2012.10.1137/110844349Search in Google Scholar

211. F. Ullmann. FiOrdOs: A Matlab Toolbox for C-Code Generation for First Order Methods. Master’s thesis, ETH Zürich, 2011.Search in Google Scholar

212. S. Vazquez, J.I. Leon, L.G. Franquelo, J. Rodriguez, H.A. Young, A.Marquez and P. Zanchetta. Model predictive control: A review of its applications in power electronics. IEEE Industrial Electronics Magazine, 8(1):16–31, 2014.10.1109/MIE.2013.2290138Search in Google Scholar

213. O. von Stryk and R. Bulirsch. Direct and indirect methods for trajectory optimization. Annals of Operational Research, 37(1–4):357–373, 1992.10.1007/BF02071065Search in Google Scholar

214. K.P. Wabersich and M. Zeilinger. Linear model predictive safety certification for learning-based control. ArXiv Preprint: arXiv:1803.08552, 2018.Search in Google Scholar

215. A. Wächter and L.T. Biegler. On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming, 106(1):25–57, 2006.10.1007/s10107-004-0559-ySearch in Google Scholar

216. Y. Wang and S. Boyd. Fast model predictive control using online optimization. IEEE Transactions on Control Systems Technology, 18(2):267–278, 2010.10.1109/TCST.2009.2017934Search in Google Scholar

217. A.G. Wills and W.P. Heath. Interior-point algorithms for nonlinear model predictive control. In R. Findeisen, F. Allgöwer and L.T. Biegler, editors, Assessment and Future Directions of Nonlinear Model Predictive Control, S. 207–216. Springer, 2007.10.1007/978-3-540-72699-9_16Search in Google Scholar

218. M.H. Wright. Ill-conditioning and computational error in interior-point methods for nonlinear programming. SIAM Journal on Optimization, 9(1):84–111, 1998.10.1137/S1052623497322279Search in Google Scholar

219. S.J. Wright. Effects of finite-precision arithmetic on interior-point methods for nonlinear programming. SIAM Journal on Optimization, 12(1):36–78, 2001.10.1137/S1052623498347438Search in Google Scholar

220. V.M. Zavala and M. Anitescu. Real-time nonlinear optimization as a gneralized equation. SIAM Journal on Control and Optimization, 48(8):5444–5467, 2010.10.1137/090762634Search in Google Scholar

221. V.M. Zavala and L.T. Biegler. The advanced-step NMPC controller: Optimality, stability and robustness. Automatica, 45(1):86–93, 2009.10.1016/j.automatica.2008.06.011Search in Google Scholar

222. M. Zeilinger, C. Jones and M. Morari. Real-time suboptimal model predictive control using a combination of explicit MPC and online optimization. IEEE Transactions on Automatic Control, 56(7):1524–1534, 2011.10.1109/TAC.2011.2108450Search in Google Scholar

223. L. Zhang, H. Gao and O. Kaynak. Network-induced constraints in networked control systems—a survey. IEEE Transactions on Industrial Informatics, 9(1):403–416, 2013.10.1109/TII.2012.2219540Search in Google Scholar

224. T. Zhang, G. Kahn, S. Levine and P. Abbeel. Learning deep control policies for autonomous aerial vehicles with MPC-guided policy search. In Proc. 2016 IEEE International Conference on Robotics and Automation (ICRA), S. 528–535, 2016.10.1109/ICRA.2016.7487175Search in Google Scholar

225. H. Zhou, F. Jia, H. Jing, Z. Liu and L. Güvenç. Coordinated longitudinal and lateral motion control for four wheel independent motor-drive electric vehicle. IEEE Transactions on Vehicular Technology, 67(5):3782–3790, 2018.10.1109/TVT.2018.2816936Search in Google Scholar

226. J.P. Zometa. Code generation for model predictive control of embedded systems. PhD thesis, Otto-von-Guerike-Universität Magdeburg, Fakultät für Elektrotechnik und Informationstechnik, 2017.Search in Google Scholar

227. P. Zometa, M. Kögel, T. Faulwasser and R. Findeisen. Implementation aspects of model predictive control for embedded systems. In Proc. 2012 American Control Conference (ACC), S. 1205–1210, 2012.10.1109/ACC.2012.6315076Search in Google Scholar

228. P. Zometa, M. Kögel and R. Findeisen. μAO-MPC: a free code generation tool for embedded real-time linear model predictive control. In Proc. 2013 American Control Conference (ACC), S. 5320–5325, 2013.10.1109/ACC.2013.6580668Search in Google Scholar