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Part of the book series: Microprocessor-Based and Intelligent Systems Engineering ((ISCA,volume 16))

Abstract

Intelligent Control has now achieved a good shape, although the definition and structure of an intelligent control system is still a debatable subject. Actually, intelligent control is an enhancement of traditional control to include the ability to sense and reason about the environment with incomplete and inexact a priori knowledge, and execute commands and controls in an adaptive, flexible and robust way. The field of intelligent control was founded by Fu in 1971 [1] as an intersection of artificial intelligence and automatic control. The demand for higher autonomy and higher productivity has motivated throughout the years extensive research in the field of intelligent control covering a diversity of topics such as learning control, knowledge-based (expert) control, fuzzy control, neural control, neurofuzzy control, sensing technology, sensory data processing and fusion, task planning, automated fault diagnosis and restoration, and obstacle-risk avoidance.

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References

  1. K.S. Fu, Learning Control Systems and Intelligent Control Systems: An Intersection of Artificial Intelligence and Automatic Control, IEEE Trans. on Automatic Control, Vol. 16, pp. 70–72 (1971).

    Article  Google Scholar 

  2. G.N. Saridis and K.P. Valavanis, Analytical Design of Intelligent Machines, Automatica, Vol. 24, pp. 123–133 (1988).

    Article  MATH  Google Scholar 

  3. K.P. Valavanis and G.N. Saridis, Intelligent Robotic Systems: Theory, Design and Applications, Kluwer, Boston/Dordrecht (1992).

    Book  MATH  Google Scholar 

  4. A. Meystel, Intelligent Control of a Multiactuator System, In: D.E. Hardt(ed.), Information Control Problems in Manufacturing Technology (Proc. IFAC Symp.), Pergamon, Oxford (1983).

    Google Scholar 

  5. A. Meystel, Knowledge-Based Nested Hierarchical Control, In: G.N. Saridis (ed.) Knowledge Based Systems for Intelligent Automation, Vol. 2, JAI Press, Greenwich, CT (1990).

    Google Scholar 

  6. A. Meystel, Multiresolutional Architectures for Autonomous Systems with Incomplete and Inadequate Knowledge Representation, In: S.G. Tzafestas and H.B. Verbruggen (eds.), Artificial Intelligence in Industrial Decision Making, Control and Automation, Kluwer, Boston/Dordrecht (1995).

    Google Scholar 

  7. A. Meystel, Autonomous Mobile Robots: Vehicles with Cognitive Control, World Scientific, Singapore (1991).

    Google Scholar 

  8. A. Meystel, Semiotic Modeling and Situation Analysis: An Introduction, AdRem, Inc., Bala Cynwyd, U.S.A. (1995).

    Google Scholar 

  9. G.N. Saridis, Self-Organizing Control of Stochastic Systems, Marcel Dekker, New York (1977).

    MATH  Google Scholar 

  10. B. Hayes-Roth, The Blackboard Architecture: A General Framework for Problem Solving, Project Report No. HPP-83-30, Stanford University, Computer Sci. Dept., May (1983).

    Google Scholar 

  11. B. Hayes-Roth, A Blackboard Architecture for Control, J. Artificial Intell., Vol. 26, pp. 251–321 (1985).

    Article  Google Scholar 

  12. S.G. Tzafestas and E.S. Tzafestas, The Blackboard Architecture in Knowledge-Based Robotic Systems, In: T. Jordanides and B. Torby (eds.),Expert Systems and Robotics, Springer, Berlin/London, pp. 285–317 (1991).

    Chapter  Google Scholar 

  13. G.K.H. Pang, A Framework for Intelligent Control, J.Intell. & Robotic Systems, Vol. 4, No. 2, pp. 109–127 (1991).

    Article  Google Scholar 

  14. V. Marik and L. Lhotska, Some Aspects of the Blackboard Control in the Diagnostics FEL-Expert Shell, In: I. Plander (ed.), Artificial Intelligence and Information Control of Robots, Elsevier/North-Holland, Amsterdam (1989).

    Google Scholar 

  15. H.P. Nii, Blackboard Systems, Stanford Univ. Report No. KSL 86-18 & AI Magaz., Vols 7-2 & 7-3 (1986).

    Google Scholar 

  16. E. Aveďyan, Learning Systems, Springer, Berlin/London (1995).

    Google Scholar 

  17. S.G. Tzafestas and H.B. Verbruggen (eds.), Artificial Intelligence in Industrial Decision Making, Control and Automation, Kluwer, Boston/Dordrecht (1995).

    MATH  Google Scholar 

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

    MATH  Google Scholar 

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

    MATH  Google Scholar 

  20. G.C. Goodwin and K.S. Sin, Adaptive Filtering, Prediction and Control, Prentice Hall, Englewood Cliffs, N.J. (1984)

    MATH  Google Scholar 

  21. K.J. ÅstrÖm, J.J. Anton and K.E. Arzen, Expert Control, Automatica, Vol. 22, No. 3, pp. 277–286 (1986)

    Article  MATH  Google Scholar 

  22. G.K.H. Pang and A.G.J MacFarlane, An Expert Systems Approach to Computer-Aided Design of Multivariable Systems, Springer, Berlin/London (1987).

    Book  MATH  Google Scholar 

  23. S.G. Tzafestas and A. Ligeza, A Framework for Knowledge-Based Control, J. Intell. & Robotic Systems, Vol. 2, pp. 407–425 (1989).

    Article  Google Scholar 

  24. S.G. Tzafestas, Knowledge Based System Diagnosis, Supervision and Control, Plenum, London (1989).

    Google Scholar 

  25. K.E. Årzen, An Architecture for Expert System Based Feedback Control, Automatica, Vol. 25, No. 6 (1989)

    Google Scholar 

  26. J.M. Boyle et al., The Development and Implementation of MAID: A Knowledge Based Support System for Use in Control System Design, Trans. Inst. Meas. and Control, Vol. 11, No.1, pp. 25–39 (1989)

    Article  Google Scholar 

  27. K.G. Shin and X. Cui, Design of Knowledge-Based Controller for Intelligent Control Systems, IEEE Trans. Syst. Man and Cybernetics, Vol. 21, No. 2, pp. 368–375 (1991).

    Article  Google Scholar 

  28. B.P. Butz and N.F. Palumbo, Expert Systems for Control Systems Design, In: S.G. Tzafestas (ed.), Applied Control, Marcel-Dekker, NY pp. 825–850 (1993).

    Google Scholar 

  29. C. Tebbutt, Expert-Aided Control System Design, Springer, Berlin (1994).

    Book  Google Scholar 

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

    Google Scholar 

  31. M. Jamshidi, N. Vadiee and T. Ross, Fuzzy Logic and Control: Software and Hardware Applications, Prentice Hall, London/Toronto (1993).

    Google Scholar 

  32. R. Palm, D. Driankov and H. Hellendoorn, Model-Based Fuzzy Control, Springer, Berlin/London (1997).

    MATH  Google Scholar 

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

    MATH  Google Scholar 

  34. C.J. Harris (ed.), Advances in Intelligent Control, Taylor&Francis, London (1994).

    MATH  Google Scholar 

  35. S. Omatu, M. Khalid and R. Yusof, Neuro-Control and Its Applications, Springer, Berlin/London (1996).

    Book  Google Scholar 

  36. J.A.K. Suykens, J.P.L. Vandewalle and B.L.R. De Moor, Artificial Neural Networks for Modelling and Control of Nonlinear Systems, Kluwer, Boston/Dordrecht (1996).

    Google Scholar 

  37. P.J. Antsaklis and K.M. Passino (eds.), An Introduction to Intelligent and Autonomous Control, Kluwer, Boston/Dordrecht (1993).

    MATH  Google Scholar 

  38. L.R. Medsker, Hybrid Neural Networks and Expert systems, Kluwer, Boston/Dordrecht (1994).

    Book  Google Scholar 

  39. S.G. Tzafestas, Fuzzy Systems and Fuzzy Expert Control: An Overview, The Knowledge Engineering Review, Vol. 9, No. 3, pp. 229–268 (1994).

    Article  Google Scholar 

  40. C.C. Lee, A Self-Learning Rule-Based Controller Employing Approximate Reasoning and Neural Net Concepts, Int.J. Intell. Systems, Vol. 6, pp. 71–93 (1991).

    Article  Google Scholar 

  41. J. Keller, R. Yager and H. Tahani, Neural Network Implementation of Fuzzy Logic, Fuzzy Sets and Systems, Vol. 45, pp. 1–12 (1992).

    Article  MathSciNet  MATH  Google Scholar 

  42. J. Keller, R. Krishnapuram and F. Rhee, Evidence Aggregation Networks for Fuzzy Logic, IEEE Trans. Neural Networks, Vol. 3, No. 5, pp. 761–769 (1992).

    Article  Google Scholar 

  43. S.G. Tzafestas and G.B. Stamou, An Improved Neural Network for Fuzzy Reasoning Implementation, Math. Computers Simul., Vol. 41, Nos.3-4, pp. 201–208 (1996).

    Article  MathSciNet  Google Scholar 

  44. S.G. Tzafestas, S. Raptis and G. Stamou, A Flexible Neurofuzzy Cell Structure for General Fuzzy Inference, Math. Computers Simul., Vol. 41, Nos.3-4, pp. 219–233 (1996).

    Article  Google Scholar 

  45. C.T. Lin and C.S.G. Lee, Neural-Network-Based Fuzzy Logic Control and Decision System, IEEE Trans. Computers, Vol. 40, No. 12, pp. 1320–1336, (1991).

    Article  MathSciNet  Google Scholar 

  46. C.T. Lin and C.S.G. Lee, Real-Time Supervised Structure/Parameter Learning for Fuzzy Neural Network, Proc. IEEE Int. Conf. on Fuzzy Systems, San Diego, Calif, pp. 1283–1290, March (1992).

    Google Scholar 

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

    Google Scholar 

  48. C.T. Lin and Y.-C. Lu, A Neural Fuzzy System with Linguistic Teaching Signals, IEEE Trans. on Fuzzy Systems, Vol. 3, No. 2, pp. 169–189 (1995).

    Article  Google Scholar 

  49. S.-I. Horikawa, T. Furuhashi and Y. Uchikawa, Fuzzy Modeling Using Neurofuzzy Networks with Back Propagation Learning, IEEE Trans. on Neural Networks, Vol. 3, No. 5, pp. 801–806 (1992).

    Article  Google Scholar 

  50. H. Ishibuchi, R. Fujioka and H. Tanaka, Neural Networks that Learn from Fuzzy If-Then Rules, IEEE Trans. on Fuzzy Systems, Vol. 1, No. 2, pp. 85–97 (1993).

    Article  Google Scholar 

  51. K. Uehara and M. Fujise, Learning of Fuzzy-Inference Criteria with Artificial Neural Networks, Proc. IIZUKA′90, Vol. 1, pp. 193–198 (1990).

    Google Scholar 

  52. K. Watanabe, S.-H. Jin and S. G. Tzafestas, Learning Multiple Fuzzy Control of Robot Manipulators, J. Artificial Neural Networks, Vol. 2, Nos. 1&2, pp. 119–136 (1995).

    Google Scholar 

  53. K. Watanabe, K. Hara, S. Koga and S. G. Tzafestas, Fuzzy-Neural Network Controllers Using Mean-Value-Based Functional Reasoning, Neurocomputing, Vol. 9, pp. 39–61 (1995).

    Article  MATH  Google Scholar 

  54. K. Watanabe, K, Hara and S. Tzafestas, Fuzzy controller design using the mean-value-based functional reasoning, Proc. Intl. Joint Conf. on Neural Networks (IJCNN′93), Nagoya, Japan, October (1993).

    Google Scholar 

  55. C. C. Lee and H. R. Berenji, An Intelligent Controller Based on Approximate Reasoning and Reinforcement Learning, Proc. IEEE Intl. Symp. on Intelligent Control (ISIC′89), Albany, N.Y., pp. 200–205 (1989).

    Google Scholar 

  56. H. R. Berenji and P. Khedkar, Learning and Tuning Fuzzy Logic Controllers Through Reinforcements, IEEE Trans. on Neural Networks, Vol. 3, No. 5, pp. 724–740 (1992).

    Article  Google Scholar 

  57. H. R. Berenji, Fuzzy and Neural Control, In: P. I. Antsaklis and K. M. Passino (eds.), An Introduction to Intelligent and Autonomous Control, Kluwer, Boston / Dordrecht, pp. 215–236 (1993).

    Google Scholar 

  58. J. L. Peterson, Petri Net Theory and the Modeling of Systems, Prentice-Hall, Englewood Cliffs, N. J. (1981).

    Google Scholar 

  59. S. G. Tzafestas and C. Athanassiou, A New Class of Petri Nets for Fast Robot Cell Prototyping, CC-AI: Communication & Cognition — Artificial Intelligence, Vol. 12, No. 3, pp. 225–252 (1995).

    Google Scholar 

  60. S. G. Tzafestas and F. Capkovic, Petri-Net Based Approach to the Synthesis of Intelligent Control Systems for DEDS, In: S. G. Tzafestas (ed.) Computer-Assisted Management and Control of Manufacturing Systems, Springer, Berlin / London, pp. 325–351 (1997).

    Google Scholar 

  61. A. W. Colombo and R. Carelli, Petri Nets for Designing Manufacturing Systems, In: S. G. Tzafestas (ed.) Computer-Assisted Management and Control of Manufacturing Systems, Springer, Berlin / London, pp. 297–324 (1997).

    Google Scholar 

  62. C. G. Looney, Fuzzy Petri Nets for Rule-Based Decision Making, IEEE Trans. Syst. Man. Cybern., Vol. 18, No. 1, pp. 178–183 (1988).

    Article  Google Scholar 

  63. F. Capkovic, A Knowledge-Based Approach to Synthesis of Intelligent Control for DEDS, In: A. Aamodt and A. J. Komorowski (eds.) Proc. 5th Scandinavian Conf. on Artificial Intelligence (SCAI′95), IOS Press Ohmsha, Amsterdam / Oxford pp. 9–18 (1995).

    Google Scholar 

  64. S. G. Tzafestas, Petri-Net and Knowledge-Based Methodologies in Manufacturing Systems, Proc. ESPRIT CIM Europe Conf., Athens, May (1989); Also: Systems & Control Encyclop.: Suppl1, Pergamon, Oxford (1990).

    Google Scholar 

  65. B. P. Zeigler, DEVS Representation of Dynamical Systems: Event Based Intelligent Control, Proc. IEEE, Vol. 77, No. 1, pp. 72–80 (1989).

    Article  Google Scholar 

  66. T. M. Sobh and R. Bajcsy, A Discrete Event Framework for Autonomous Observation Under Uncertainty, J. Intell. & Robotic Syst., Vol. 16, No. 4, pp. 315–385 (1996).

    Article  Google Scholar 

  67. B. P. Zeigler, Object Oriented Simulation with Hierarchical, Modular Models: Intelligent Agents and Endomorphic Systems, Academic Press, New York (1990).

    MATH  Google Scholar 

  68. B. P. Zeigler and S.-D. Chi, Symbolic Discrete Event System Specification, IEEE Trans. Syst., Man, Cyber., Vol. 22, No. 6, pp. 28–43 (1992).

    Google Scholar 

  69. S.-D. Chi and B. P. Zeigler, Hierarchical Model-Based Diagnosis for High Autonomy Systems, J. Intell. & Robotic Syst., Vol. 9, pp. 193–207 (1994).

    Google Scholar 

  70. A. K. A. Toguyeni, E. Craye and J.-C. Gentina, Knowledge-Based Supervision of Flexible Manufacturing Systems, In: S. G. Tzafestas and H. B. Verbruggen (eds.) Artificial Intelligence in Industrial Decision Making, Control and Automation, Kluwer, Boston / Dordrecht, pp. 631–685 (1995).

    Chapter  Google Scholar 

  71. S. G. Tzafestas (ed.), Engineering Systems with Intelligence, Kluwer, Boston-Dordrecht (1991).

    Google Scholar 

  72. E. A. Yfantis, Intelligent Systems, Kluwer, Boston-Dordrecht (1995).

    Google Scholar 

  73. R. G. Andreson and K. Warwick, Intelligent Industrial Automation and Soft Computing, ICSC Academic Press, Millet, Alberta, Canada (1996).

    Google Scholar 

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

    Google Scholar 

  75. R. M. Glorioso and F. C. Colon Osorio, Engineering Intelligent Systems, Digital Press, New York (1980).

    Google Scholar 

  76. E. R. Dougherty and C. R. Giardina, Mathematical Methods for Artificial Intelligence and Autonomous Systems, Prentice Hall (1988).

    Google Scholar 

  77. L. R. Medsker, Hybrid Intelligent Systems, Kluwer, Boston-Dordrecht (1995).

    Book  MATH  Google Scholar 

  78. S. G. Tzafestas (ed.), Intelligent Robotic Systems, Marcel Dekker, New York (1991).

    MATH  Google Scholar 

  79. S.G. Tzafestas (ed.), Applied Control: Current Trends and Modern Methodologies, Marcel Dekker, New York (1993).

    MATH  Google Scholar 

  80. S.G. Tzafestas (ed.), Expert Systems in Engineering Applications, Springer, Berlin/London (1993).

    MATH  Google Scholar 

  81. P. Antsaklis, Defining Intelligent Control, IEEE Control Syst. Magaz., Vol. 14, No. 3, pp. 4–5 & 58-66 (1994).

    Google Scholar 

  82. K. J. Åstrom and T. J. McAvoy, Intelligent Control, J. Process Control, Vol. 2, No. 3, pp. 115–127 (1992).

    Article  Google Scholar 

  83. A. Meystel, Intelligent Control in Robotics, J. Robotic Systems, Vol. 5, No. 4, pp. 269–308 (1988).

    Google Scholar 

  84. H. Tian and Q. D. Wu, Intelligent Control Systems, Proc. WAC′96: World Automation Congress, Montpellier, France, May (1996).

    Google Scholar 

  85. J.S. Albus, Outlines for a Theory of Intelligence, IEEE Trans. Syst. Man Cybern., Vol. 21, No. 3, pp. 473–501 (1991).

    Article  MathSciNet  Google Scholar 

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Tzafestas, S.G. (1997). Introduction: Overview of Intelligent Controls. In: Tzafestas, S.G. (eds) Methods and Applications of Intelligent Control. Microprocessor-Based and Intelligent Systems Engineering, vol 16. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-5498-7_1

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