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Dr. Alexander Semionovich Poznyak Gorbatch: Biography

  • Alexander S. PoznyakEmail author
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Abstract

Alexander S. Poznyak (Alexander Semion Pozniak Gorbatch) was born on December 6, 1946 in Moscow and graduated from Moscow Physical Technical Institute (MPhTI) in 1970. He earned Ph.D. and Doctor Degrees from the Institute of Control Sciences of Russian Academy of Sciences in 1978 and 1989, respectively. From 1973 up to 1993, he served in this institute as researcher and leading researcher, and in 1993 he accepted a post of full professor (3-F) at CINVESTAV of IPN in Mexico.

Books, Articles and Conferences

  1. 1.
    Aguilar, R., Martinez-Guerra, R., Poznyak, A.S.: Nonlinear PID controller for the regulation of fixed bed bioreactors. In: Proceedings of the 41st IEEE Conference on Decision and Control, vol. 4, pp. 4126–4131 (2002). https://doi.org/10.1109/CDC.2002.1185014
  2. 2.
    Alazki, H., Ordaz, P., Poznyak, A.S.: Robust bounded control for the flexible arm robot. In: Proceedings of the 52nd IEEE Conference on Decision and Control, pp. 3061–3066 (2013). https://doi.org/10.1109/CDC.2013.6760349
  3. 3.
    Alazki, H., Poznyak, A.S.: Output linear feedback tracking for discrete-time stochastic model using robust attractive ellipsoid method with LMI application. In: Proceedings of the 2009 6th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE), pp. 1–6 (2009). https://doi.org/10.1109/ICEEE.2009.5393429
  4. 4.
    Alazki, H., Poznyak, A.S.: Constraint robust stochastic discrete-time tracking: attractive ellipsoids technique. In: Proceedings of the 7th International Conference on Electrical Engineering Computing Science and Automatic Control, pp. 99–104 (2010). https://doi.org/10.1109/ICEEE.2010.5608567
  5. 5.
    Alazki, H., Poznyak, A.S.: Probabilistic analysis of robust attractive ellipsoids for quasi-linear discrete-time models. In: Proceedings of the 49th IEEE Conference on Decision and Control (CDC), pp. 579–584 (2010). https://doi.org/10.1109/CDC.2010.5717662
  6. 6.
    Alazki, H., Poznyak, A.S.: Averaged attractive ellipsoid technique applied to inventory projectional control with uncertain stochastic demands. In: Proceedings of the 50th IEEE Conference on Decision and Control and European Control Conference, pp. 2082–2087 (2011). https://doi.org/10.1109/CDC.2011.6160847
  7. 7.
    Alazki, H., Poznyak, A.S.: Robust stochastic tracking for discrete-time models: designing of ellipsoid where random trajectories converge with probability one. Int. J. Syst. Sci. 43(8), 1519–1533 (2012). https://doi.org/10.1080/00207721.2010.547664
  8. 8.
    Alazki, H., Poznyak, A.S.: A class of robust bounded controllers tracking a nonlinear discrete-time stochastic system: attractive ellipsoid technique application. J. Frankl. Inst. Eng. Appl. Math. 350(5), 1008–1029 (2013). https://doi.org/10.1016/j.jfranklin.2013.02.001
  9. 9.
    Alazki, H., Poznyak, A.S.: Robust output stabilization for a class of nonlinear uncertain stochastic systems under multiplicative and additive noises: the attractive ellipsoid method. J. Ind. Manag. Optim. 12(1), 169–186 (2016). https://doi.org/10.3934/jimo.2016.12.169
  10. 10.
    Alazki, H.S., Poznyak Gorbatch, A.S.: Inventory constraint control with uncertain stochastic demands: attractive ellipsoid technique application. IMA J. Math. Control Inf. 29(3), 399–425 (2012). https://doi.org/10.1093/imamci/dnr038
  11. 11.
    Alvarez, I., Poznyak, A.S.: Game theory applied to urban traffic control problem. Proc. ICCAS 2010, 2164–2169 (2010). https://doi.org/10.1109/ICCAS.2010.5670234
  12. 12.
    Alvarez, I., Poznyak, A.S., Malo, A.: Urban traffic control problem via a game theory application. In: Proceedings of the 46th IEEE Conference on Decision and Control, pp. 2957–2961 (2007). https://doi.org/10.1109/CDC.2007.4434820
  13. 13.
    Alvarez, I., Poznyak, A.S., Malo, A.: Urban traffic control problem a game theory approach. In: Proceedings of the 47th IEEE Conference on Decision and Control, pp. 2168–2172 (2008). https://doi.org/10.1109/CDC.2008.4739461
  14. 14.
    Azhmyakov, V., Poznyak, A.S.: A variational characterization of the sliding mode control processes. In: Proceedings of the American Control Conference (ACC), pp. 5383–5388 (2012). https://doi.org/10.1109/ACC.2012.6315542
  15. 15.
    Azhmyakov, V., Boltyanski, V., Poznyak, A.S.: The dynamic programming approach to multi-model robust optimization. Nonlinear Anal. Theory, Methods Appl. Int. Multidiscip. J. 72(2), 1110–1119 (2010). https://doi.org/10.1016/j.na.2009.07.050
  16. 16.
    Azhmyakov, V., Boltyanski, V.G., Poznyak, A.S.: First order optimization techniques for impulsive hybrid dynamical systems. In: Proceedings of International Workshop on Variable Structure Systems, pp. 173–178 (2008). https://doi.org/10.1109/VSS.2008.4570703
  17. 17.
    Azhmyakov, V., Boltyanski, V.G., Poznyak, A.S.: On the dynamic programming approach to multi-model robust optimal control problems. In: Proceedings of the American Control Conference, pp. 4468–4473 (2008). https://doi.org/10.1109/ACC.2008.4587199
  18. 18.
    Azhmyakov, V., Boltyanski, V.G., Poznyak, A.S.: Optimal control of impulsive hybrid systems. Nonlinear Anal. Hybrid Syst. 2(4), 1089–1097 (2008). https://doi.org/10.1016/j.nahs.2008.09.003
  19. 19.
    Azhmyakov, V., Cabrera Martinez, J., Poznyak, A.S.: Optimal fixed-levels control for nonlinear systems with quadratic cost-functionals. Optim. Control Appl. Methods 37(5), 1035–1055 (2016). https://doi.org/10.1002/oca.2223
  20. 20.
    Azhmyakov, V., Egerstedt, M., Fridman, L., Poznyak, A.S.: Approximability of nonlinear affine control systems. Nonlinear Anal. Hybrid Syst. 5(2), 275–288 (2011). https://doi.org/10.1016/j.nahs.2010.07.005
  21. 21.
    Azhmyakov, V., Galvan-Guerra, R., Poznyak, A.S.: On the hybrid LQ-based control design for linear networked systems. J. Frankl. Inst. Eng. Appl. Math. 347(7), 1214–1226 (2010). https://doi.org/10.1016/j.jfranklin.2010.05.012
  22. 22.
    Azhmyakov, V., Martinez, J.C., Poznyak, A.S., Serrezuela, R.R.: Optimization of a class of nonlinear switched systems with fixed-levels control inputs. In: Proceedings of the American control Conference (ACC), pp. 1770–1775 (2015). https://doi.org/10.1109/ACC.2015.7170989
  23. 23.
    Azhmyakov, V., Polyakov, A., Poznyak, A.S.: Consistent approximations and variational description of some classes of sliding mode control processes. J. Frankl. Inst. Eng. Appl. Math. 351(4), 1964–1981 (2014). https://doi.org/10.1016/j.jfranklin.2013.01.011
  24. 24.
    Azhmyakov, V., Poznyak, A.S., Gonzalez, O.: On the robust control design for a class of nonlinearly affine control systems: the attractive ellipsoid approach. J. Ind. Manag. Optim. 9(3), 579–593 (2013). https://doi.org/10.3934/jimo.2013.9.579
  25. 25.
    Azhmyakov, V., Poznyak, A.S., Juárez, R.: On the practical stability of control processes governed by implicit differential equations: the invariant ellipsoid based approach. J. Frankl. Inst. Eng. Appl. Math. 350(8), 2229–2243 (2013). https://doi.org/10.1016/j.jfranklin.2013.04.016
  26. 26.
    Baev, S., Shkolnikov, I., Shtessel, Y., Poznyak, A.S.: Parameter identification of non-linear system using traditional and high order sliding modes. In: Proceedings of the American Control Conference, p. 6 (2006). https://doi.org/10.1109/ACC.2006.1656620
  27. 27.
    Baev, S., Shkolnikov, I.A., Shtessel, Y.B., Poznyak, A.S.: Sliding mode parameter identification of systems with measurement noise. Int. J. Syst. Sci. 38(11), 871–878 (2007). https://doi.org/10.1080/00207720701622809
  28. 28.
    Bejarano, F.J., Fridman, L., Poznyak, A.S.: Output integral sliding mode with application to the LQ - optimal control. In: Proceedings of the International Workshop on Variable Structure Systems VSS’06, pp. 68–73 (2006). https://doi.org/10.1109/VSS.2006.1644495
  29. 29.
    Bejarano, F.J., Fridman, L., Poznyak, A.S.: Estimation of unknown inputs, with application to fault detection, via partial hierarchical observation. In: Proceedings of the European Control Conference (ECC), pp. 5154–5161 (2007)Google Scholar
  30. 30.
    Bejarano, F.J., Fridman, L., Poznyak, A.S.: Exact state estimation for linear systems with unknown inputs based on hierarchical super-twisting algorithm. Int. J. Robust Nonlinear Control 17(18), 1734–1753 (2007). https://doi.org/10.1002/rnc.1190
  31. 31.
    Bejarano, F.J., Fridman, L., Poznyak, A.S.: Hierarchical observer for strongly detectable systems via second order sliding mode. In: Proceedings of the 46th IEEE Conference on Decision and Control, pp. 3709–3714 (2007). https://doi.org/10.1109/CDC.2007.4434968
  32. 32.
    Bejarano, F.J., Fridman, L.M., Poznyak, A.S.: Output integral sliding mode control based on algebraic hierarchical observer. Int. J. Control 80(3), 443–453 (2007). https://doi.org/10.1080/00207170601080205
  33. 33.
    Bejarano, F.J., Fridman, L.M., Poznyak, A.S.: Output integral sliding mode for min-max optimization of multi-plant linear uncertain systems. IEEE Trans. Autom. Control 54(11), 2611–2620 (2009). https://doi.org/10.1109/TAC.2009.2031718
  34. 34.
    Bejarano, F.J., Fridman, L.M., Poznyak, A.S.: Unknown input and state estimation for unobservable systems. SIAM J. Control Optim. 48(2), 1155–1178 (2009). https://doi.org/10.1137/070700322
  35. 35.
    Bejarano, F.J., Poznyak, A.S., Fridman, L.: Hierarchical second-order sliding-mode observer for linear time invariant systems with unknown inputs. Int. J. Syst. Sci. Princ. Appl. Syst. Integr. 38(10), 793–802 (2007). https://doi.org/10.1080/00207720701409280
  36. 36.
    Bejarano, F.J., Poznyak, A.S., Fridman, L.: Min-max output integral sliding mode control for multiplant linear uncertain systems. In: Proceedings of the American Control Conference, pp. 5875–5880 (2007). https://doi.org/10.1109/ACC.2007.4282716
  37. 37.
    Bejarano, F.J., Poznyak, A.S., Fridman, L.M.: Observation of linear systems with unknown inputs via high-order sliding-modes. Int. J. Syst. Sci. 38(10), 773–791 (2007). https://doi.org/10.1080/00207720701409538
  38. 38.
    Boltyanski, V.G., Poznyak, A.S.: Robust maximum principle for minimax mayer problem with uncertainty from a compact measured set. In: Proceedings of the American Control Conference (IEEE Cat. No.CH37301), vol. 1, pp. 310–315 (2002). https://doi.org/10.1109/ACC.2002.1024822
  39. 39.
    Boltyanski, V.G., Poznyak, A.S.: A compact uncertainty set. The Robust Maximum Principle (2012). https://doi.org/10.1007/978-0-8176-8152-4_17
  40. 40.
    Boltyanski, V.G., Poznyak, A.S.: Dynamic programming for robust optimization. The Robust Maximum Principle (2012). https://doi.org/10.1007/978-0-8176-8152-4_12
  41. 41.
    Boltyanski, V.G., Poznyak, A.S.: Extremal problems in banach spaces. The Robust Maximum Principle (2012). https://doi.org/10.1007/978-0-8176-8152-4_7
  42. 42.
    Boltyanski, V.G., Poznyak, A.S.: Finite collection of dynamic systems. The Robust Maximum Principle (2012). https://doi.org/10.1007/978-0-8176-8152-4_8
  43. 43.
    Boltyanski, V.G., Poznyak, A.S.: Introduction. The Robust Maximum Principle (2012). https://doi.org/10.1007/978-0-8176-8152-4_1
  44. 44.
    Boltyanski, V.G., Poznyak, A.S.: Linear multimodel time optimization. The Robust Maximum Principle (2012). https://doi.org/10.1007/978-0-8176-8152-4_10
  45. 45.
    Boltyanski, V.G., Poznyak, A.S.: Linear quadratic optimal control. The Robust Maximum Principle (2012). https://doi.org/10.1007/978-0-8176-8152-4_4
  46. 46.
    Boltyanski, V.G., Poznyak, A.S.: LQ-stochastic multimodel control. The Robust Maximum Principle (2012). https://doi.org/10.1007/978-0-8176-8152-4_16
  47. 47.
    Boltyanski, V.G., Poznyak, A.S.: The maximum principle. The Robust Maximum Principle (2012). https://doi.org/10.1007/978-0-8176-8152-4_2
  48. 48.
    Boltyanski, V.G., Poznyak, A.S.: A measurable space as uncertainty set. The Robust Maximum Principle (2012). https://doi.org/10.1007/978-0-8176-8152-4_11
  49. 49.
    Boltyanski, V.G., Poznyak, A.S.: Min-max sliding-mode control. The Robust Maximum Principle (2012). https://doi.org/10.1007/978-0-8176-8152-4_13
  50. 50.
    Boltyanski, V.G., Poznyak, A.S.: Multimodel Bolza and LQ problem. The Robust Maximum Principle (2012). https://doi.org/10.1007/978-0-8176-8152-4_9
  51. 51.
    Boltyanski, V.G., Poznyak, A.S.: Multimodel differential games. The Robust Maximum Principle (2012). https://doi.org/10.1007/978-0-8176-8152-4_14
  52. 52.
    Boltyanski, V.G., Poznyak, A.S.: Multiplant robust control. The Robust Maximum Principle (2012). https://doi.org/10.1007/978-0-8176-8152-4_15
  53. 53.
    Boltyanski, V.G., Poznyak, A.S.: The Robust Maximum Principle. Foundations and Applications. Birkhauser, New York, Systems and Control (2012)CrossRefzbMATHGoogle Scholar
  54. 54.
    Boltyanski, V.G., Poznyak, A.S.: The tent method in finite-dimensional spaces. The Robust Maximum Principle (2012). https://doi.org/10.1007/978-0-8176-8152-4_6
  55. 55.
    Boltyanski, V.G., Poznyak, A.S.: Time-optimization problem. The Robust Maximum Principle (2012). https://doi.org/10.1007/978-0-8176-8152-4_5
  56. 56.
    Bregeault, V., Brgeault, V., Plestan, F., Shtessel, Y., Poznyak, A.S.: Adaptive sliding mode control for an electropneumatic actuator. In: Proceedings of the 11th International Workshop on Variable Structure Systems (VSS), pp. 260–265 (2010). https://doi.org/10.1109/VSS.2010.5544714
  57. 57.
    Cabrera, A., Poznyak, A.S., Poznyak, T., Aranda, J.: Some experiments on identification of a fed-batch culture via differential neural networks. In: Proceedings of the IEEE International Conference on Control Applications (CCA ’01), pp. 152–156 (2001). https://doi.org/10.1109/CCA.2001.973855
  58. 58.
    Carrillo, L., Escobar, J.A., Clempner, J.B., Poznyak, A.S.: Optimization problems in chemical reactions using continuous-time Markov chains. J. Math. Chem. 54(6), 1233 (2016). https://doi.org/10.1007/s10910-016-0620-0
  59. 59.
    Castillo, R.G., Clempner, J.B., Poznyak, A.S.: Solving the multi-traffic signal-control problem for a class of continuous-time Markov games. In: Proceedings of the 12th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE) 2015, pp. 1–5 (2015). https://doi.org/10.1109/ICEEE.2015.7357932
  60. 60.
    Chairez, I., Fuentes, R., Poznyak, A.S., Poznyak, T.: Robust identification of uncertain Schrödinger type complex partial differential equations. In: Proceedings of the 7th International Conference on Electrical Engineering, Computing Science and Automatic Control, pp. 170–175 (2010). https://doi.org/10.1109/ICEEE.2010.5608635
  61. 61.
    Chairez, I., Fuentes, R., Poznyak, A.S., Poznyak, T.: Robust identification of uncertain Schrödinger type complex partial differential equations. In: Proceedings of the 7th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2010 (Formerly known as ICEEE) IEEE, Tuxtla Gutierrez, Mexico, 8–10 Sept 2010, pp. 170–175 (2010). https://doi.org/10.1109/ICEEE.2010.5608635
  62. 62.
    Chairez, I., Fuentes, R., Poznyak, A.S., Poznyak, T., Escudero, M., Viana, L.: Neural network identification of uncertain 2D partial differential equations. In: Proceedings of the 6th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE) 2009, pp. 1–6 (2009). https://doi.org/10.1109/ICEEE.2009.5393456
  63. 63.
    Chairez, I., Fuentes, R., Poznyak, A.S., Poznyak, T., Escudero, M., Viana, L.: DNN-state identification of 2D distributed parameter systems. Int. J. Syst. Sci. 43(2), 296–307 (2012). https://doi.org/10.1080/00207721.2010.495187
  64. 64.
    Chairez, I., Garca, A., Poznyak, A.S., Poznyak, T.: Model predictive control by differential neural networks approach. In: Proceedings of the International Joint Conference on Neural Networks (IJCNN), pp. 1–8 (2010). https://doi.org/10.1109/IJCNN.2010.5596521
  65. 65.
    Chairez, I., Poznyak, A.S., Poznyak, T.: Dynamic neural observer with sliding mode learning. In: Proceedings of the 3rd International IEEE Conference on Intelligent Systems, pp. 600–605 (2006). https://doi.org/10.1109/IS.2006.348487
  66. 66.
    Chairez, I., Poznyak, A.S., Poznyak, T.: New sliding-mode learning law for dynamic neural network observer. IEEE Trans. Circuits Syst. II: Express Briefs 53(12), 1338–1342 (2006). https://doi.org/10.1109/TCSII.2006.883096
  67. 67.
    Chairez, I., Poznyak, A.S., Poznyak, T.: High order dynamic neuro observer: application for ozone generator. In: Proceedings of the International Workshop on Variable Structure Systems, pp. 291–295 (2008). https://doi.org/10.1109/VSS.2008.4570723
  68. 68.
    Chairez, I., Poznyak, A.S., Poznyak, T.: High order sliding mode neurocontrol for uncertain nonlinear SISO systems: theory and applications. Modern Sliding Mode Control Theory (2008). https://doi.org/10.1007/978-3-540-79016-7_9
  69. 69.
    Chairez, I., Poznyak, A.S., Poznyak, T.: Stable weights dynamics for a class of differential neural network observer. IET Control Theory Appl. 3(10), 1437–1447 (2009). https://doi.org/10.1049/iet-cta.2008.0261
  70. 70.
    Clempner, J.B., Poznyak, A.S.: Convergence method, properties and computational complexity for Lyapunov games. Appl. Math. Comput. Sci. 21(2), 349–361 (2011). https://doi.org/10.2478/v10006-011-0026-x
  71. 71.
    Clempner, J.B., Poznyak, A.S.: Analysis of best-reply strategies in repeated finite Markov chains games. In: Proceedings of the 52nd IEEE Conference on Decision and Control, pp. 568–573 (2013). https://doi.org/10.1109/CDC.2013.6759942
  72. 72.
    Clempner, J.B., Poznyak, A.S.: Simple computing of the customer lifetime value: a fixed local-optimal policy approach. J. Syst. Sci. Syst. Eng. 23(4), 439 (2014). https://doi.org/10.1007/s11518-014-5260-y
  73. 73.
    Clempner, J.B., Poznyak, A.S.: Computing the strong Nash equilibrium for Markov chains games. Appl. Math. Comput. 265, 911–927 (2015). https://doi.org/10.1016/j.amc.2015.06.005
  74. 74.
    Clempner, J.B., Poznyak, A.S.: Modeling the multi-traffic signal-control synchronization: a Markov chains game theory approach. Eng. Appl. Artif. Intell. 43, 147–156 (2015). https://doi.org/10.1016/j.engappai.2015.04.009
  75. 75.
    Clempner, J.B., Poznyak, A.S.: Stackelberg security games: computing the shortest-path equilibrium. Expert Syst. Appl. 42(8), 3967–3979 (2015). https://doi.org/10.1016/j.eswa.2014.12.034
  76. 76.
    Clempner, J.B., Poznyak, A.S.: Analyzing an optimistic attitude for the leader firm in duopoly models: a strong Stackelberg equilibrium based on a Lyapunov game theory approach. Econ. Comput. Econ. Cybern. Stud. Res. 4(50), 41–60 (2016)Google Scholar
  77. 77.
    Clempner, J.B., Poznyak, A.S.: Conforming coalitions in Markov Stackelberg security games: setting max cooperative defenders vs. non-cooperative attackers. Appl. Soft Comput. 47, 1–11 (2016). https://doi.org/10.1016/j.asoc.2016.05.037
  78. 78.
    Clempner, J.B., Poznyak, A.S.: Constructing the Pareto front for multi-objective Markov chains handling a strong Pareto policy approach. Comput. Appl. Math. 1 (2016). https://doi.org/10.1007/s40314-016-0360-6
  79. 79.
    Clempner, J.B., Poznyak, A.S.: Convergence analysis for pure stationary strategies in repeated potential games: Nash, Lyapunov and correlated equilibria. Expert Syst. Appl. 46, 474–484 (2016). https://doi.org/10.1016/j.eswa.2015.11.006
  80. 80.
    Clempner, J.B., Poznyak, A.S.: Solving the Pareto front for multiobjective Markov chains using the minimum Euclidean distance gradient-based optimization method. Math. Comput. Simul. 119, 142–160 (2016). https://doi.org/10.1016/j.matcom.2015.08.004
  81. 81.
    Clempner, J.B., Poznyak, A.S.: Multiobjective Markov chains optimization problem with strong Pareto frontier: principles of decision making. Expert Syst. Appl. 68, 123–135 (2017). https://doi.org/10.1016/j.eswa.2016.10.027
  82. 82.
    Clempner, J.B., Poznyak, A.S.: Using Manhattan distance for computing the multiobjective Markov chains problem. Int. J. Comput. Math. (2017) (To be published)Google Scholar
  83. 83.
    Clempner, J.B., Poznyak, A.S.: Using the extraproximal method for computing the shortest-path mixed Lyapunov equilibrium in Stackelberg security games. Math. Comput. Simul. 138, 14–30, (2017). https://doi.org/10.1016/j.matcom.2016.12.010. (To be published)
  84. 84.
    Clempner, J.B., Poznyak, A.S.: A Tikhonov regularized penalty function approach for solving polylinear programming problems. J. Comput. Appl. Math. 328, 267–286 (2018)Google Scholar
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    Clempner, J. B., Poznyak, A.: Negotiating The Transfer Pricing Using The Nash Bargaining Solution. Int J Appl Math Comput Sci. (To be published)Google Scholar
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    Clempner, J.B., Poznyak, A.: A Tikhonov Regularization Parameter Approach For Solving Lagrange Constrained Optimization Problems. Eng Optimiz. (To be published)Google Scholar
  87. 87.
    Davila, J., Poznyak, A.S.: Sliding modes parameter adjustment in the presence of fast actuators using invariant ellipsoids method. In: Proceedings of the 6th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE) 2009, pp. 1–6 (2009). https://doi.org/10.1109/ICEEE.2009.5393474
  88. 88.
    Davila, J., Poznyak, A.S.: Attracting ellipsoid method application to designing of sliding mode controllers. In: Proceedings of the 11th International Workshop on Variable Structure Systems (VSS), pp. 83–88 (2010). https://doi.org/10.1109/VSS.2010.5544627
  89. 89.
    Davila, J., Poznyak, A.S.: Design of sliding mode controllers with actuators using attracting ellipsoid method. In: Proceedings of the 49th IEEE Conference on Decision and Control (CDC), pp. 72–77 (2010). https://doi.org/10.1109/CDC.2010.5717774
  90. 90.
    Davila, J., Poznyak, A.S.: Dynamic sliding mode control design using attracting ellipsoid method. Automatica 47(7), 1467–1472 (2011). https://doi.org/10.1016/j.automatica.2011.02.023
  91. 91.
    Davila, J., Poznyak, A.S.: Sliding mode parameter adjustment for perturbed linear systems with actuators via invariant ellipsoid method. Int. J. Robust Nonlinear Control 21(5), 473–487 (2011). https://doi.org/10.1002/rnc.1599
  92. 92.
    Davila, J., Fridman, L., Poznyak, A.S.: Observation and identification of mechanical systems via second order sliding modes. Int. J. Control 79(10), 1251–1262 (2006). https://doi.org/10.1080/00207170600801635
  93. 93.
    Escobar, J., Poznyak, A.S.: Continuous-time identification using LS-method under colored noise perturbations. In: Proceedings of the 46th IEEE Conference on Decision and Control, pp. 5516–5521 (2007). https://doi.org/10.1109/CDC.2007.4434168
  94. 94.
    Escobar, J., Poznyak, A.S.: Robust continuous-time matrix estimation under dependent noise perturbations: sliding modes filtering and LSM with forgetting. CSSP 28(2), 257–282 (2009). https://doi.org/10.1007/s00034-008-9080-5
  95. 95.
    Escobar, J., Poznyak, A.S.: Time-varying parameter estimation in continuous-time under colored perturbations using “equivalent control concept” and LSM with forgetting factor. In: Proceedings of the 11th International Workshop on Variable Structure Systems (VSS), pp. 209–214 (2010). https://doi.org/10.1109/VSS.2010.5544662
  96. 96.
    Escobar, J., Poznyak, A.S.: Time-varying matrix estimation in stochastic continuous-time models under coloured noise using LSM with forgetting factor. Int. J. Syst. Sci. 42(12), 2009–2020 (2011). https://doi.org/10.1080/00207721003706852
  97. 97.
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© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of Control AutomáticoCINVESTAV-IPNCiudad de MéxicoMéxico

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