Deterministic Decision Making

  • Yasmina Bestaoui Sebbane
Part of the Intelligent Systems, Control and Automation: Science and Engineering book series (ISCA, volume 71)


Decision Making is mission level autonomy and intelligence: Given an agent that can fly and sense its environment, the considered task is to plan intelligent motions and take decisions when required. If one has perfect information of the environmental conditions that will be encountered, a safe path can be constructed. Symbolic planning methods such as hybrid automaton and linear temporal logic are first presented. Symbolic motion planning is the problem of automatic construction of robot control strategies from task specifications given in high level human like language. Some computational intelligence approaches follow such as neural networks, evolution algorithms, decision tables and fuzzy systems. Intelligent decision making, the discipline where planning algorithms are developed by emulating certain characteristics of intelligent biological system is an emerging area of planning. One important application in aerial robotics being the choice of the way points, some operations research methods, such as traveling salesman problem, chinese postman problem and rural postman problem are presented. They enable to formulate and solve such flight planning problems. Finally, some case studies are discussed. The first one concerns surveillance mission using neural networks as function approximation tools to improve computational efficiency of a direct trajectory optimization. The second one proposes a flight route planning technique for autonomous navigation of an aerial robot based on the combination of evolutionary algorithms and virtual potential fields. The third application concerns bridge monitoring. The aerial robot is required to take photos of thousands of points located on an bridge. So the problem of choosing adequate subsets of waypoints appear while the environmental constraints must be verified, the proposed solution uses operational research techniques. The final case is soaring flight for an airplane like robot, as appropriate flight techniques are expected to allow extraction of energy from the atmosphere.


  1. 1.
    Akos Z, Nagy M, Leven S, Vicsek T (2010) Thermal soaring flight of birds and UAV. Bioinspiration Biomimetics 5(4):045003CrossRefGoogle Scholar
  2. 2.
    Albluwi I, Elnagar A (2010) Pursuit evasion in dynamic environments with visibility constraints. In: Liu H (ed), ICIRA 2010, pp 116–129Google Scholar
  3. 3.
    Altshuler Y, Bruckstein A (2011) Static and expanding grid coverage with ant robots: complexity results. Theoret. Comput. Sci. 41:4661–4674CrossRefMathSciNetGoogle Scholar
  4. 4.
    Arkin EM, Mitchell JS, Polishchuk V (2010) Maximum thick paths in static and dynamic environments. Comput Geom Theory Appl 43:279–294CrossRefMATHMathSciNetGoogle Scholar
  5. 5.
    Atkins E, Moylan G, Hoskins A (2006) Space based assembly with symbolic and continuous planning experts. In: IEEE aerospace conference. doi:  10.1109/AERO.2006.1656009
  6. 6.
    Bakolas E, Tsiotras P (2012) The Zermelo-Voronoi diagram, a dynamic partition problem. Automatica 46:2059–2067CrossRefMathSciNetGoogle Scholar
  7. 7.
    Belta C, Bicchi A, Egersted M, Frazzoli E, Klavins E, Pappas G (2007) Symbolic planning and control of robot motion. IEEE Robot Autom Mag 14:61–70CrossRefGoogle Scholar
  8. 8.
    Ben Asher JZ (2010) Optimal control theory with aerospace applications. AIAA Press, RestonCrossRefGoogle Scholar
  9. 9.
    Berger J, Barkaoui M, Boukhtouta A (2007) A hybrid genetic approach for airborne sensor vehicle routing in real-time reconnaissance missions. Aerosp Sci Technol 11(4):317–326Google Scholar
  10. 10.
    Bertsekas DP (1995) Dynamic programming and optimal control, vol 1. Athena Scientific, BelmontMATHGoogle Scholar
  11. 11.
    Bertsekas DP, Tsitsiklis JN (1996) Neuro-dynamic programming, vol 1. Athena Scientific, BelmontMATHGoogle Scholar
  12. 12.
    Bertsimas D, VanRyzin G (2011) The dynamic traveling repairman problem, MIT Sloan paper 3036–89-MSGoogle Scholar
  13. 13.
    Bestaoui Y (2011) Bridge monitoring by a lighter than air robot. In: AIAA aerospace sciences meeting including new horizons forum, OrlandoGoogle Scholar
  14. 14.
    Bestaoui Y (2012) Lighter than air robot planning under uncertain wind. In: 9th International Airship Convention, Ashford, GBGoogle Scholar
  15. 15.
    Bestaoui Y, Ait Taleb N, Oukid S (2011) Route planning under uncertainty for a lighter than air Robot in bridge monitoring, In: 3rd CEAS ConferenceGoogle Scholar
  16. 16.
    Bestaoui Y, Dahmani H, Belharet K (2009) Geometry of translational trajectories for an autonomous aerospace vehicle with wind effect. In: 47th AIAA aerospace sciences meeting, Orlando, Florida, paper AIAA-1352Google Scholar
  17. 17.
    Bestaoui Y, Dicheva S (2010) 3D flight plan for an autonomous aircraft. In: 48th AIAA aerospace sciences meeting, Orlando, Florida, paper AIAA-1352Google Scholar
  18. 18.
    Bestaoui Y, Kahale E (2011) Time optimal trajectories for an autonomous airship. In: IEEE workshop on robot motion control (ROMOCO 2011). Bukowy Dworek, PolandGoogle Scholar
  19. 19.
    Bestaoui Y, Lakhlef F (2010) Flight plan for an autonomous aircraft in a windy environment. In Lozano R (ed) Unmanned aerial vehicles embedded control. Wiley, ISBN-13-9781848211278Google Scholar
  20. 20.
    Bianchi L (2000) Notes on dynamic vehicle routing–the state of the art. Technical report IDSIA-05-01Google Scholar
  21. 21.
    Bijlsma SJ (2009) Optimal aircraft routing in general wind fields. J Guid Contr Dyn 32:1025–1029. doi: 10.2514/1.42425 CrossRefGoogle Scholar
  22. 22.
    Boutilier C, Dean T, Hawks S (1999) Decision theoretic planning: structural assumptions and computational leverage. J. Artif Intell Res 11:1–94MATHGoogle Scholar
  23. 23.
    Bryson AE, Ho YC (1975) Applied optimal control: optimization, estimation and control. Taylor and Francis, New YorkGoogle Scholar
  24. 24.
    Cao Y, Muse J, Casbeer D, Kingston D (2013) Circumnavigation of an unknown target using UAV with and and range rate measurements, arXiv preprint arXiv:1308.6250, 2013 - arxiv.orgGoogle Scholar
  25. 25.
    Cassandras C, Ding X, Liu X (2011) An optimal control approach for the persistent monitoring problem. In: IEEE conference on decision and control and European control conference (CDC-ECC), pp 2907–2912Google Scholar
  26. 26.
    Chakravarty A, Langelaan JW (2011) Energy-based long-range path planning for soaring capable unmanned aerial vehicles. AIAA J Guid Control Dyn 34:1002–1015CrossRefGoogle Scholar
  27. 27.
    Chen G, Pham TT (2001) Introduction to fuzzy sets, fuzzy logic and fuzzy control system. CRC Press, New YorkGoogle Scholar
  28. 28.
    Colgren RD (2007) Basic Matlab simulink and stateflow. AIAA Press, Reston VaGoogle Scholar
  29. 29.
    Cook WJ (2012) In pursuit of the traveling salesman: mathematics at the limits of computation. Princeton University Press, PrincetonGoogle Scholar
  30. 30.
    Cotta C, Van Hemert I (2008) Recent advances in evolutionary computation for combinatorial optimization. Springer BerlinGoogle Scholar
  31. 31.
    Cummings ML, Marquez J, Roy N (2012) Human automated path planning optimization and decision support. Int J Hum Comput Stud 70:116–128CrossRefGoogle Scholar
  32. 32.
    Dantam N, Stilman M (2012) The motion grammar, Robotics Science and Systems, MIT Press, CambridgeGoogle Scholar
  33. 33.
    Dantzig G, Fulkerson R, Johnson S (1954) Solution of a large-scale traveling-salesman problem. J Oper Res Soc Am 2(4):393–410MathSciNetGoogle Scholar
  34. 34.
    Dantzig GB, Ramser JH (1959) The truck dispatching problem. Manage Sci 6(1):80–91CrossRefMATHMathSciNetGoogle Scholar
  35. 35.
    Ding X, Belta C, Cassandras CG (2010) Receding horizon surveillance with temporal logic specifications. In: IEEE control and decision conference, pp 256–261Google Scholar
  36. 36.
    Edelkamp S, Kissman P (2009) Optimal symbolic planning with action costs and preferences. In: International joint conference on artificial intelligence, pp 1690–1695Google Scholar
  37. 37.
    Etele J (2006) Overview of wind gust modelling with application to autonomous low level UAV control. Contract report, DRDC-OTTAWA-CR-2006-221Google Scholar
  38. 38.
    Fainekos GE, Girard A, Kress-Gazit H, Pappas GJ (2009) Temporal logic motion planning for dynamic robots. Automatica 45:343–353CrossRefMATHMathSciNetGoogle Scholar
  39. 39.
    Fokkink W (2000) Introduction to process algebra. Springer, BerlinCrossRefMATHGoogle Scholar
  40. 40.
    Frazzoli E, Dahleh MA, Feron E (2008) Maneuver based motion planning for nonlinear systems with symmetries. IEEE Trans Robot 4:355–365Google Scholar
  41. 41.
    Frazzoli E, Dahleh MA, Feron E (2002) Real time motion planning for agile autonomous vehicles. AIAA J Guid Control Dyn 25:116–129Google Scholar
  42. 42.
    Frederickson G, Wittman B (2009) Speedup in the traveling repairman problem with unit time window. doi:arXiv:0907.5372[cs.DS]Google Scholar
  43. 43.
    Gao Z, Gu H, Liu H (2009) Real time simulation of large aircraft flying through microburst wind field. Chin J Aeronaut 22:459–466CrossRefGoogle Scholar
  44. 44.
    Gao CR, Guo J (2010) Pareto optimal coordination of multiple robots with safety guarantees. Auton Robot 32:189–205Google Scholar
  45. 45.
    Goldberg DE (2008) Genetic algorithms. Addison Wesley publishing company, MassachusettsGoogle Scholar
  46. 46.
    Grace J, Baillieul J (2005) Stochastic strategies for autonomous robotic surveillance. In: IEEE Conference on Decision and Control, pp 2200–2205Google Scholar
  47. 47.
    Guerrero JA, Bestaoui Y, Lozano R (2012) Optimal guidance for rotorcraft platoon formation flying in wind fields. In: Guerrero JA, Lozano R (ed) Unmanned aerial vehicles formation, Wiley-ISTE, ISBN: 978-1-84821-323-4, (2012)Google Scholar
  48. 48.
    Guerrero JA, Bestaoui Y (2013) UAV path planning for structure inspection in windy environments. J Intell Robot Syst 69:297–311CrossRefGoogle Scholar
  49. 49.
    Guha S, Munagala K, Shi P (2010) Approximation algorithms for restless bandit problems. J ACM (JACM) 58. doi:  10.1145/1870103.1870106
  50. 50.
    Gulrez T, Hassanien AE (2012) Advances in robotics and virtual reality. Springer, BerlinCrossRefGoogle Scholar
  51. 51.
    Gunetti P, Thompson H, Dodd T (2013) Simulation of a soar based autonomous mission management system for unmammed aircraft. AIAA J Aerosp Inf Syst 10:53–70Google Scholar
  52. 52.
    Haykin S (2009) Neural networks and learning machines. Pearson Education, New JerseyGoogle Scholar
  53. 53.
    Horn J, Schmidt E, Geiger BR, DeAngelo M (2012) Neural network based trajectory optimization for UAV. AIAA J Guid Control Dyn 35:548–562CrossRefGoogle Scholar
  54. 54.
    Hristu-Varsakelis D, Egerstedt M, Krishnaprasad P (2003) On the structural complexity of the motion description language MDLe. In: 42nd IEEE conference on decision and control, pp 3360–3365Google Scholar
  55. 55.
    Jardin MR, Bryson AE (2001) Neighboring optimal aircraft guidance in winds. AIAA J Guid Control Dyn 24:710–715CrossRefGoogle Scholar
  56. 56.
    Jensen R, Shen Q (2008) Computational intelligence and feature selection. IEEE PressGoogle Scholar
  57. 57.
    Jiang C, Chang Y Evolutionary graph based particle swarm optimized fuzzy controller with application to mobile robot navigation in unknown environmentsGoogle Scholar
  58. 58.
    Jiang M, Yu Y, Liu X, Zhang F, Hong Q (2012) Fuzzy-neural network based dynamic path planning. In: 2012 International conference on machine learning and cybernetics, Xian, pp 326–330Google Scholar
  59. 59.
    Karaman S, Frazzoli E (2008) Vehicle routing with Linear temporal logic specifications: application to multi UAV mission planning. In: AIAA conference on guidance, navigation and control. doi:  10.2514/6.2008-6838
  60. 60.
    Karimi A, Siarry P (2012) Global simplex optimization, a simple and efficient metaheuristic for continous optimization. Eng Appl Artif Intell 25:48–55CrossRefGoogle Scholar
  61. 61.
    Karimi J, Pourtakdoust SH (2013) Optimal maneuver based motion planning over terrain and threats using a dynamic hybrid PSO algorithm. Aerosp Sci Technol. doi: 10.1016/j.ast2012.02.014 Google Scholar
  62. 62.
    Kelley T, Avery E, Long L, Dimperio E (2009) A hybrid symbolic and sub-symbolic intelligent system for mobile robots. In: AIAA infotech \(@\) aerospace conference 2009Google Scholar
  63. 63.
    Kress-Gazit H, Fainekos GE, Pappas GJ (2009) Temporal logic based reactive mission and motion planning. IEEE Trans Robot 25:1370–1381CrossRefGoogle Scholar
  64. 64.
    Kuroki Y, Young G, Haupt SE (2010) UAV navigation by an expert system for contaminant mapping with a genetic algorithm. Expert Syst Appl 37:4687–4697CrossRefGoogle Scholar
  65. 65.
    Lam TM (ed) (2009) Aerial Vehicles. In-Tech, ViennaGoogle Scholar
  66. 66.
    Larsen A, Mardsen O, Solomon M (2002) Partially dynamic vehicle routing models and algorithms. J Oper Res soc 53:637–646CrossRefMATHGoogle Scholar
  67. 67.
    Lawler EL, Lenstra JK, Rinnoy Kan AHG, Shmoys DB (1995) A guided tour of combinatorial optimization. Wiley, New YorkGoogle Scholar
  68. 68.
    Lee S, Bang H (2007) 3D ascent trajectory optimization for stratospheric airship platforms in the jet stream. AIAA J Guid Control Dyn 30:1341–1352CrossRefGoogle Scholar
  69. 69.
    LeNy J, Feron E, Frazzoli E (2012) On the dubins traveling salesman problem. IEEE Trans Autom Control 57:265–270Google Scholar
  70. 70.
    Levine W (2011) The control hankbook. CRC Press, Boca RatonGoogle Scholar
  71. 71.
    Lin CL, Lee CS, Huang CH, Kao TZ (2012) Unmanned aerial vehicles evolutional flight route planner using the potential field approach. AIAA J Aerosp Comput Inf and Commun 9:92–109CrossRefGoogle Scholar
  72. 72.
    Lorenz RD (2001) Flight power scaling of airplanes, airships and helicopters: application to planetary exploration. AIAA J aircr 38:208–214CrossRefGoogle Scholar
  73. 73.
    Marigo A, Bichi A (1998) Steering driftless nonholonomic systems by control quanta. In: IEEE international conference on decision and control, vol 4, pp 466–478Google Scholar
  74. 74.
    Martin P, Egerstedt M (2008) Optimal timing control of interconnected, switched systems with applications to robotic marionettes. 9th international workshop on discrete event systems. Goteborg, Sweden, pp 156–161Google Scholar
  75. 75.
    Mattei M, Blasi L (2010) Smooth flight trajectory planning in the presence of no-fly zones and obstacles. AIAA J Guid Control Dyn 33(2):454–462CrossRefGoogle Scholar
  76. 76.
    Mendel JM, John RI, Liu F (2006) Interval Type 2 fuzzy logic systems made simple. IEEE Trans Fuzzy Syst 14:802–821Google Scholar
  77. 77.
    Mufalli F, Batta R, Nagi R (2012) Simultaneous sensor selection and routing of UAV for complex mission plans. Comput Oper Res 39:2787–2799CrossRefMATHGoogle Scholar
  78. 78.
    Mumford CL, Jain LC (eds) (2009) Computational intelligence: collaboration, fusion and emergence. Springer, Berlin, pp 131–173Google Scholar
  79. 79.
    Obermeyer K, Oberlin P, Darbha S (2012) Sampling based path planning for a visual reconnaissance UAV. AIAA J Guid Control Dynamics 35:619–631CrossRefGoogle Scholar
  80. 80.
    Oikonomopoulos AS, Kyriakopoulos KJ, Loizou SG (2010) Modeling and control of heterogeneous nonholonomic input-constrained multiagent systems. In: 49th IEEE conference on decision and control, pp 4204–4209Google Scholar
  81. 81.
    Palade V, Danut Bocaniala C, Jain LC (eds) (2006) Computational intelligence in fault diagnosis. Springer, BerlinGoogle Scholar
  82. 82.
    Pashilkar AA, Sundararajan N, Saratchandran P (2006) A fault-tolerant neural aided controller for aircraft auto-landing. Aerosp Sci Technol 10:49–61CrossRefGoogle Scholar
  83. 83.
    Pastor E, Royo P, Santamaria E, Prats X (2012) In flight contingency management for unmanned aircraft systems. J Aerosp Comput Inf Commun 9:144–160CrossRefGoogle Scholar
  84. 84.
    Peng Z, Li B, Chen X, Wu J (2012) Online route planning for UAV based on model predictive control and particle swarm optimization algorithm. In: World congress on international control and automation, Beijing, pp 397–401Google Scholar
  85. 85.
    Plaku E, Hager GD (2010) Sampling based motion and symbolic action planning with geometric and differential constraint. In: IEEE international conference on robotics and automation, pp 5002–5008Google Scholar
  86. 86.
    Pongpunwattana A, Rysdyk R (2007) Evolution-based dynamic path planning for autonomous vehicles. Stud Comput Intell 70:113–145CrossRefGoogle Scholar
  87. 87.
    Pourtakdoust SH, Kiani M, Hassanpour A (2011) Optimal trajectory planning for flight through microburst wind shears. Aerosp Sci Technol 15:567–576CrossRefGoogle Scholar
  88. 88.
    Prats X, Puig V, Quevedo J, Nejjari F (2010) Lexicographic optimization for optimal departure aircraft trajectories. Aerosp Sci Technol 14:26–37CrossRefGoogle Scholar
  89. 89.
    Rosen KH (2013) Discrete mathematics, Mc Graw Hill, New YorkGoogle Scholar
  90. 90.
    Rozier KY (2011) Linear temporal logic symbolic model checking. Comput Sci Rev 5:163–203CrossRefGoogle Scholar
  91. 91.
    Rutkowski L (2008) Computational intelligence. Springer, BerlinGoogle Scholar
  92. 92.
    Samy I, Postlethwaite I, Gu DW (2011) Survey and application of sensor fault detection and isolation schemes. Control Eng Pract 19:658–674CrossRefGoogle Scholar
  93. 93.
    Savla K, Frazzoli E, Bullo F (2008) Traveling salesperson problems for the dubbins vehicle. IEEE Trans Autom Control 53(6):1378–1391CrossRefMathSciNetGoogle Scholar
  94. 94.
    Schouwenaars T, Mettler B, Feron E (2004) Hybrid model for trajectory planning of agile autonomous vehicles. AIAA J Aeronaut Comput Inf Commun 12:466–478Google Scholar
  95. 95.
    Schouwenaars T, Valenti M, Feron E, How J, Roche E (2006) Linear programming and language processing for human/unmanned-aerial-vehicle team missions. AIAA J Guid Control Dyn 29(2):303–313CrossRefGoogle Scholar
  96. 96.
    Schwefel HP, Wegener I, Weinert K (2002) Advances in computational intelligence. Springer, BerlinGoogle Scholar
  97. 97.
    Seibel CW, Farines JM, Cury JE (1999) Towards hybrid automata for the mission planning of unmanned aerial vehicles. In: Antsaklis V (ed) Hybrid Systems. Springer, Berlin, pp 324–340Google Scholar
  98. 98.
    Sun TY, Huo CL, Tsai S, Yu Y, Liu C (2011) Intelligent flight task algorithm for UAV. Expert Syst Appl 38:10036–10048CrossRefGoogle Scholar
  99. 99.
    Sundar K, Rathinam S (2012) Algorithms for routing an UAV in the presence of refueling depots. In: Proceedings of the American control conference (ACC), pp 3266–3271Google Scholar
  100. 100.
    Sutton RS, Barto AG (1998) Reinforcement learning. The MIT Press, CambridgeGoogle Scholar
  101. 101.
    Svorenova M, Tumova J, Barnat J, Cerna I (2012) Attraction based receding horizon path planning with temporal logic constraints. In: IEEE 51th control and decision conference, pp 6749–6754Google Scholar
  102. 102.
    Teinreiro Machado JA, Patkai B, Rudas I (2009) Intelligent engineering systems and computational cybernetics. Springer, PortoCrossRefGoogle Scholar
  103. 103.
    Toth P, Vigo D (2002) The vehicle routing problem. SIAM, PhiladelphiaCrossRefMATHGoogle Scholar
  104. 104.
    Treleaven K, Pavone M, Frazzoli E (2013) Asymptotically optimal algorithms for one-to-one pickup and delivery problems with applications to transportation systems. IEEE Trans Autom Control 44:888–894MathSciNetGoogle Scholar
  105. 105.
    Turnbull O, Richards A, Lawry J, Lowenberg M (2006) Fuzzy decision tree cloning of flight trajectory optimization for rapid path planning. IEEE conference on decision and control. San Diego, CA, pp 6361–6366CrossRefGoogle Scholar
  106. 106.
    Vinh NX (1993) Flight mechanics of high performance aircraft. Cambridge aerospace series, vol 4. Cambridge University Press, CambridgeGoogle Scholar
  107. 107.
    Volkan-Pehlivanoglu Y (2011) A new vibrational genetic algorithm enhanced with a Voronoi diagram for path planning of autonomous UAV. Aerosp Sci Technol. doi: 10.1016/j.ast2011.02.006
  108. 108.
    Vucina D, Loznia Z, Vlak F (2010) NPV based decision support in multi objective design using evolutionary algorithms. Eng Appl Artif Intell 23:48–60CrossRefGoogle Scholar
  109. 109.
    Wang HF, Wen YP (2002) Time-constrained Chinese postman problems. Int J Comput Math Appl 6:375–387Google Scholar
  110. 110.
    Wang Y, Wei T, Qu X (2012) Study of multi-objective fuzzy optimization for path planning. Chin J Aeronaut 25:51–56CrossRefGoogle Scholar
  111. 111.
    Wilkins DE, Smith SF, Kramer LA, Lee T, Rauenbusch T (2008) Airlift mission monitoring and dynamic rescheduling. Eng Appl Artif Intell 21:141–155CrossRefGoogle Scholar
  112. 112.
    Wongpiromsarn T, Topcu U, Murray RM (2012) Receding horizon temporal logic planning. IEEE Trans Autom Control 57:2817–2830CrossRefMathSciNetGoogle Scholar
  113. 113.
    Wu PY (2009) Multiobjective mission flight planning in civil unmanned aerial system. Phd Thesis, Queensland universityGoogle Scholar
  114. 114.
    Wu P, Campbell D, Merz T (2011) Multi-objective 4D vehicle motion planning in large dynamic environment. IEEE Trans Syst Man Cybern 41:621–634CrossRefGoogle Scholar
  115. 115.
    Xia M, Xu Z, Zhu B (2012) Generalized intuitionistic fuzzy Bonferroni means. Int J Intell Syst 27:23–47CrossRefGoogle Scholar
  116. 116.
    Xu L, Stentz T (2010) A fast traversal heuristic and optimal algorithms for effective environmental coverage. In: Matsuoka Y, Durrant-White H, Neira J (eds) Robotics, science and systems. The MIT Press, pp 121–128Google Scholar
  117. 117.
    Yakimenko OA (2000) Direct method for rapid prototyping of near optimal aircraft trajectory. AIAA J Guid Control Dyn 23:865–875Google Scholar
  118. 118.
    Yakimenko OA (2011) Engineering computations and modeling in Matlab/Simulink. AIAA Press, Reston VaGoogle Scholar
  119. 119.
    Yanushevsky R (2011) Guidance of unmanned aerial vehicles. CRC Press, Boca RatonCrossRefGoogle Scholar
  120. 120.
    Yordanov B, Tumova J, Cerna I, Barnat J, Belta C (2012) Temporal logic control of discrete-time piecewise affine systems. IEEE Trans Autom Control 57:1491–1504CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.UFR Sciences and TechnologiesUniversité d’Evry Val-D’EssoneEvryFrance

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