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Feasibility and Robustness

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Nonlinear Model Predictive Control

Part of the book series: Communications and Control Engineering ((CCE))

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Abstract

In this chapter we consider two different but related issues. In the first part we discuss the feasibility problem, i.e., that the nominal NMPC closed-loop solutions remain inside a set on which the finite horizon optimal control problems defining the NMPC feedback law are feasible. We formally define the property of recursive feasibility and explain why the assumptions of the previous chapters, i.e., viability of the state constraint set or of the terminal constraint set ensure this property. Then we present two ways to relax the viability assumption on the state constraint set in the case that no terminal constraints are used. After a comparative discussion on NMPC schemes with and without stabilizing terminal conditions, we start with the second part of the chapter in which robustness of the closed loop under additive perturbations and measurement errors is investigated. Here robustness concerns both the feasibility and admissibility as well as the stability of the closed loop. We provide different assumptions and resulting NMPC schemes for which we can rigorously prove such robustness results and also discuss examples which show that in general robustness may fail to hold.

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Notes

  1. 1.

    In fact, the infeasibility already disappears for \(N=3\) and \(N=4\) but this is not covered by our theorem.

  2. 2.

    We only learned about this after the first edition of this book was published.

References

  1. Artstein, Z.: Stabilization with relaxed controls. Nonlinear Anal. 7(11), 1163–1173 (1983)

    Article  MathSciNet  MATH  Google Scholar 

  2. 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 

  3. Camilli, F., Grüne, L., Wirth, F.: Control Lyapunov functions and Zubov’s method. SIAM J. Control Optim. 47, 301–326 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  4. Curtain, R.F., Zwart, H.: An Introduction to Infinite-Dimensional Linear Systems Theory. Texts in Applied Mathematics, vol. 21. Springer, New York (1995)

    MATH  Google Scholar 

  5. De Nicolao, G., Magni, L., Scattolini, R.: On the robustness of receding-horizon control with terminal constraints. IEEE Trans. Autom. Control 41(3), 451–453 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  6. Grimm, G., Messina, M.J., Tuna, S.E., Teel, A.R.: Examples when nonlinear model predictive control is nonrobust. Automatica 40(10), 1729–1738 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  7. Grimm, G., Messina, M.J., Tuna, S.E., Teel, A.R.: Model predictive control: for want of a local control Lyapunov function, all is not lost. IEEE Trans. Autom. Control 50(5), 546–558 (2005)

    Article  MathSciNet  Google Scholar 

  8. Grimm, G., Messina, M.J., Tuna, S.E., Teel, A.R.: Nominally robust model predictive control with state constraints. IEEE Trans. Autom. Control 52(10), 1856–1870 (2007)

    Article  MathSciNet  Google Scholar 

  9. Grüne, L., Nešić, D.: Optimization based stabilization of sampled-data nonlinear systems via their approximate discrete-time models. SIAM J. Control Optim. 42, 98–122 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  10. Kellett, C.M., Shim, H., Teel, A.R.: Further results on robustness of (possibly discontinuous) sample and hold feedback. IEEE Trans. Autom. Control 49(7), 1081–1089 (2004)

    Article  MathSciNet  Google Scholar 

  11. Kerrigan, E.C.: Robust constraint satisfaction: Invariant sets and predictive control. PhD thesis, University of Cambridge (2000)

    Google Scholar 

  12. Langson, W., Chryssochoos, I., Raković, S.V., Mayne, D.Q.: Robust model predictive control using tubes. Automatica 40(1), 125–133 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  13. Lazar, M., Heemels, W.P.M.H.: Predictive control of hybrid systems: input-to-state stability results for sub-optimal solutions. Automatica 45(1), 180–185 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  14. Limón, D., Alamo, T., Camacho, E.: Input-to-state stable MPC for constrained discrete-time nonlinear systems with bounded additive uncertainties. In: Proceedings of the 41st IEEE Conference on Decision and Control - CDC 2002, Las Vegas, Nevada, pp. 4619–4624 (2002)

    Google Scholar 

  15. Limón, D., Alamo, T., Raimondo, D.M., Muñoz de la Peña, D., Bravo, J.M., Ferramosca, A., Camacho, E.F.: Input-to-state stability: a unifying framework for robust model predictive control. In: Magni, L., Raimondo, D.M., Allgöwer, F. (eds.) Nonlinear Model Predictive Control. Lecture Notes in Control and Information Sciences, vol. 384, pp. 1–26. Springer, erlin (2009)

    Chapter  Google Scholar 

  16. Magni, L., Scattolini, R.: Robustness and robust design of MPC for nonlinear discrete-time systems. In: Findeisen, R., Allgöwer, F., Biegler, L.T. (eds.) Assessment and Future Directions of Nonlinear Model Predictive Control. Lecture Notes in Control and Information Sciences, vol. 358, pp. 239–254. Springer, Berlin (2007)

    Chapter  Google Scholar 

  17. Mayne, D.Q.: An apologia for stabilising terminal conditions in model predictive control. Int. J. Control 86(11), 2090–2095 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  18. Michalska, H., Mayne, D.Q.: Robust receding horizon control of constrained nonlinear systems. IEEE Trans. Autom. Control 38(11), 1623–1633 (1993)

    Article  MathSciNet  MATH  Google Scholar 

  19. Nešić, D., Teel, A.R., Kokotović, P.V.: Sufficient conditions for stabilization of sampled-data nonlinear systems via discrete-time approximations. Syst. Control Lett. 38(4–5), 259–270 (1999)

    MathSciNet  MATH  Google Scholar 

  20. Primbs, J.A., Nevistić, V.: Feasibility and stability of constrained finite receding horizon control. Automatica 36(7), 965–971 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  21. Quincampoix, M.: Differential inclusions and target problems. SIAM J. Control Optim. 30(2), 324–335 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  22. Raimondo, D.M., Limón, D., Lazar, M., Magni, L., Camacho, E.F.: Min-max model predictive control of nonlinear systems: a unifying overview on stability. Europ. J. Control 15(1), 5–21 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  23. Rawlings, J.B., Mayne, D.Q.: Model Predictive Control: Theory and Design. Nob Hill Publishing, Madison (2009)

    Google Scholar 

  24. Shim, D.H., Jin Kim, H., Sastry, S.: Decentralized nonlinear model predictive control of multiple flying robots. In: Proceedings of the 42nd IEEE Conference on Decision and Control - CDC 2003, Maui, Hawaii, USA, pp. 3621–3626 (2003)

    Google Scholar 

  25. Soner, H.M.: Optimal control with state-space constraint. I and II. SIAM J. Control Optim. 24(3), 552–561, 1110–1122 (1986)

    Google Scholar 

  26. Yu, S., Böhm, C., Chen, H., Allgöwer, F.: Robust model predictive control with disturbance invariant sets. In: Proceedings of the American Control Conference - ACC 2010, Baltimore, Maryland, USA, pp. 6262–6267 (2010)

    Google Scholar 

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Correspondence to Lars Grüne .

Problems

Problems

  1. 1.

    Consider the feasible set \(\mathscr {F}_N\) for a constraint set \(\mathbb {X}\subset X\) and an optimization horizon \(N\in \mathbb {N}\) according to Definition 7.2. Assume that for a point \(x\in \mathbb {X}\) and some \(K\in \mathbb {N}\) there exists an admissible control sequence \(u\in \mathbb {U}^K(x)\) with \(x_u(K,x)\in \mathscr {F}_N\). Prove that \(x\in \mathscr {F}_{N+K}\) holds.

  2. 2.

    Consider a symmetric matrix \(Q\in \mathbb {R}^{n\times n}\) and a constant \(C>0\) such that the inequality \(|x^\top Q y | \le C\Vert x\Vert \Vert y\Vert \) holds for all \(x,y\in \mathbb {R}^n\). Let \(\rho >0\) be given and consider the set \(A=\overline{\mathscr {B}}_\rho (0)\).

    1. (a)

      Show that

      $$\begin{aligned} \omega (r) = 2C\rho \, r \end{aligned}$$

      is a modulus of continuity of the function \(W(x)=x^\top Q x\) on A.

    2. (b)

      Compute a modulus of continuity of the function \(W(x)=(x^\top Q x)^2\) on A.

  3. 3.

    Verify the following facts that have been used in Example 7.31.

    1. (a)

      For \(x\in \mathbb {R}^2\) with \(x_2>0\) and \(u\in \mathbb {R}\) with \(u<0\) the step \(x^+=f(x,u)\) defines a clockwise movement.

    2. (b)

      For all \(c\in (0,1)\), all circles \(S_r\) with \(r > r_c = c/\sqrt{1-c^2}\) and all points \(x\in S_{r}\cap \mathbb {X}\) with \(x_2 > r\) and \(x_1=c\) the relation \(f(x,-1)\not \in \mathbb {X}\) holds. Use this fact to conclude that for all initial values \(x\in S_{r}\cap \mathbb {X}\) with \(x_2 > r\) it is not possible to move clockwise toward 0.

    3. (c)

      For all \(c<1/2\) and \(y'\in Y'\) with \(\varepsilon >0\) sufficiently small the inequality \(\ell (f(y',-1)) > \ell (y')\) holds.

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Grüne, L., Pannek, J. (2017). Feasibility and Robustness. In: Nonlinear Model Predictive Control. Communications and Control Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-46024-6_7

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  • DOI: https://doi.org/10.1007/978-3-319-46024-6_7

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