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Fuzzy adaptive-predictive decentralized control of discrete time interconnected systems

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Proposes an approach for the design of discrete-time decentralized control systems with m-step delay sharing information pattern, employing model-based predictive control (MBPC) combined with fuzzy prediction for the interconnections among the subsystems. A state space model is used at each control station to predict the corresponding subsystem output over a long-range time period. The interaction trajectories are considered to be non-linear functions of the states of the subsystems. For all cases the interconnections and the necessary predictions for them are estimated by an appropriate adaptive fuzzy identifier based on the generation of linguistic IF-THEN rules and the on-line construction of a common fuzzy rule base. Representative computer simulation results are provided and compared for nontrivial example systems.
Czasopismo
Rocznik
Strony
5--24
Opis fizyczny
Bibliogr. 25 poz.
Twórcy
autor
  • National Technical Univeristy of Athens, Department of Electrical and Computer Engineering, Division of Signals, Control and Robotics, Zagraphou 15773, Athens, Greece
  • National Technical Univeristy of Athens, Department of Electrical and Computer Engineering, Division of Signals, Control and Robotics, Zagraphou 15773, Athens, Greece
  • National Technical Univeristy of Athens, Department of Electrical and Computer Engineering, Division of Signals, Control and Robotics, Zagraphou 15773, Athens, Greece
Bibliografia
  • [1] AllgOwer F., Badgwell T. A., Qm I. S., Rawlings J. B., Wright S., Nonlinear Predictive Control and Moving Horizon Estimation, [in:] Admnces in Control, P. M. Frank (ed.), Springer, Ber- lin/London 1999.
  • [2] Bahnasawi a. Ą., Al-Fuhaid A. S., Mahmoud M. S., Decentralized and Hierarchical Control of Interconnected Systems, Proc. lEE, 1990, D-137, 311-321.
  • [3] Camacho E. F., Bordons C., Model Predictive Control in the Process Industry, Springer, London/Berlin 1994.
  • [4] Clarke D. w., Generalized Predictive Control and its Application, J. Richalet, S. Tzafestas (eds.) Computer Integrated Design of Controlled Industrial Systems, Proc. CIM-Europe Workshop, Brus- sels, April 26-27, 1990.
  • [5] De Keyser R. M. C., Model Based Predictive Control Toolbox, Richalet J., Tzafestas S. (eds.) Computer Integrated Design of Controlled Industrial Systems, Proc. CIM-Europe Workshop, Brus- sels, April 26-27,1990.
  • [6] Gossner J. R., Kouyaritakis B., Rossiter J. a., Cautious Stable Predictive Control: A Guaran- teed Stable Predictive Control Algorithm with Low Input Activity and Good Robustness, Proc. 3rd IEEE Mediterranean Symposium on New Directions in Control & Automation, Limassol, Cyprus, Vol. 2, 1995, 243-250.
  • [7] Ho Y. C., Chu K. C., Information Structure in Many—Person Optimization Problems, Automatica 10,1974,149-160.
  • [8] Hrissagis K., Crisallc O.D., Robust Predictive Regulator Design for Unstable Plants with Input Saturation, Proc. 3rd IEEE Mediterranean Symposium on New Directions in Control & Automation, Limassol, Cyprus, Vol. 2,195, 267-274.
  • [9] JAMSHIDI M., Large Scalę Systems: Modeling and Control, Elsevier/North-Holland, New York, 1983.
  • [10] Jamshidi M., Large Scalę Systems: Modeling, Control and FuzzyLogic, Prentice-Hall PTR, 1996.
  • [11] Kurtaram B., Siyan R.,.LQG Control with One-Step-Delay Sharing Pattern, IEEE Trans., AC-19, 1974, 571-574.
  • [12] Linnemann a., Decentralized Control of Dynamically Interconnected Systems, IEEE Trans. Autom. Cotrol, AC-29,1984, 1052-1054.
  • [13] Richalet J., Model Based Predictive Control in the Context of Integrated Design, [in:] Richalet J., Tzafestas S. (eds.), Computer Integrated Design of Controlled Industrial Systems, Proc. CIM-Europe Workshop, Brussels, April 26-27, 1990.
  • [14] Rossiter J.A., Grinell B. G., Extended Input Horizon Generalized Predictive Control - a GPC Algoritmh with Better Tracking, Proc. 3rd IEEE Mediterranean Symposium on New Directions in Control & Automation, Limassol, Cyprus, Vol. 2,1995,259-266.
  • [15] Rumelhart D. E., Hinton G. E., Williams R. J., Leaming Intemal Representations by Error Propagation, Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1, Cambridge, MA: MIT Press, 1989.
  • [16] Sandell N. R., Athans M., Solution of Some Non-classical LQG Stochastic Decision Problems, IEEE Trans. Autom. Control, AC-19, 1974,108-116.
  • [17] Sezer M. E., Śiuak D. D., Decentralized Control, Ch. 49, William S. Levine (ed.), The Control Handbook, CRC Press/IEEE Press, 1996.
  • [18] SiNGH M. G., Titu a. (eds), Handbook of Large Scalę Systems Engineering Applications, North Holland, Amsterdam 1977.
  • [19] Singh M. G., Titli A., Systems Decomposition, Optimization and Control, Pergamon; Oxford 1978.
  • [20] Singh M. G., Decentralised Control, North Holland, Amsterdam 1981.
  • [21] Tzafestas, S. G., Kyriannakis E., Kapsiotis G., Deccntralized Model Based Predictive Control of Large Scalę Systems, Proc. 3rd IEEE Mediterranean Symposium on New Directions in Control & Automation, Vol. 2, Limassol, Cyprus, 1995, 275-284.
  • [22] Tzafestas S. G., Watanabe. K., Stochastic Large-Scale Engineering Systems, Marcel Dekker, New York 1992.
  • [23] Wang L.-X., Adaptive Fuzzy Systems and Control, Prentice Hall, Englewood Cliffs, NJ, 1994.
  • [24] Xu X. M., Xl Y. G., Zhang Z. J., Decentralized Predictive Control (DPC) of Large Scalę Systems, North-Hołland, Information and Decision Technologies 14, 1988, 307-322.
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Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPW4-0002-0073
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