Approximation of the pareto optimal set for multiobjective optimal control problems using viability kernels

Alexis Guigue

ESAIM: Control, Optimisation and Calculus of Variations (2014)

  • Volume: 20, Issue: 1, page 95-115
  • ISSN: 1292-8119

Abstract

top
This paper provides a convergent numerical approximation of the Pareto optimal set for finite-horizon multiobjective optimal control problems in which the objective space is not necessarily convex. Our approach is based on Viability Theory. We first introduce a set-valued return function V and show that the epigraph of V equals the viability kernel of a certain related augmented dynamical system. We then introduce an approximate set-valued return function with finite set-values as the solution of a multiobjective dynamic programming equation. The epigraph of this approximate set-valued return function equals to the finite discrete viability kernel resulting from the convergent numerical approximation of the viability kernel proposed in [P. Cardaliaguet, M. Quincampoix and P. Saint-Pierre. Birkhauser, Boston (1999) 177–247. P. Cardaliaguet, M. Quincampoix and P. Saint-Pierre, Set-Valued Analysis 8 (2000) 111–126]. As a result, the epigraph of the approximate set-valued return function converges to the epigraph of V. The approximate set-valued return function finally provides the proposed numerical approximation of the Pareto optimal set for every initial time and state. Several numerical examples illustrate our approach.

How to cite

top

Guigue, Alexis. "Approximation of the pareto optimal set for multiobjective optimal control problems using viability kernels." ESAIM: Control, Optimisation and Calculus of Variations 20.1 (2014): 95-115. <http://eudml.org/doc/272960>.

@article{Guigue2014,
abstract = {This paper provides a convergent numerical approximation of the Pareto optimal set for finite-horizon multiobjective optimal control problems in which the objective space is not necessarily convex. Our approach is based on Viability Theory. We first introduce a set-valued return function V and show that the epigraph of V equals the viability kernel of a certain related augmented dynamical system. We then introduce an approximate set-valued return function with finite set-values as the solution of a multiobjective dynamic programming equation. The epigraph of this approximate set-valued return function equals to the finite discrete viability kernel resulting from the convergent numerical approximation of the viability kernel proposed in [P. Cardaliaguet, M. Quincampoix and P. Saint-Pierre. Birkhauser, Boston (1999) 177–247. P. Cardaliaguet, M. Quincampoix and P. Saint-Pierre, Set-Valued Analysis 8 (2000) 111–126]. As a result, the epigraph of the approximate set-valued return function converges to the epigraph of V. The approximate set-valued return function finally provides the proposed numerical approximation of the Pareto optimal set for every initial time and state. Several numerical examples illustrate our approach.},
author = {Guigue, Alexis},
journal = {ESAIM: Control, Optimisation and Calculus of Variations},
keywords = {multiobjective optimal control; Pareto optimality; viability theory; convergent numerical approximation; dynamic programming; set-valued map; set-valued return function; viability kernel; external stability; recession cone},
language = {eng},
number = {1},
pages = {95-115},
publisher = {EDP-Sciences},
title = {Approximation of the pareto optimal set for multiobjective optimal control problems using viability kernels},
url = {http://eudml.org/doc/272960},
volume = {20},
year = {2014},
}

TY - JOUR
AU - Guigue, Alexis
TI - Approximation of the pareto optimal set for multiobjective optimal control problems using viability kernels
JO - ESAIM: Control, Optimisation and Calculus of Variations
PY - 2014
PB - EDP-Sciences
VL - 20
IS - 1
SP - 95
EP - 115
AB - This paper provides a convergent numerical approximation of the Pareto optimal set for finite-horizon multiobjective optimal control problems in which the objective space is not necessarily convex. Our approach is based on Viability Theory. We first introduce a set-valued return function V and show that the epigraph of V equals the viability kernel of a certain related augmented dynamical system. We then introduce an approximate set-valued return function with finite set-values as the solution of a multiobjective dynamic programming equation. The epigraph of this approximate set-valued return function equals to the finite discrete viability kernel resulting from the convergent numerical approximation of the viability kernel proposed in [P. Cardaliaguet, M. Quincampoix and P. Saint-Pierre. Birkhauser, Boston (1999) 177–247. P. Cardaliaguet, M. Quincampoix and P. Saint-Pierre, Set-Valued Analysis 8 (2000) 111–126]. As a result, the epigraph of the approximate set-valued return function converges to the epigraph of V. The approximate set-valued return function finally provides the proposed numerical approximation of the Pareto optimal set for every initial time and state. Several numerical examples illustrate our approach.
LA - eng
KW - multiobjective optimal control; Pareto optimality; viability theory; convergent numerical approximation; dynamic programming; set-valued map; set-valued return function; viability kernel; external stability; recession cone
UR - http://eudml.org/doc/272960
ER -

References

top
  1. [1] J.-P. Aubin, Viability theory. Birkhauser, Boston (1991). Zbl1179.93001MR1134779
  2. [2] J.-P. Aubin and H. Frankowska, Set-Valued Analysis. Birkhauser, Boston (1990). Zbl1168.49014MR1048347
  3. [3] M. Bardi and I. Capuzzo-Dolcetta, Optimal Control and Viscosity Solutions of Hamilton-Jacobi-Bellman Equations. Birkhauser, Boston (1997). Zbl0890.49011MR1484411
  4. [4] P. Cardaliaguet, M. Quincampoix and P. Saint-Pierre, Set-valued numerical analysis for optimal control and differential games. Stochastic and differential games: Theory and numerical methods. Annals of the international Society of Dynamic Games, edited by M. Bardi, T.E.S. Raghavan, T. Parthasarathy. Birkhauser, Boston (1999) 177–247. Zbl0982.91014MR1678283
  5. [5] P. Cardaliaguet, M. Quincampoix and P. Saint-Pierre, Numerical schemes for discontinuous value functions of optimal control. Set-Valued Anal.8 (2000) 111–126. Zbl0988.49016MR1780578
  6. [6] P. Cardaliaguet, M. Quincampoix and P. Saint-Pierre, Differential games through viability theory: Old and recent results. Advances in Dynamic Game Theory. Annals of the international Society of Dynamic Games, edited by S. Jorgensen, M. Quincampoix, T.L. Vincent, T. Basar. Birkhauser, Boston (2007) 3–35. Zbl1152.91360MR2341236
  7. [7] V. Coverstone-Carroll, J.W. Hartmann and W.J. Mason, Optimal multi-objective low-thrust spacecraft trajectories. Comput. Methods Appl. Mech. Engrg.186 (2000) 387–402. Zbl0956.70020
  8. [8] A.J. Diaz de Leon and J.C. Seijo, A multi-criteria non-linear optimization model for the control and management of a tropical fishery. Mar. Resour. Econ.7 (1992) 23–40. 
  9. [9] K. Deb, Multi-objective optimization using evolutionary algorithms. John Wiley and Sons, Chichister (2001). Zbl1165.90019MR1840619
  10. [10] L. Doyen and P. Saint-Pierre, Scale of viability and minimal time of crisis. Set-Valued Anal.5 (1997) 227–246. Zbl0899.49003MR1486773
  11. [11] P.J. Fleming and R.C. Purshouse, Evolutionary algorithms in control systems engineering: a survey. Control Engrg. Pract.10 (2002) 1223–1241. 
  12. [12] A. Guigue, An approximation method for multiobjective optimal control problems application to a robotic trajectory planning problem. Submitted to Optim. Engrg. (2010). 
  13. [13] A. Guigue, Set-valued return function and generalized solutions for multiobjective optimal control problems (moc). Submitted to SIAM J. Control Optim. (2011). Zbl1273.49022MR3063146
  14. [14] A. Guigue, M. Ahmadi, M.J.D. Hayes and R.G. Langlois, A discrete dynamic programming approximation to the multiobjective deterministic finite horizon optimal control problem. SIAM J. Control Optim.48 (2009) 2581–2599. Zbl1203.49044MR2556358
  15. [15] A. Guigue, M. Ahmadi, R.G. Langlois and M.J.D. Hayes, Pareto optimality and multiobjective trajectory planning for a 7-dof redundant manipulator. IEEE Trans. Robotics26 (2010) 1094–1099. 
  16. [16] B.-Z. Guo and B. Sun, Numerical solution to the optimal feedback control of continuous casting process. J. Glob. Optim.39 (1998) 171–195. Zbl1123.49024MR2336370
  17. [17] A. Kumar and A. Vladimirsky, An efficient method for multiobjective optimal control and optimal control subject to integral constraints. J. Comp. Math.28 (2010) 517–551. Zbl1240.90345MR2666839
  18. [18] S. Mardle and S. Pascoe, A review of applications of multiple-criteria decision-making techniques to fisheries. Mar. Resour. Econ.14 (1998) 41–63. Zbl0643.90045
  19. [19] K.M. Miettinen, Nonlinear Multiobjective Optimization. Kluwer Academic Publishers, Boston (1999). Zbl0949.90082MR1784937
  20. [20] Y. Sawaragi, H. Nakayama and T. Tanino, Theory of Multiobjective Optimization. Academic Press, Inc., Orlando (1985). Zbl0566.90053MR807529
  21. [21] T. Tanino, Sensitivity analysis in multiobjective optimization. J. Optim. Theory Appl.56 (1988) 479–499. Zbl0619.90073MR930219
  22. [22] R. Vinter, Optimal Control. Birkauser, Boston (2000). Zbl1215.49002MR1756410
  23. [23] P.L. Yu, Cone convexity, cone extreme points, and nondominated solutions in decision problems with multiobjectives. J. Optim. Theory Appl.14 (1974) 319–377. Zbl0268.90057MR381739

NotesEmbed ?

top

You must be logged in to post comments.

To embed these notes on your page include the following JavaScript code on your page where you want the notes to appear.

Only the controls for the widget will be shown in your chosen language. Notes will be shown in their authored language.

Tells the widget how many notes to show per page. You can cycle through additional notes using the next and previous controls.

    
                

Note: Best practice suggests putting the JavaScript code just before the closing </body> tag.