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Feedback and Control II: Modern Methodologies

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Energy, Information, Feedback, Adaptation, and Self-organization

Part of the book series: Intelligent Systems, Control and Automation: Science and Engineering ((ISCA,volume 90))

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Abstract

Modern control has decisively contributed to the human society development providing the means for successful control and efficient and safe operation of complex technological and non-technological systems such as computer-based systems, aircrafts, robots, automation systems, managerial systems, decision support systems, economic systems, etc. It is based on the concepts of “system state vector” and “state-space models” which are applicable to time-varying, multivariable, and nonlinear systems in both continuous-time and discrete-time representations. In this chapter, we present the fundamental concepts, principles, and methodologies covering most developments at an introductory level. Specifically, the following topics are considered: state-space modeling, Lyapunov stability, controllability and observability, optimal, stochastic, adaptive, predictive, robust, nonlinear, and intelligent control. Also, the following classes of dynamic models, that cover a wider range of natural and man-made systems, are briefly discussed: large-scale, distributed-parameter, time delay, finite state, and discrete event models. The field of modern control is still expanding offering new challenges in research and real-life bioengineering and technological applications.

Because we are self-controlling beings,

we are also responsible for our actions. This is

not a moral or ethical proposition, but

simply a causal one.

Butter Shaffer

People have to be responsible for their thoughts,

so they have to learn to control them. It may

not be easy, but it can be done.

Rolling Thunder

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References

  1. R.C. Dorf, R.H. Bishop, Modern Control Systems, 7th edn. (Addison-Wesley, Reading, MA, 1995)

    MATH  Google Scholar 

  2. J.J. DiStefano III, A.R. Stubberud, I.J. Williams, Theory and Problems of Feedback Control Systems Design (Mc Graw-Hill, New York, 1990)

    Google Scholar 

  3. L.A. Zadeh, C.A. Desoer, Linear Systems Theory: The State Space Approach (Mc Graw-Hill, New York, 1963)

    MATH  Google Scholar 

  4. R.E. Kalman, J.E. Bertram, Control system analysis and design via the “Second Method” of Lyapunov, (I) continuous-time systems. Trans. ASME J. Basic Eng., 371–393 (1960)

    Google Scholar 

  5. L.S. Pontryagin, V.G. Boltyansky, R.V. Gamkrelidze, E.F. Mishchenko, The Mathematical Theory of Optimal Processes (Wiley, New York, 1962)

    Google Scholar 

  6. J.P. Lasalle, Some extensions of Lyapunov’s second method. IRE Trans. Circuit Theor. 7, 520 (1960)

    Article  Google Scholar 

  7. R. Belmann, Dynamic Programming (Princeton University Press, New Jersey, 1957)

    Google Scholar 

  8. A.P. Sage, C.C. White III, Optimum Systems Control (Prentice-Hall, Englewood Cliffs, NJ, 1977)

    MATH  Google Scholar 

  9. M. Jamshidi, M. Malek-Zavarei, Linear Control Systems: A Computer-Aided Approach (Pergamon Press, Oxford, 1986)

    MATH  Google Scholar 

  10. M. Wonham, Linear Multivariable Control: A Geometric Approach, 3rd edn. (Springer, Berlin, 1985)

    Book  MATH  Google Scholar 

  11. W.A. Wolowitz, Linear Multivariable Systems (Springer, Berlin/New York, 1974)

    Google Scholar 

  12. M. Athans, P. Falb, Optimal Control (Mc Graw-Hill, New York, 1966)

    MATH  Google Scholar 

  13. P.L. Falb, W.A. Wolowitz, Decoupling in the design of multivariable feedback systems. IEEE Trans. Autom. Control 12, 651–659 (1967)

    Article  Google Scholar 

  14. D.G. Luenberger, An introduction to observers. IEEE Trans. Autom. Control 16, 233–239 (1971)

    Google Scholar 

  15. D.G. Luenberger, Canonical forms for multivariable feedback systems. IEEE Trans. Autom. Control 12, 290–293 (1967)

    Article  Google Scholar 

  16. D.G. Luenberger, Observers for multivariable systems. IEEE Trans. Autom. Control 11(2), 190–197 (1966)

    Article  MathSciNet  Google Scholar 

  17. H. Kwakernaak, R. Sivan, Linear Optimal Control Systems (Wiley, New York, 1972)

    MATH  Google Scholar 

  18. J.S. Meditch, Stochastic Optimal Linear Estimation and Control (McGraw-Hill, New York, 1969)

    MATH  Google Scholar 

  19. K.J. Aström, B. Wittenmark, Adaptive Control (Addison-Wesley, Reading, MA, 1989)

    Google Scholar 

  20. K.S. Narendra, A. Annaswamy, Stable Adaptive Systems (Prentice-Hall, New Jersey, 1989)

    MATH  Google Scholar 

  21. I.D. Landau, Adaptive Control: The Model Reference Approach (Marcel-Dekker, New York, 1979)

    MATH  Google Scholar 

  22. I.D. Landau, R. Lozano, M. M’Saad, Adaptive Control (Springer, New York, 1998)

    Book  MATH  Google Scholar 

  23. K.J. Aström, B. Wittenmark, Problems of identification and control. J. Math Anal. Appl. 34, 90–113 (1971)

    Article  MathSciNet  MATH  Google Scholar 

  24. J. Richalet, A. Rault, L. Testaud, J. Papon, Model predictive heuristic control: applications to industrial processes. Automatica 14, 413 (1978)

    Article  Google Scholar 

  25. G. Tao, Adaptive Control Design and Analysis (Wiley, Hoboken, NJ, 2003)

    Book  MATH  Google Scholar 

  26. C.R. Cutler, B.L. Ramaker, Dynamic Matrix Control: A Computer Control Algorithm, in Proceedings of the JACC (San Francisco, CA, WP-5, 1980)

    Google Scholar 

  27. D.W. Clarke, C. Mohtadi, P.S. Tuffs, Generalized predictive control, Part I: the basic algorithm. Automatica 23(2), 137–148 (1987); Part II: Extensions and interpretations, ibid, p. 149 (1987)

    Google Scholar 

  28. R.M.G. De Keyser, A.R. Van Cauwenberghe, Extended Prediction Self-Adaptive Control, in Proceedings of the 7th IFAC Symposium Identification Systems Parameter Estimation (York, U.K, 1985), pp. 1255–1260

    Google Scholar 

  29. R.M.C. De Keyser, P.H.G.A. Van de Velde, F.A.G. Dumortier, A comparative study of self-adaptive long range predictive control methods. Automatica 24(2), 149 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  30. J., Martin-Sànchez, A New Solution to Adaptive Control, in Proceedings of the IEEE, vol. 64, no. 8 (1976), pp. 1209–1218

    Google Scholar 

  31. D.W. Clarke (ed.), Advances in Model-Based Predictive Control (Oxford University Press, Oxford, 1994)

    MATH  Google Scholar 

  32. F. Allgöwer, A. Zheng, Nonlinear Model Predictive Control (Birkhauser Basel, Switzerland, 2000)

    Book  MATH  Google Scholar 

  33. G. Zames, On the input-output stability of time-varying non-linear feedback systems, Part I: conditions derived using concepts of loop gain, conicity, and positivity. IEEE Trans. Autom. Control 11(2), 228–238 (1966). Part II: conditions involving circles in the frequency plane and sector nonlinearities, ibid, no. 3, pp. 465–476 (1966)

    Google Scholar 

  34. G. Zames, Feedback and optimal sensitivity: model reference transformations. G. Zames, On the input-output stability of time-varying non-linear feedback systems, Part I: conditions derived using concepts of loop gain, conicity, and positivity. IEEE Trans. Autom. Control 11(2), 228–238 (1966). Part II: conditions involving circles in the frequency plane and sector nonlinearities, ibid, no. 3, pp. 465–476 (1966)

    Google Scholar 

  35. G. Zames, Feedback and optimal sensitivity: model reference transformations multiplicative semi-norms and approximate inverses. IEEE Trans. Autom. Control 26, 301–320 (1981)

    Google Scholar 

  36. G. Zames, N.A. Shneydor, Structural stabilization and quenching by dither in nonlinear systems. IEEE Trans. Autom. Control 22(3), 352–361 (1977)

    Google Scholar 

  37. M. Vidyasagar, Normalized coprime factorizations for non-strictly proper systems. IEEE Trans. Auto. Control 33, 300–301 (1988)

    Google Scholar 

  38. M. Vidyasagar, Control System Synthesis: A Factorization Approach (MIT Press, Cambridge, MA, 1985)

    MATH  Google Scholar 

  39. J.F. Doyle, A. Tannenbaum, Feedback Control Theory (Macmillan, New York, 1992)

    Google Scholar 

  40. T. Chen, B. Francis, Optimal Sampled-Data Control Systems (Springer, London/Berlin, 1995)

    Book  MATH  Google Scholar 

  41. J.C. Doyle, Analysis of Feedback Systems with Structured Uncertainties, in IEEE Proceedings, vol. 129, Part D (6) (1982), pp. 242–250

    Google Scholar 

  42. G.J. Balas, J.C. Doyle, R. Glover, A. Packard, R. Smith, The μ-Analysis and Synthesis Toolbox (Mathworks, Natick, MA, 1991)

    Google Scholar 

  43. I. Kurylowicz, J. Jaworska, S.G. Tzafestas, Robust Stabilizing Control: An Overview, Applied Digital Control (Marcel Dekker, New York, pp. 289–324, 1993). 41 J.C. Doyle, Analysis of Feedback Systems with Structured Uncertainties, IEEE Proc., Vol.129, Part D (6), pp.242–250, 1982

    Google Scholar 

  44. R.E. Kalman, A new approach to linear filtering and prediction problems. ASME J. Basic Eng. 82, 34–45 (1960)

    Google Scholar 

  45. D.E. Kirk, Optimal Control Theory (Prentice Hall, Englewood Cliffs, NJ, 1970)

    Google Scholar 

  46. B.D.O. Anderson, J.B. Moore, Optimal Control (Prentice Hall, Englewood Cliffs, NJ, 1990)

    MATH  Google Scholar 

  47. A.E. Bryson Jr., Y.-C. Ho, Applied Optimal Control (Hemisphere, New York, 1975)

    Google Scholar 

  48. T. Kailath, Linear Systems (Prentice Hall, Englewood Cliffs, 1980)

    MATH  Google Scholar 

  49. J.-J.E. Slotine, W. Li, Applied Nonlinear Control (Prentice-Hall, Englewood Cliffs, NJ, 1991)

    MATH  Google Scholar 

  50. A. Isidori, T.J. Tarn (eds.), Systems Models and Feedback (Birkhauser, Berlin, 1992), pp. 1–402

    Google Scholar 

  51. A. Isidori, Nonlinear Control Systems (Springer, Berlin, 1995)

    Book  MATH  Google Scholar 

  52. G.N. Saridis, Analytic formulation of the principle of increasing precision with decreasing intelligence for intelligent machines. Automatica 25(3), 461–467 (1989)

    Article  MATH  Google Scholar 

  53. K.S. Fu, Learning control systems and intelligent control systems: an intersection of artificial intelligence and automatic control. IEEE Trans Autom. Control 16(2), 70–72 (1971)

    Google Scholar 

  54. K.S. Fu, Syntactic Pattern Recognition and Applications (Prentice-Hall, Englewood Cliffs, 1982)

    MATH  Google Scholar 

  55. A. Meystel, Cognitive Controller for Autonomous Systems, in Proceedings of the IEEE Workshop on Intelligent Control (RPI, Troy, New York, 1985)

    Google Scholar 

  56. R.C. Arkin, Motor schema-based mobile robot navigation. Intl. Robot. Res. 8(4), 92–112 (1989)

    Article  MathSciNet  Google Scholar 

  57. R.C, Arkin, Behavior-Based Robotics (MIT Press, Cambridge, MA, 1998)

    Google Scholar 

  58. J. Albus, R. Quintero, Towards a Reference Model Architecture for Real-Time Intelligent Control Systems (ARTICS), in Robotics and Manufacturing (ASME), vol. 3 (ASME Press, New York, 1990)

    Book  Google Scholar 

  59. S.G. Tzafestas (ed.), Knowledge- Based Systems: Advanced Concepts, Techniques and Applications (World Scientific, Singapore/London, 1997)

    Google Scholar 

  60. S.G. Tzafestas, A. Venetsanopoulos (eds.), Fuzzy Reasoning in Information, Decision and Control Systems (Kluwer, Boston/Dordrecht, 1994)

    MATH  Google Scholar 

  61. S.G. Tzafestas (ed.), Soft Computing in Systems and Control Technology (World Scientific, Singapore/London, 1999)

    MATH  Google Scholar 

  62. S.G. Tzafestas (ed.), Computational Intelligence in Systems and Control Design and Applications (Kluwer, Boston/Dordrecht, 1999)

    Google Scholar 

  63. Y.-H. Song, A. Johns, R. Aggarwal, Computational Intelligence Applications to Power Systems (Kluwer, Boston/Dordrecht, 1996)

    Google Scholar 

  64. S. Haykin, Neural Networks: A Comprehensive Foundation (Prentice Hall, Upper Saddle River, NJ, 1999)

    MATH  Google Scholar 

  65. A.G. Barto, R.S. Sutton, W. Aderson, Neuronlike adaptive elements that can solve difficult learning control problems. IEEE Trans. Syst. Man Cybern 13(5), 834–847 (1983)

    Google Scholar 

  66. F.C. Chen, Back Propagation Neural Network for Nonlinear Self-Tuning Adaptive Control, in Proceedings of the IEEE Intelligent Machines, pp. 274–279 (1989)

    Google Scholar 

  67. K. Tzafestas, Watanabe, Learning Algorithms for Neural Networks with the Kalman Filters. J. Intell. and Robotic Syst. 3(4), 305–319 (1990)

    Article  Google Scholar 

  68. K. Watanabe, T. Fukuda, S.G. Tzafestas, Adaptive control for CARMA systems using linear neural networks. Int. J. Control 56, 483–497 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  69. S. Omatu, M. Khalil, R. Yusof, Neuro-Control and its Applications (Springer, Berlin, 1996)

    Book  Google Scholar 

  70. D. Nguyen, B. Widrow, Neural-networks for self-learning control systems. IEEE Control Syst. Mag. 10(3), 18–23 (1990)

    Article  Google Scholar 

  71. C.T. Lin, Neural Fuzzy Control Systems with Structure and Parameter Learning (World Scientific, Singapore/London, 1994)

    Book  Google Scholar 

  72. F.L. Lewis, J. Campos, R. Selmic, Neuro-Fuzzy Control of Industrial Systems with Actuator Nonlinearities (SIAM Publications, Philadelphia, PA, 2002)

    Book  MATH  Google Scholar 

  73. S.G. Tzafestas, G.G, Rigatos, Neural and neurofuzzy FELA adaptive robot control using feedforward and counterpropagration networks. J. Intell. Robot. Syst. 23(2–4), 291–330 (1998)

    Google Scholar 

  74. C.I. Harris, C.G. Moore, M. Brown, Intelligent Control: Aspects of Fuzzy Logic and Neural Networks (World Scientific, Singapore/London, 1993)

    Book  MATH  Google Scholar 

  75. J.-S.R. Jang, ANFIS: Adaptive-network-based fuzzy inference systems. IEEE Trans. Syst. Man Cybern. 23, 665–685 (1993)

    Article  Google Scholar 

  76. H.R. Berenji, P. Khedkar, Learning and tuning fuzzy logic controllers through reinforcements. IEEE Trans. Neural Netw. 3, 724–740 (1992)

    Article  Google Scholar 

  77. G.A. Carpenter, M.N. Gjaja, S. Gopal, C.E. Woodcock, ART neural networks for remote sensing: vegetation classification from landsat TM and terrain data. IEEE Trans. Geosci. Remote Sens. 35, 308–325 (1997)

    Article  Google Scholar 

  78. G.A. Carpenter, S. Grossberg, N. Markuson, J.H. Reynolds, D.B. Rosen, Fuzzy ARTMAP: a neural network architecture for incremental supervised learning of analog multidimensional maps. IEEE Trans. Neural Netw. 3, 698–712 (1992)

    Article  Google Scholar 

  79. S.G. Tzafestas, K.C. Zikidis, An Online Learning Neuro-fuzzy Architecture Based on Functional Reasoning and Fuzzy ARTMAP, in Proceedings of the ICSC International Symposium on Fuzzy Logic Applications (Zurich, Switzerland, 1997)

    Google Scholar 

  80. S.G. Tzafestas, K.C. Zikidis, NeuroFAST: on-line neuro-fuzzy ART-based structure and parameter learning TSK model. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 31(5), 797–802 (2001)

    Google Scholar 

  81. T.C. Lin, C.S. Lee, Neural network based fuzzy logic control and decision system. IEEE Trans. Comput. 40(12), 1320–1336 (1991)

    Google Scholar 

  82. R.E. Bellman, R.E. Kalaba, Quasilinearization and Nonlinear Boundary-Value Problems, in Modern Analytic and Computational Methods in Science and Mathematics, vol 3, ed. R.E. Bellman, R.E. Kalaba (American Elsevier, New York, 1965)

    Google Scholar 

  83. B. Pradin, A. Titli, Methods of Decomposition—Coordination for the Optimization of Interconnected DPS, in Proceedings of the 6th IFAC Triennial Congress, Paper 15-3 (Boston, 1975)

    Google Scholar 

  84. M.G. Singh, A. Titli, Systems: Decomposition, Optimization and Control (Pergamon, Oxford, 1978)

    MATH  Google Scholar 

  85. D.D. Siljak, M.K. Sundareshan, A Multilevel Optimization of Large Scale Dynamic Systems. IEEE Trans. Auto. Control 21(3), 363–366 (1976)

    Google Scholar 

  86. D.D. Siljak, Multilevel stabilization of large scale systems: a spinning flexible spacecraft. Automatica 12, 309–320 (1976)

    Article  MathSciNet  MATH  Google Scholar 

  87. M.G. Singh, M.F. Hassan, A. Titli, Multilevel Feedback Control of Interconnected Dynamical Principle Using the Prediction Principle, IEEE Trans. Syst. Man. Cybern., Vol.SMC-6, No.4, pp. 233–239, 1976

    Google Scholar 

  88. M.S. Mahmoud, M.F. Hassan, M.G. Darwish, Large Scale Control Systems Theories and Techniques (Marcel Dekker, New York, 1985)

    MATH  Google Scholar 

  89. S.G. Tzafestas, M.F. Hassan, Complex large scale systems methodologies in conjunction with modern computer technology. Control-Theor. Adv. Technol. 2(2), 105–120 (1986)

    Google Scholar 

  90. E.J. Davison, The robust decentralized control of a general servomechanism problem. IEEE Trans. Auto Control 21(1), 14–24 (1976)

    Google Scholar 

  91. D.D. Siljak, M.B. Vukcevic, Decentralization, stabilization and estimation of large scale linear systems. IEEE Trans. Auto. Control 21(3), 363–366 (1976)

    Google Scholar 

  92. M.G., Singh, Decentralized Control (North-Holland, Amsterdam, 1981)

    Google Scholar 

  93. C.W. Sanders, E.C. Tacker, T.D. Linton, Decentralized Filtering Algorithms for Interconnected Systems, in Proceedings of the IFAC Congress, Paper 26-2 (Boston, 1975)

    Google Scholar 

  94. C.W. Sanders, E.C. Tacker,T.D. Linton, A new class of decentralized filters for interconnected systems. IEEE Trans. Auto. Control 19(3), 259–262 (1974)

    Google Scholar 

  95. S.H. Wang, E.J. Davison, on the stabilization of decentralized control systems. IEEE Trans. Auto. Control 18(5), 473–478 (1973)

    Google Scholar 

  96. S.G. Tzafestas (ed.), Distributed Parameter Control Systems: Theory and Application (Pergamon, Oxford, 1982)

    MATH  Google Scholar 

  97. S.G. Tzafestas, Distributed Parameter Systems Estimation and Control: An Update, Systems and Control Encyclopedia, vol. 2, ed. M.G. Singh (Pergamon, Oxford, 1992), pp. 710–723

    Google Scholar 

  98. P.K.C. Wang, Asymptotic stability of distributed parameter systems with feedback controls. IEEE Trans. Auto. Control 11(1), 46–54 (1966)

    Google Scholar 

  99. A.G. Butkovskii, The maximum principle for optimum systems with distributed parameters. Autom. Remote Control 22, 1429–1438 (1962)

    MathSciNet  Google Scholar 

  100. Y. Sakawa, Optimal control of a certain type of linear distributed- parameter systems. IEEE Trans. Autom. Control 11(1), 42–45 (1966)

    Google Scholar 

  101. P.K.C. Wang, Mathematical Modeling of Systems with Distributed- Parameter Systems (ASME Publications, 1959), pp. 49–63

    Google Scholar 

  102. P.K.C. Wang, Modeling and control of nonlinear micro-distributed systems, nonlinear analysis. Theor. Methods Appl. 30(6), 3215–3226 (1997)

    Article  MATH  Google Scholar 

  103. Y. Sawaragi, T. Soeda, S. Omatu, Modeling Estimation and Their Applications for Distributed Parameter Systems (Springer, Berlin/New York, 1978). (Springer, LNCTS-11)

    MATH  Google Scholar 

  104. S.G. Tzafestas, J.M. Nightingule, Optimal filtering, smoothing and prediction in linear distributed—parameter systems. Proc. IEE 115(8), 1207–1212 (1968)

    MathSciNet  Google Scholar 

  105. S.G. Tzafestas, J.M. Nightingale, Optimal control of a class of linear stochastic distributed—parameter systems. Proc. IEE 115(8), 1213–1220 (1968)

    MathSciNet  Google Scholar 

  106. S.G. Tzafestas, J.M. Nightingale, Differential dynamic programming approach to optimal nonlinear distributed-parameter control systems. Proc. IEE 116(6), 1085–1093 (1969)

    MathSciNet  Google Scholar 

  107. S.G. Tzafestas, On optimum distributed—parameter filtering and fixed-interval smoothing for colored noise. IEEE Trans. Auto. Control 17(4), 448–458 (1972)

    Google Scholar 

  108. Y. Sawaragi, T. Soeda, S. Omatu, Modeling Estimation and Their Applications for Distributed Parameter Systems (Springer, Berlin/New York, 1978). (Springer, LNCTS-11)

    MATH  Google Scholar 

  109. Y. Sunahara, A. Ohsumi, M. Imamura, A method of parameter identification for linear distributed parameter systems. Automatica 12, 245–256 (1976)

    Article  MathSciNet  MATH  Google Scholar 

  110. G.K. Lausterer, W.H. Ray, H.R. Martenes, Real time distributed-parameter state estimation applied to a two-dimensional heated ingot. Automatica 14(4), 335–344 (1978)

    Article  MATH  Google Scholar 

  111. S.G. Tzafestas, Decoupling of nonlinear time-delay systems. Int. J. Syst. Sci. 5(4), 301–307 (1974)

    Article  MATH  Google Scholar 

  112. S.G. Tzafestas, T. Pimenides, Partial input-output decoupling in time-delay systems. Electron. Lett. 11(15), 353–354 (1975)

    Article  Google Scholar 

  113. S.G. Tzafestas, Model-matching in time-delay control systems. IEEE Trans. Autom. Control 21, 426–428 (1976)

    Google Scholar 

  114. J.-P. Richard, Time-delay systems: an overview of some recent advances and open problems. Automatica 39, 1667–1694 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  115. W. Michiels, S.-I. Niculescu, On delay sensitivity of Smith predictors. Int. J. Syst. Sci. 34(8–9), 543–551 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  116. Z. Wang, K.J. Burnham, Robust filtering for a class of stochastic uncertain nonlinear time-delay systems via exponential state estimation. IEEE Trans. Signal Process. 49(4) (2001)

    Google Scholar 

  117. V. Suplin, E. Fridman, U. Shaked, H∞ control of linear uncertain time-delay systems: a projection approach. IEEE Trans. Auto. Control 51(4), 680–684 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  118. P.A. Prokopiou, S.G. Tzafestas, W.S. Harwin, A novel scheme for human-friendly and time-delays robust neuropredictive teleoperation. J. Intell. Robot. Syst. 25(4), 311–340 (1999)

    Article  Google Scholar 

  119. Q.-C. Zhong, Robust Control of Time-Delay Systems (Springer, London, 2006)

    MATH  Google Scholar 

  120. O.J.M. Smith, A controller to overcome dead time. ISA J. 6(2), 28–33 (1959)

    Google Scholar 

  121. Z.J. Palmor, Time-delay compensation-Smith predictor and its modifications, in Control Handbook, ed. W.S. Levine (CRC and IEEE Press, New York, 1996), pp. 224–237

    Google Scholar 

  122. A.V. Kim, W.H. Kwon, V.G. Pimenov, Time Delay System Toolbox (for Use with MATLAB), Users Guide 2000. http://home.imm.wran.ru/fde/toolbox.html, http://fde.imm.uran.ru (for on line simulation)

  123. F. Haugen, Tutorial for Control System Toolbox for MATLAB, Oct 11, 2003. http://techteach.no/publications/control_system_toolbox/

  124. A. Gill, Introduction to the Theory of Finite-state Machines (McGraw-Hill, New York, 1962)

    MATH  Google Scholar 

  125. S. Ginsburg, An Introduction to Mathematical Machine Theory (Addison-Wesley, Reading, MA, 1962)

    MATH  Google Scholar 

  126. T.A. Booth, Sequential Machines and Automata Theory (Wiley, New York, 1967)

    MATH  Google Scholar 

  127. M.A. Arbib, Theories of Abstract Automata (Prentice Hall, Englewood Cliffs, NJ, 1969)

    MATH  Google Scholar 

  128. T. Kam, Synthesis of Finite State Machines: Functional Optimization (Kluwer, Boston, MA, 1997)

    Book  MATH  Google Scholar 

  129. P. Linz, Formal Languages and Automata (Jones and Barlett, Sudbury, MA, 2006)

    MATH  Google Scholar 

  130. Y. Pang, M.P. Spathopoulos, On Weighted Time-Optimal Control for Linear Hybrid Automata Using Quantifier Elimination. citeseerx.ist.psu.edu/viewdoc/summary.doi=10.11.1.3.4570

    Google Scholar 

  131. Y. Pang, M.P. Spathopoulos, J. Raisch, On Suboptimal Control Design for Hybrid Automata Using Predictive Control Techniques, in Proceedings of the16th IFAC World Congress (Prague, 2005)

    Google Scholar 

  132. S.G. Tzafestas, Input- output modeling and identification of linear automata. Inform-Process. Lett. 1(3), 273, 295 (1972)

    Google Scholar 

  133. S.G. Tzafestas, Concerning controllability and observability of linear sequential machines. Int. J. Syst. Sci. 4(6), 833–858 (1973)

    Article  Google Scholar 

  134. A. Nerode, W. Kohn, Models for Hybrid Systems: Automata, Topologies, Controllability, Observability, in Hybrid Systems, LNCS-736 (Springer, Heidelberg, 1993)

    Google Scholar 

  135. G.F. Beckhoff, Controllability-Observability Type Duality Relations of Finite-State Machines, LNCS-1 (Springer, Heidelberg, 1973)

    MATH  Google Scholar 

  136. S.G. Tzafestas, Multivariable control theory in linear sequential machines. Int. J. Syst. Sci. 4(3), 363–396 (1973)

    Article  MathSciNet  MATH  Google Scholar 

  137. S.G. Tzafestas, State estimation algorithms for nonlinear stochastic sequential machines. Comput. J. 6(3), 295–253 (1973)

    Google Scholar 

  138. E. Athanasopoulou, C.N. Hadjicostis, Maximum Likelihood Diagnosis in Partially Observable Finite State Machines, in Proceedings of the MED-2005, IEEE International Symposium on Intelligent Control and Automation (Limassol, 2005), pp. 896–901

    Google Scholar 

  139. Y. Pang, M.P. Spathopoulos, J. Raisch, On Suboptimal Control Design for Hybrid Automata Using Predictive Control Techniques, in Proceedings of the 16th IFAC World Congress (Prague, 2005). http://www.nt.ntnu.no/users/skoge/prost/procedings/ifac2005/Fullpapers/03461.pdf

  140. A. Ray, J. Fu, C. Lagoa, Optimal supervisory control of finite state automata. Int. J. Control 77(12), 1083–1100 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  141. E. Tronci, Optimal Finite State Supervisory Control, in Proceedings 35th IEEE Conference on Decision and Control (Kobe, Japan, 1996)

    Google Scholar 

  142. YCh. Ho, Introduction to dynamics of discrete event systems. Proc. IEEE 77(1), 3–6 (1989)

    Article  MathSciNet  Google Scholar 

  143. S.L. Chung, S. Lafortune, F. Lin, Limited lookahead policies in supervisory control of discrete event systems. IEEE Trans. Auto. Control 37(12), 1921–1935 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  144. F. Capkovic, Knowledge-Based Control Synthesis of Discrete Event Dynamic Systems, in Advances in Manufacturing: Decision, Control and Information Technology, ed. S.G. Tzafestas (Springer, London, 1999), pp. 195–180

    Google Scholar 

  145. C.G. Cassandras, S. Lafortune, Introduction to Discrete Event Systems (Kluwer, Norwell, MA, 1999)

    Book  MATH  Google Scholar 

  146. M. Zhou, K. Vencatech, Modeling Simulation and Control of Flexible Manufacturing Systems: A Petri Net Approach (World Scientific, Singapore, 1998)

    Google Scholar 

  147. W.M. Wonham, P.J. Ramadge, On the supremal controllable sublanguage of a given language. SIAM J. Control Optimiz. 25, 637–659 (1987)

    Article  MathSciNet  Google Scholar 

  148. P.J. Ramadge, W.M. Wonham, Supervisory control of a class of discrete event processes. SIAM J. Control Optimiz. 25(1), 206–230 (1987)

    Article  MathSciNet  MATH  Google Scholar 

  149. W.M. Wonham, P.J. Ramadge, Modular supervisory control of discrete event systems. Mathem. Control Signals Syst. 1(1), 13–30 (1988)

    Google Scholar 

  150. F. Lin, W.M. Wonham, Decentralized supervisory control of discrete—event systems. Inform. Sci. 44, 199–224 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  151. J. Duggan, J. Browne, ESPNET: Expert-Systems-Based Simulator of Petri Nets, Proc. IEEE Part D 135(4), 239–247 (1988)

    Google Scholar 

  152. R. Kumar, V.K. Carg, Modeling and Control of Logical Discrete Event Systems (Kluwer, Norwell, MA, 1995)

    Book  Google Scholar 

  153. R. Kumar, V. Gong, S.I. Markus, Predicates and predicate transformers for supervisory control of discrete event systems. IEEE Trans. Autom. Control 38(2), 232–247 (1993)

    Google Scholar 

  154. J.C. Willems, The Birth of Optimal Control, in Proceedings of the 35th CDC (Kobe, Japan. 1996), pp. 1586–1587

    Google Scholar 

  155. H.H. Goldstine, A History of Calculus of Variations from the 17th through the 19th Century (Springer, Berlin, 1980)

    Book  MATH  Google Scholar 

  156. L.A. Zadeh, Fuzzy sets. Inf. Control 8, 338 (1965)

    Article  MATH  Google Scholar 

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    Spyros G. Tzafestas

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Tzafestas, S.G. (2018). Feedback and Control II: Modern Methodologies. In: Energy, Information, Feedback, Adaptation, and Self-organization. Intelligent Systems, Control and Automation: Science and Engineering, vol 90. Springer, Cham. https://doi.org/10.1007/978-3-319-66999-1_7

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