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Direct Optimal Control and Model Predictive Control

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Optimal Control: Novel Directions and Applications

Part of the book series: Lecture Notes in Mathematics ((LNM,volume 2180))

Abstract

Model predictive control is a feedback control technique based on repeatedly solving optimal control problems. Direct methods for optimal control have gained popularity especially for practical applications, due to their flexibility. In this chapter we first present the state of the art in MPC stability theory. Then, we introduce the numerical methods used for direct optimal control and some variants specifically tailored to MPC. We conclude the chapter with five application examples.

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Notes

  1. 1.

    A continuous function \(\rho: \mathbb{R}_{0}^{+} \rightarrow \mathbb{R}_{0}^{+}\) is a \(\mathcal{K}\)-function if ρ(0) = 0 and is strictly increasing. ρ is a \(\mathcal{K}_{\infty }\)-function if it is a \(\mathcal{K}\)-function that is unbounded. A continuous function \(\beta: \mathbb{R}_{0}^{+} \times \mathbb{R}_{0}^{+} \rightarrow \mathbb{R}_{0}^{+}\) is a \(\mathcal{KL}\)-function if for each r ≥ 0, β(r, ⋅ ) is decreasing and lim t β(r, t) = 0 and for each \(t \geq 0,\ \beta (\cdot,t) \in \mathcal{ K}_{\infty }\). A continuous function \(\beta: \mathbb{R}_{0}^{+} \times \mathbb{R}_{0}^{+} \rightarrow \mathbb{R}_{0}^{+}\) is a \(\mathcal{KL}_{0}\)-function if for each r ≥ 0, β(r, ⋅ ) is decreasing and lim t β(r, t) = 0 and for each t ≥ 0 we either have \(\beta (\cdot,t) \in \mathcal{ K}_{\infty }\) or β(⋅ , t) ≡ 0.

  2. 2.

    A set \(S \subset \mathbb{X}\) is said to be forward invariant or viable for (3.1) if, for every xS there exists \(u \in \mathbb{U}(x)\) such that f(x, u) ∈ S.

  3. 3.
    1. (i)

      The optimal value function V N is said to be uniformly continuous on a set \(A \subseteq \mathbb{X}\) if there exists a \(\mathcal{K}\)-function \(\omega _{V _{N}}\) such that for all x 1, x 2A

      $$\displaystyle{\vert V _{N}(x_{1}) - V _{N}(x_{2})\vert \leq \omega _{V _{N}}\left (\|x_{1} - x_{2}\|\right ).}$$
    2. (ii)

      The cost functional J N is said to be uniformly continuous on \(A \subseteq \mathbb{X}\) uniformly in \(u \in \mathbb{U}^{N}\) if there exists a function \(\omega _{J_{N}} \in \mathcal{ K}\) such that for all x 1, x 2A and all \(u \in \mathbb{U}^{N}\)

      $$\displaystyle{\vert J_{N}(x_{1},u) - J_{N}(x_{2},u)\vert \leq \omega _{J_{N}}\left (\|x_{1} - x_{2}\|\right ).}$$

    The functions \(\omega _{V _{N}}\) and \(\omega _{J_{N}}\) are called moduli of continuity. Analogous uniform continuity definitions can be defined for f, ℓ and B K with the corresponding moduli of continuity.

  4. 4.

    We say that f is uniformly bounded on each ball \(\overline{\mathcal{B}}_{\varDelta }(x_{s})\) if for any Δ > 0 the value \(\sup _{\|x\|_{x_{ s}}\leq \varDelta,u\in \mathbb{U}(x)}\|\,f(x,u)\|\) is finite.

References

  1. KUKA youBot store. www.youbot-store.com

  2. POV-Ray. www.povray.org

  3. Amrit, R., Rawlings, J., Angeli, D.: Economic optimization using model predictive control with a terminal cost. Ann. Rev. Control 35, 178–186 (2011)

    Article  Google Scholar 

  4. Angeli, D., Amrit, R., Rawlings, J.: Receding horizon cost optimization for overly constrained nonlinear plants. In: Proceedings of the Joint 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference (2009)

    Google Scholar 

  5. Angeli, D., Amrit, R., Rawlings, J.: Enforcing convergence in nonlinear economic MPC. In: Proceedings of the 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC) (2011)

    Google Scholar 

  6. Angeli, D., Amrit, R., Rawlings, J.: On Average Performance and Stability of Economic Model Predictive Control. IEEE Trans. Autom. Control 57, 1615–1626 (2012)

    Article  MathSciNet  Google Scholar 

  7. Angeli, D., Rawlings, J.: Receding horizon cost optimization and control for nonlinear plants. In: Proceedings of the 8th IFAC Symposium on Nonlinear Control Systems (2010)

    Google Scholar 

  8. Ascher, U.M., Mattheij, R.M., Russell, R.D.: Numerical Solution of Boundary Value Problems for Ordinary Differential Equations. SIAM, Philadelphia (1995)

    Book  MATH  Google Scholar 

  9. Baier, R., Gerdts, M.: A computational method for non-convex reachable sets using optimal control. In: Proceedings of the European Control Conference, pp. 23–26 (2009)

    Google Scholar 

  10. Baier, R., Gerdts, M., Xausa, I.: Approximation of reachable sets using optimal control algorithms. Numer. Algebra Control Optim. 3(3), 519–548 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  11. Bardi, M., Capuzzo-Dolcetta, I.: Optimal control and viscosity solutions of Hamilton–Jacobi–Bellman equations. Springer, Berlin (2008)

    MATH  Google Scholar 

  12. Bellman, R.: Dynamic Programming. Princeton University Press, Princeton (1957)

    MATH  Google Scholar 

  13. Berkovitz, L.D.: Convexity and Optimization in \(\mathbb{R}^{n}\). Wiley, New York (2001)

    Google Scholar 

  14. Bertsekas, D.P.: Dynamic Programming and Optimal Control, vol. II, 2nd edn. Athena Scientific, Belmont (2001)

    Google Scholar 

  15. Bertsekas, D.P.: Dynamic Programming and Optimal Control, vol. I, 3rd edn. Athena Scientific, Belmont (2005)

    Google Scholar 

  16. Betts, J.T.: Practical Methods for Optimal Control and Estimation Using Nonlinear Programming. SIAM, Philadelphia (2010)

    Book  MATH  Google Scholar 

  17. Bischoff, R., Huggenberger, U., Prassler, E.: KUKA youBot- a mobile manipulator for research and education. In: The 2011 IEEE International Conference on Robotics and Automation (2011)

    Google Scholar 

  18. Boccia, A.: Optimization based control of nonlinear constrained systems. Ph.D. Thesis, Imperial College London (2014)

    Google Scholar 

  19. Boccia, A., Grüne, L., Worthmann, K.: Stability and feasibility of state-constrained linear MPC without stabilizing terminal constraints. In: Proceedings of the 21st International Symposium on Mathematical Theory of Networks and Systems, pp. 453–460 (2014)

    Google Scholar 

  20. Boccia, A., Grüne, L., Worthmann, K.: Stability and feasibility of state constrained MPC without stabilizing terminal constraints. Syst. Control Lett. 72, 14–21 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  21. Bock, H.: Recent advances in parameter identification techniques for ODE. In: Deuflhard, P., Hairer, E. (eds.) Numerical Treatment of Inverse Problems in Differential and Integral Equations. Birkhäuser, Boston (1983)

    Google Scholar 

  22. Bock, H., Plitt, K.: A multiple shooting algorithm for direct solution of optimal control problems. In: Proceedings 9th IFAC World Congress Budapest, pp. 242–247. Pergamon Press (1984)

    Google Scholar 

  23. Bokanowski, O., Désilles, A., Zidani, H.: Roc-hj solver numerical parallel library for solving Hamilton–Jacobi equations. Technical Report(2011). http://uma.ensta-paristech.fr/files/ROC-HJ/

  24. Bokanowski, O., Forcadel, N., Zidani, H.: Reachability and minimal times for state constrained nonlinear problems without any controllability assumption. SIAM J. Control Optim. 48, 4292–4316 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  25. Bokanowski, O., Zidani, H.: Minimal time problems with moving targets and obstacles. In: Proceedings of 18th IFAC World Congress, pp. 2589–2593 (2011)

    Google Scholar 

  26. Bossanyi, E.A.: Further load reductions with individual pitch control. Wind Energy 8, 481–485 (2005)

    Article  Google Scholar 

  27. Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press, Cambridge (2004)

    Book  MATH  Google Scholar 

  28. Broyden, C.: The convergence of a class of double-rank minimization algorithms. IMA J. Appl. Math. 6(1), 76–90 (1970)

    Article  MATH  Google Scholar 

  29. Büskens, C.: Users’s guide for the fortran subroutines bdsco, nudocccs, kollokat, diroptim and dirmesch. Technical Report, Universität Münster, Germany (1994)

    Google Scholar 

  30. Büskens, C.: Optimierungsmethoden und sensitivitätsanalyse für optimale steuerprozesse mit steuer-und zustands-beschränkungen. Ph.D. Thesis, Universität Münster, Germany (1998)

    Google Scholar 

  31. Büskens, C., Maurer, H.: Sensitivity analysis and real-time optimization of parametric nonlinear programming problems. In: Grötschel, M., Krumke, S.O., Rambau, J. (eds.) Online Optimization of Large Scale Systems, pp. 3–16. Springer, Berlin/Heidelberg (2001)

    Chapter  Google Scholar 

  32. Butcher, J.C.: Numerical Methods for Ordinary Differential Equations. Wiley, New York (2008)

    Book  MATH  Google Scholar 

  33. Byrd, R.H., Nocedal, J., Schnabel, R.B.: Representations of quasi-Newton matrices and their use in limited memory methods. Math. Program. 63, 129–156 (1994)

    Article  MathSciNet  MATH  Google Scholar 

  34. Désilles, A., Zidani, H., Crück, E.: Collision analysis for an UAV. In: Proceedings of AIAA Guidance, Navigation, and Control Conference (2012)

    Book  Google Scholar 

  35. Deuflhard, P., Pesch, H.J., Rentrop, P.: A modified continuation method for the numerical solution of nonlinear two-point boundary value problems by shooting techniques. Numer. Math. 26, 327–343 (1976)

    Article  MathSciNet  MATH  Google Scholar 

  36. Diehl, M., Amrit, R., Rawlings, J.: A Lyapunov function for economic optimizing model predictive control. IEEE Trans. Autom. Control 56(3), 703–707 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  37. Diehl, M., Bock, H., Schlöder, J., Findeisen, R., Nagy, Z., Allgöwer, F.: Real-time optimization and nonlinear model predictive control of processes governed by differential-algebraic equations. J. Process Control 12(4), 577–585 (2002)

    Article  Google Scholar 

  38. Diehl, M., Ferreau, H.J., Haverbeke, N.: Efficient numerical methods for nonlinear MPC and moving horizon estimation. In: Nonlinear Model Predictive Control. Lecture Notes in Control and Information Sciences, vol. 384, pp. 391–417. Springer, Berlin (2009)

    Google Scholar 

  39. Diehl, M., Findeisen, R., Allgöwer, F., Bock, H., Schlöder, J.: Nominal stability of the real-time iteration scheme for nonlinear model predictive control. IEE Proc. Control Theory Appl. 152(3), 296–308 (2005)

    Article  Google Scholar 

  40. Diehl, M., Magni, L., Nicolao, G.D.: Efficient NMPC of unstable periodic systems using approximate infinite horizon closed loop costing. Ann. Rev. Control 28(1), 37–45 (2004)

    Article  Google Scholar 

  41. Ellis, M., Christofides, P.D.: Economic model predictive control with time-varying objective function for nonlinear process systems. AIChE J. 60, 507–519 (2014)

    Article  Google Scholar 

  42. Fabien, B.C.: DSOA: The implementation of a dynamic system optimization algorithm. Optim. Control Appl. Methods 31, 231–247 (2010)

    MathSciNet  MATH  Google Scholar 

  43. Ferreau, H.: Model predictive control algorithms for applications with millisecond timescales. Ph.D. Thesis, K.U. Leuven (2011)

    Google Scholar 

  44. Ferreau, H., Kirches, C., Potschka, A., Bock, H., Diehl, M.: qpOASES: A parametric active-set algorithm for quadratic programming. Math. Program. Comput. 6(4), 327–363 (2014)

    Google Scholar 

  45. Fiacco, A.: Introduction to sensitivity and stability analysis in nonlinear programming. In: Mathematics in Science and Engineering. Academic, New York (1983)

    MATH  Google Scholar 

  46. Fiacco, A.V.: Sensitivity analysis for nonlinear programming using penalty methods. Math. Program. 10(1), 287–311 (1976)

    Article  MathSciNet  MATH  Google Scholar 

  47. Fletcher, R.: A new approach to variable metric algorithms. Comput. J. 13(3), 317–322 (1970)

    Article  MATH  Google Scholar 

  48. Frasch, J.V., Gray, A.J., Zanon, M., Ferreau, H.J., Sager, S., Borrelli, F., Diehl, M.: An auto-generated nonlinear MPC algorithm for real-time obstacle avoidance of ground vehicles. In: Proceedings of the European Control Conference (2013)

    Google Scholar 

  49. Geebelen, K., Ahmad, H., Vukov, M., Gros, S., Swevers, J., Diehl, M.: An experimental test set-up for launch/recovery of an airborne wind energy (AWE) system. In: Proceedings of the 2012 American Control Conference (2012)

    Google Scholar 

  50. Geebelen, K., Vukov, M., Wagner, A., Ahmad, H., Zanon, M., Gros, S., Vandepitte, D., Swevers, J., Diehl, M.: An experimental test setup for advanced estimation and control of an airborne wind energy systems. In: Ahrens, U., Diehl, M., Schmehl, R. (eds.) Airborne Wind Energy. Springer (2013)

    Google Scholar 

  51. Geebelen, K., Wagner, A., Gros, S., Swevers, J., Diehl, M.: Moving horizon estimation with a huber penalty function for robust pose estimation of tethered airplanes. In: Proceedings of the 2012 American Control Conference (2013)

    Google Scholar 

  52. Gerdts, M.: Direct shooting method for the numerical solution of higher-index DAE optimal control problems. J. Optim. Theory Appl. 117, 267–294 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  53. Gerdts, M.: A variable time transformation method for mixed-integer optimal control problems. Optim. Control Appl. Methods 27, 169–182 (2006)

    Article  MathSciNet  Google Scholar 

  54. Gerdts, M.: Ocpid-dae1 optimal control and parameter identification with differential-algebraic equations of index 1 (2011). http://www.optimal-control.de

    Google Scholar 

  55. Gerdts, M.: Optimal Control of ODEs and DAEs. De Gruyter Textbook. De Gruyter, Berlin (2012)

    Book  MATH  Google Scholar 

  56. Gerdts, M., Xausa, I.: Avoidance trajectories using reachable sets and parametric sensitivity analysis. In: System Modeling and Optimization, pp. 491–500. Springer, Berlin (2013)

    Google Scholar 

  57. Gertz, M., Wright, J.W.: Object-Oriented Software for Quadratic Programming. ACM Trans. Math. Softw. 29(1), 58–81 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  58. Geyer, T., Papafotiou, G., Frasca, R., Morari, M.: Constrained optimal control of the step-down DC-DC converter. IEEE Trans. Power Electron. 23(5), 2454–2464 (2008)

    Article  Google Scholar 

  59. Girsanov, I.V.: Lectures on Mathematical Theory of Extremum Problems. Springer, Berlin (1972)

    Book  MATH  Google Scholar 

  60. Goldfarb, D.: A family of variable-metric methods derived by variational means. Math. Comput. 24(109), 23–26 (1970)

    Article  MathSciNet  MATH  Google Scholar 

  61. Gray, A., Zanon, M., Frasch, J.: Parameters for a Jaguar X-Type. Technical Report (2012). http://www.mathopt.de/RESEARCH/obstacleAvoidance.php

  62. Gros, S., Ahmad, H., Geebelen, K., Diehl, M.: In-flight estimation of the aerodynamic roll damping and trim angle for a tethered aircraft based on multiple-shooting. In: System Identification Conference (2012)

    Google Scholar 

  63. Gros, S., Diehl, M.: Modeling of airborne wind energy systems in natural coordinates. Airborne Wind Energy. Springer, Berlin (2013)

    Book  Google Scholar 

  64. Gros, S., Zanon, M., Diehl, M.: Orbit Control for a Power Generating Airfoil Based on Nonlinear MPC. In: American Control Conference (2012) (submitted)

    Book  Google Scholar 

  65. Gros, S., Zanon, M., Diehl, M.: A relaxation strategy for the optimization of airborne wind energy systems. In: European Control Conference (2013)

    Google Scholar 

  66. Gros, S., Zanon, M., Diehl, M.: Control of airborne wind energy systems based on nonlinear model predictive control & moving horizon estimation. In: European Control Conference (2013)

    Google Scholar 

  67. Gros, S., Zanon, M., Quirynen, R., Bemporad, A., Diehl, M.: From linear to nonlinear MPC: bridging the gap via real-time iteration. Int. J. Control 1–19 (2016)

    Google Scholar 

  68. Gros, S., Zanon, M., Vukov, M., Diehl, M.: Nonlinear MPC and MHE for mechanical multi-body systems with application to fast tethered airplanes. In: Proceedings of the 4th IFAC Nonlinear Model Predictive Control Conference, Noordwijkerhout, The Netherlands (2012)

    Google Scholar 

  69. Grüne, L.: Analysis and design of unconstrained nonlinear MPC schemes for finite and infinite dimensional systems. SIAM J. Control Optim. 48(2), 1206–1228 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  70. Grüne, L.: Economic receding horizon control without terminal constraints. Automatica 49, 725–734 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  71. Grüne, L.: Approximation properties of receding horizon optimal control. Jahresber. Dtsch. Math. Ver 118(1), 3–37 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  72. Grüne, L., Palma, V.G.: On the benefit of re-optimization in optimal control under perturbations. In: 21st International Symposium on Mathematical Theory of Networks and Systems (MTNS 2014), pp. 439–446 (2014)

    Google Scholar 

  73. Grüne, L., Palma, V.G.: Robustness of performance and stability for multistep and updated multistep MPC schemes, Discret. Continuous Dyn. Syst. A 35(9), 4385–4414 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  74. Grüne, L., Pannek, J.: Nonlinear model predictive control: theory and algorithms. In: Communications and Control Engineering. Springer, London (2011)

    Book  MATH  Google Scholar 

  75. Grüne, L., Rantzer, A.: On the infinite horizon performance of receding horizon controllers. IEEE Trans. Autom. Control 53(9), 2100–2111 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  76. Hairer, E., Nørsett, S.P., Wanner, G.: Solving Ordinary Differential Equations I: Nonstiff Problems. Springer Science & Business, New York (2008)

    MATH  Google Scholar 

  77. Hargraves, C.R., Paris, S.W.: Direct trajectory optimization using nonlinear programming and collocation. AIAA J. Guid. Control, Dyn. 10, 338–342 (1987)

    Google Scholar 

  78. Hartl, R.F., Sethi, S.P., Vickson, R.G.: A survey of the maximum principles for optimal control problems with state constraints. SIAM Rev. 37, 181–218 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  79. Heidarinejad, M., Liu, J., Christofides, P.D.: Economic model predictive control using Lyapunov techniques: handling asynchronous, delayed measurements and distributed implementation. In: Proceedings of the 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC) (2011)

    Google Scholar 

  80. Heidarinejad, M., Liu, J., Christofides, P.D.: Economic model predictive control of switched nonlinear systems. Syst. Control Lett. 62, 77–84 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  81. Horn, G., Gros, S., Diehl, M.: Airborne Wind Energy. Springer, Berlin (2013)

    Google Scholar 

  82. Houska, B., Diehl, M.: Optimal control for power generating kites. In: Proceedings of the 9th European Control Conference, pp. 3560–3567. Kos, Greece (2007). (CD-ROM)

    Google Scholar 

  83. Houska, B., Ferreau, H., Diehl, M.: An auto-generated real-time iteration algorithm for nonlinear MPC in the microsecond range. Automatica 47(10), 2279–2285 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  84. Huang, R., Biegler, L.T., Harinath, E.: Robust stability of economically oriented infinite horizon NMPC that include cyclic processes. J. Process Control 22, 51–29 (2012)

    Article  Google Scholar 

  85. Ioffe, A.D., Tikhomirov, V.M., Makowski, K.: Theory of Extremal Problems. Elsevier, North-Holland, New York (2009)

    Google Scholar 

  86. Jadbabaie, A., Hauser, J.: On the stability of receding horizon control with a general terminal cost. IEEE Trans. Autom. Control 50(5), 674–678 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  87. Karush, W.: Minima of Functions of Several Variables with Inequalities as Side Constraints. Master’s Thesis, Department of Mathematics, University of Chicago (1939)

    Google Scholar 

  88. Kehrle, F., Frasch, J.V., Kirches, C., Sager, S.: Optimal control of formula 1 race cars in a VDrift based virtual environment. In: Bittanti, S., Cenedese, A., Zampieri, S. (eds.) Proceedings of the 18th IFAC World Congress, pp. 11,907–11,912 (2011)

    Google Scholar 

  89. Kerrigan, E.: Robust constraint satisfaction: invariant sets and predictive control. Ph.D. Thesis, University of Cambridge (2000)

    Google Scholar 

  90. Khalil, H.: Nonlinear Systems. Prentice Hall, Upper Saddle River (2002)

    MATH  Google Scholar 

  91. Kiencke, U., Nielsen, L.: Automotive Control Systems. Springer, Berlin (2005)

    Book  Google Scholar 

  92. Knauer, M., Büskens, C.: From WORHP to TransWORHP. In: 5th International Conference on Astrodynamics Tools and Techniques, Noordwijk, Netherlands. Proceedings of the 5th International Conference on Astrodynamics Tools and Techniques (2012)

    Google Scholar 

  93. Kühl, P., Diehl, M., Kraus, T., Schlöder, J.P., Bock, H.G.: A real-time algorithm for moving horizon state and parameter estimation. Comput. Chem. Eng. 35(1), 71–83 (2011)

    Article  Google Scholar 

  94. Laks, J., Pao, L., Wright, A.: Control of wind turbines: past, present, and future. In: American Control Conference, pp. 2096–2103 (2009)

    Google Scholar 

  95. Landry, C., Gerdts, M., Henrion, R., Hömberg, D.: Path planning and Collision avoidance for robots. Numer. Algebra Control Optim. 2, 437–463 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  96. Li, W., Biegler, L.: Multistep, Newton-type control strategies for constrained nonlinear processes. Chem. Eng. Res. Des. 67, 562–577 (1989)

    Google Scholar 

  97. Liu, D.C., Nocedal, J.: On the limited memory BFGS method for large scale optimization. Math. Program. 45, 503–528 (1989)

    Article  MathSciNet  MATH  Google Scholar 

  98. Loyd, M.: Crosswind kite power. J. Energy 4(3), 106–111 (1980)

    Article  Google Scholar 

  99. Marler, R., Arora, J.: Survey of multi-objective optimization methods for engineering. Struct. Multidiscip. Optim. 26(6), 369–395 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  100. Maurer, H., Pesch, H.: Solution differentiability for parametric nonlinear control problems with control-state constraints. J. Optim. Theory Appl. 86(2), 285–309 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  101. Mayne, D., Rawlings, J.: Model Predictive Control. Nob Hill, Madison (2013)

    Google Scholar 

  102. Middlebrook, R., Cuk, S.: A general unified approach to modeling switching-converter power stages. Int. J. Electron. 42(6), 521–550 (1977)

    Article  Google Scholar 

  103. Mitchell, I.M.: A toolbox of level set methods. Technical Report, University British Columbia, Vancouver, BC, Canada (2004)

    Google Scholar 

  104. Mitchell, I.M., Bayen, A.M., Tomlin, C.J.: A time-dependent Hamilton-Jacobi formulation of reachable sets for continuous dynamic games. IEEE Trans. Autom. Control 50, 947–957 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  105. Müller, M., Angeli, D., Allgöwer, F.: On convergence of averagely constrained economic MPC and necessity of dissipativity for optimal steady-state operation. In: Proceedings of the American Control Conference (2013)

    Book  Google Scholar 

  106. Müller, M., Angeli, D., Allgöwer, F.: On necessity and robustness of dissipativity in economic model predictive control. IEEE Trans. Autom. Control 60(6), 1671–1676 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  107. Müller, M.A., Grüne, L.: Economic model predictive control without terminal constraints for optimal periodic behavior. Automatica 70(C), 128–139 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  108. Nocedal, J., Wright, S.: Numerical Optimization. Springer Series in Operations Research and Financial Engineering, 2 edn. Springer, Berlin (2006)

    Google Scholar 

  109. Oberle, H.J., Grimm, W.: BNDSCO: a program for the numerical solution of optimal control problems. Institut für Angewandte Mathematik (2001)

    Google Scholar 

  110. Ohtsuka, T.: A Continuation/GMRES method for fast computation of nonlinear receding horizon control. Automatica 40(4), 563–574 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  111. Otte, D., Jänsch, M., Haasper, C.: Injury protection and accident causation parameters for vulnerable road users based on German In-Depth Accident Study (GIDAS). Accid. Anal. Prev. 44, 149–153 (2012)

    Article  Google Scholar 

  112. Pacejka, H.B.: Tyre and Vehicle Dynamics. Elsevier, Delft (2006)

    MATH  Google Scholar 

  113. Paelinck, R., Geebelen, K., Gros, S., Swevers, J., Vandepitte, D., Diehl, M.: Snelle vliegers maken groene stroom. Het Ingenieursblad 5, 16–20 (2011)

    Google Scholar 

  114. Palma, V.G.: Robust updated MPC schemes. Ph.D. Thesis, University of Bayreuth (2015)

    Google Scholar 

  115. Palma, V.G., Grüne, L.: Stability, performance and robustness of sensitivity-based multistep feedback NMPC. In: 20th International Symposium on Mathematical Theory of Networks and Systems (MTNS 2012) (2012). Extended Abstract, CD-ROM, Paper No. 68, 4 pp.

    Google Scholar 

  116. Palma, V.G., Suardi, A., Kerrigan, E.: Sensitivity-based multistep MPC for embedded systems. In: 5th IFAC Conference on Nonlinear Model Predictive Control (NMPC 15), 6 pp. (2015)

    Google Scholar 

  117. Pamadi: Performance, Stability, Dynamics, and Control of Airplanes. American Institute of Aeronautics and Astronautics, Inc., Reston (2003)

    Google Scholar 

  118. Payne, P., McCutchen, C.: Self-Erecting Windmill. United States Patent 3987987 (1976). http://www.google.com/patents/about?id=njstAAAAEBAJ\&dq=4076190

  119. Pesch, H.J.: Numerical computation of neighboring optimum feedback control schemes in real-time. Appl. Math. Optim. 5(1), 231–252 (1979)

    Article  MathSciNet  MATH  Google Scholar 

  120. Pesch, H.J., Bulirsch, R.: The maximum principle, Bellman’s equation, and Carathéodory’s work. J. Optim. Theory Appl. 80, 199–225 (1994)

    Article  MathSciNet  MATH  Google Scholar 

  121. Primbs, J., Nevistić, V.: Feasibility and stability of constrained finite receding horizon control. Automatica 36, 965–971 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  122. Pytlak, R.: Runge-Kutta based procedure for the optimal control of differential-algebraic equations. J. Optim. Theory Appl. 97, 675–705 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  123. Quirynen, R., Houska, B., Vallerio, M., Telen, D., Logist, F., Van Impe, J., Diehl, M.: Symmetric algorithmic differentiation based exact hessian SQP method and software for economic MPC. In: Conference on Decision and Control, 2014

    Book  Google Scholar 

  124. Quirynen, R., Vukov, M., Diehl, M.: Auto generation of implicit integrators for embedded NMPC with microsecond sampling times. In: Lazar, M., Allgöwer, F. (eds.) Proceedings of the 4th IFAC Nonlinear Model Predictive Control Conference (2012)

    Google Scholar 

  125. Rauški, S.: Limited Memory BFGS method for Sparse and Large- Scale Nonlinear Optimization. Ph.D. Thesis, University Bremen (2014)

    Google Scholar 

  126. Rawlings, J., Bonne, D., Jorgensen, J., Venkat, A., Jorgensen, S.: Unreachable setpoints in model predictive control. IEEE Trans. Autom. Control 53, 2209–2215 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  127. Rawlings, J., Ji, L.: Optimization-based state estimation: current status and some new results. J. Process Control 22(8), 1439–1444 (2012)

    Article  Google Scholar 

  128. Rawlings, J., Mayne, D.: Model Predictive Control: Theory and Design. Nob Hill, Madison (2009)

    Google Scholar 

  129. Rawlings, J.B., Amrit, R.: Optimizing process economic performance using model predictive control. In: Proceedings of NMPC 08 Pavia (2009)

    Google Scholar 

  130. Robinson, S.: Perturbed Kuhn-Tucker points and rates of convergence for a class of nonlinearprogramming algorithms. Math. Program. 7, 1–16 (1974)

    Article  MATH  Google Scholar 

  131. Ross, I.M., Fahroo, F.: A unified computational framework for real-time optimal control. In: Proceedings of 42nd IEEE Conference on Decision and Control, pp. 2210–2215 (2003)

    Google Scholar 

  132. Shamma, J.S., Xiong, D.: Linear nonquadratic optimal control. IEEE Trans. Autom. Control 42(6), 875–879 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  133. Shanno, D.F.: Conditioning of quasi-Newton methods for function minimization. Math. Comput. 24, 647–656 (1970)

    Article  MathSciNet  MATH  Google Scholar 

  134. Sternberg, J., Goit, J., Gros, S., Meyers, J., Diehl, M.: Robust and stable periodic flight of power generating kite systems in a turbulent wind flow field. In: In Proceedings of the 15th IFAC Workshop on Control Applications of Optimization (2012)

    Google Scholar 

  135. Sternberg, J., Gros, S., Houska, B., Diehl, M.: Approximate robust optimal control of periodic systems with invariants and high-index differential algebraic systems. In: In Proceedings of the 7th IFAC Symposium on Robust Control Design, pp. 678–683 (2012)

    Google Scholar 

  136. Suardi, A., Longo, S., Kerrigan, E.C., Constantinides, G.A.: Energy-aware MPC co-design for DC-DC converters. In: 2013 European Control Conference (ECC), pp. 3608–3613. IEEE (2013)

    Google Scholar 

  137. Subchan, S., Zbikowski, R.: Computational Optimal Control: Tools and Practice. Wiley, New York (2009)

    Book  Google Scholar 

  138. Süli, E., Mayers, D.F.: An Introduction to Numerical Analysis. Cambridge University Press, Cambridge (2003)

    Book  MATH  Google Scholar 

  139. Teo, K., Jennings, L., Lee, H., Rehbock, V.: The control parameterization enhancing transform for constrained optimal control problems. J. Aust. Math. Soc. Ser. B. Appl. Math. 40, 314–335 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  140. Verschueren, R., Zanon, M., Quirynen, R., Diehl, M.: A sparsity preserving convexification procedure for indefinite quadratic programs arising in direct optimal control (2016). Available on Optimization Online: http://www.optimization-online.org/DB_HTML/2016/06/5512.html

  141. Vinter, R.: Optimal control. In: Systems & Control: Foundations & Applications. Birkhäuser, Boston (2000)

    Google Scholar 

  142. Wassel, D.: Exploring novel designs of NLP solvers: architecture and Implementation of WORHP. Ph.D. Thesis, University Bremen (2013)

    Google Scholar 

  143. Wilson, R.: A simplicial algorithm for concave programming. Ph.D. Thesis, Department of Computer Science. Stanford University (1963)

    Google Scholar 

  144. Worthmann, K.: Stability Analysis of unconstrained Receding Horizon Control. Ph.D. Thesis, University of Bayreuth (2011)

    Google Scholar 

  145. Wright, S., Nocedal, J.: Numerical Optimization. Springer, Berlin (1999)

    MATH  Google Scholar 

  146. Xausa, I., Baier, R., Gerdts, M., Gonter, M., Wegwerth, C.: Avoidance trajectories for driver assistance systems via solvers for optimal control problems. In: Proceedings of 20th International Symposium on Mathematical Theory of Networks and Systems (2012)

    Google Scholar 

  147. Yang, X., Biegler, L.T.: Advanced-multi-step nonlinear model predictive control. J. Process Control 23(8), 1116–1128 (2013)

    Article  Google Scholar 

  148. Zanon, M., Frasch, J., Diehl, M.: Nonlinear moving horizon estimation for combined state and friction coefficient estimation in autonomous driving. In: Proceedings of the European Control Conference (2013)

    Google Scholar 

  149. Zanon, M., Frasch, J.V., Vukov, M., Sager, S., Diehl, M.: Model predictive control of autonomous vehicles. In: Proceedings of the Workshop on Optimization and Optimal Control of Automotive Systems (2014)

    Book  Google Scholar 

  150. Zanon, M., Gros, S., Andersson, J., Diehl, M.: Airborne Wind Energy Based on Dual Airfoils. IEEE Trans. Control Syst. Technol. 21, 1215–1222 (2013)

    Article  Google Scholar 

  151. Zanon, M., Gros, S., Diehl, M.: A Lyapunov function for periodic economic optimizing model predictive control. In: Proceedings of the 52nd Conference on Decision and Control (CDC) (2013)

    Google Scholar 

  152. Zanon, M., Gros, S., Diehl, M.: Model predictive control of rigid-airfoil airborne wind energy systems. In: Ahrens, U., Diehl, M., Schmehl, R. (eds.) Airborne Wind Energy. Springer, Berlin (2013)

    Google Scholar 

  153. Zanon, M., Gros, S., Diehl, M.: Rotational start-up of tethered airplanes based on nonlinear MPC and MHE. In: Proceedings of the European Control Conference (2013)

    Google Scholar 

  154. Zanon, M., Gros, S., Diehl, M.: Indefinite linear MPC and approximated economic MPC for nonlinear systems. J. Process Control 24(8), 1273–1281 (2014)

    Article  Google Scholar 

  155. Zanon, M., Gros, S., Diehl, M.: A tracking MPC formulation that is locally equivalent to economic MPC. J. Process Control 45, 30–42 (2016)

    Article  Google Scholar 

  156. Zanon, M., Gros, S., Meyers, J., Diehl, M.: Airborne wind energy: airfoil-airmass interaction. In: Proceedings of the 19th World Congress of the International Federation of Automatic Control (2014)

    Google Scholar 

  157. Zanon, M., Horn, G., Gros, S., Diehl, M.: Control of dual-airfoil airborne wind energy systems based on nonlinear MPC and MHE. In: European Control Conference (2014)

    Book  Google Scholar 

  158. Zavala, V.M., Biegler, L.: The advanced step NMPC controller: optimality, stability and robustness. Automatica 45, 86–93 (2009)

    Article  MathSciNet  MATH  Google Scholar 

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Zanon, M., Boccia, A., Palma, V.G.S., Parenti, S., Xausa, I. (2017). Direct Optimal Control and Model Predictive Control. In: Tonon, D., Aronna, M., Kalise, D. (eds) Optimal Control: Novel Directions and Applications. Lecture Notes in Mathematics, vol 2180. Springer, Cham. https://doi.org/10.1007/978-3-319-60771-9_3

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