
FIRST ‘Small’ Cluster Proposal: “Modular Learning and Co-ordination in Complex Systems”
References used in Case for Support:
[1] Anderson, B; Brinsmead, T; Liberzon, D and S Morse, “Multiple model adaptive control
with safe switching”. International Journal of Adaptive Control and Signal Processing,
Vol. 15(5), pp. 445–470, 2001.
[2] Aufderheide, B and B Bequette,
“A variable tuned multiple model predictive controller
based on minimal process knowledge”. Proc.
American Control Conference,
[3] Bemporad A.;
[4] Chen, G. and J. McAvoy, “Predictive on-line monitoring of continuous
processes”, J. of Process Control,
Vol. 8, pp. 409-420, 1997.
[5] Chen
J. and R. Patton. Robust
model based fault diagnosis for dynamic systems, Kluwer
Academic Publisher, 1999.
[6] Chen,
L and K Narendra, “Intelligent control using
multiple neural networks”. International Journal of Adaptive Control and Signal Processing,
Vol. 17(6), pp. 417–430, 2003.
[7] Chiang L., Russel,
E. and Braatz R. Fault Detection and Diagnosis in
Industrial Systems, Springer-Verlag London Limited,
2001.
[8] Chiu, M and
[9] Close C. and D. Frederick, Modelling and analysis of dynamic systems,
John Wiley & Sons. Inc. 1995.
[10] Das, S, Fox, J, Elsdon,
D and P Hammond. “A flexible architecture for
autonomous agents”, Journal of
Experimental and Theoretical Artificial Intelligence, Vol. 9, pp. 407-440,
1997.
[11] Elsaesser, C and M
Slack. “Integrating deliberative planning in a robot architecture”,
in Proceedings of AIAA/NASA
Conference on Intelligent Robots in Field, Factory, Service and Space, Houston,
pp. 782-787, 1994.
[12] Frank, P.
“Fault diagnosis in dynamic systems using analytical and knowledge-based
redundancy: A survey and new results”, Automatica, Vol. 26, pp. 459-474, 1990.
[13] Gat, E. “On three layer
architectures”, in D Kortenkamp, R Bonasso and R Murphey (Eds.) Artificial Intelligence and Mobile Robots.
AAAI Press.
[14] Gertler, J. Fault
Detection and Diagnosis in Engineering Systems, Marcel Dekker
Inc.,
[15] Giovanini, L and M Grimble, “Predictive Feedback
Control using a Multiple Model Approach”, Workshop on Fault Detection and Diagnosis,
[16] Gomez-Ramirez,
P. Lofti Najim and E. Ikonen, Stochastic
learning control for non-linear systems, Intern. Joint Conf. on Neural Networks,
[17] Hexmoor, H and D Kortenkamp,
“Issues on building software for hardware agents”, Knowledge Engineering Review, Vol.
10(3), pp. 301-304, 1995.
[18] Huang, Y. and
L. Fan, “HIDEN: A Hybrid Intelligent System for Synthesizing Highly Controllable
Exchanger Networks: Implementation of Distributed Strategy for Integration of
Process Design and Control”, Industrial
& Engineering Chemistry Research, Vol. 33, 1171-1185, 1994.
[19] Isermann R. “Process fault detection based on modeling and estimation methods - A survey”, Automatica, pp
387-404, 1984.
[20] Isermann R and P. Balle,
“Trends in the application of model-based fault detection and diagnosis
of technical processes”, Control
Engineering Practice, Vol. 5, pp 709-719, 1997.
[21] Jones, H. L, Failure detection in linear systems, PhD
thesis, Dept. of Aeronautics, MIT , Cambridge, Mass, 1973
[22] Kanev, S and M Verhaegen,
“Controller reconfiguration for non-linear systems”, Control Engineering Practice, Vol.
8(11), pp. 1223–1235, 2000.
[23] Kordon, A;
Dhurjati, P; Fuentes Y and B Ogunnaike, “An intelligent parallel control
system structure for plants with multiple operating regimes”, Journal of Process Control, Vol. 9, pp.
453–460, 1999.
[24] Luo, K. and Y. Huang, “Intelligent Decision Support
on Process Modification and Operational Enhancement for Source Reduction in
Electroplating Plants”, Int. J. of
Eng. Appl. of Artificial Intelligence, Vol.
10(4), pp. 321-333, 1997.
[25] Maes, P. “The agent network architecture
(ANA)”, SIGART Bulletin, Vol.
2(4), pp. 115-120, 1991.
[26] Morse, A.
Control using logic-based switching, In A. Isidori
(Ed.), Trends in control: a Erupean perspective (pp.
69-113). Springer Verlag, 1995.
[27] Najim, K and A. Poznyak, Learning
Automata Theory and Applications, Pergamon Press,
[28] Najim, K; E. Ikonen
and Aït-Kadi Daoud. Stochastic Processes: Estimation, Optimisation
and analysis, Hermes Science Publisher Ltd.,
[29] Najim, K.; A. Poznyak
and E. Ikonen, Optimization technique based on a team
of learning automata with binary outputs, Automatica,
Vol. 40.9, 2004.
[30] Narendra, K and J Balakrishnan,
“Improving transient response of adaptive control systems using multiple
models and switching”. IEEE Trans. on Autom. Control, Vol. 39(9), pp. 1861–1866, 1994.
[31] Narendra, K; Balakrishnan J and M
Ciliz, “Adaptation and leraning
using multiple models, switching and tuning”. IEEE Trans. on Autom. Control, Vol. 39(9), pp. 1861–1866, 1995.
[32] Narendra, K and J Balakrishnan,
“Adaptive control using multiple models”. IEEE Trans. on Autom. Control, Vol. 42(2), pp. 171–187, 1997.
[33] Norman, D and T Shallice. “Attention to action: Willed and automatic control of
behaviour”, in R Davison, G Schwartz and D Shapiro (Eds.) Consciousness and self-regulation, Vol.
4, pp. 1-18.
[34] Neimann, H and J Stoustrup, “An architecture for fault tolerant
controllers”. Submitted to Int. J of Control, 2003.
[35] Patton R.
“Robust model-based fault diagnosis: the state of art”. IFAC Fault Detection, Supervision and Safety for Technical
Processes, pp. 11-24,
[36] Shallice, T and P Burgess. “The domain of supervisory process and temporal organization
of behaviour”, Phylosophical Transaction of the Royal Society of London,
Vol. 351, pp. 1405-1412, 1996.
[37] Sofanov, M and T Tsao, “The
unfulsified control concept and learning”. IEEE Trans. on Autom. Control, Vol. 42(2), 1997.
[38] Van Overschee, P. and
B. De Moor. Closed-loop
subspace system identification, Internal report. Katholieke Universitiet
[39] Van Overschee, P. and
B. De Moor. “Closed-loop subspace
system identification”, Proceedings
36th IEEE Control and Decisin Conference,
[40] Veres, S. “Self-tuning control by model unfulsification (Part I)”. Int. J of Control, Vol. 73, pp.
1548–1559, 2000.
[41] Veres, S. “Self-tuning control by model unfulsification (Part II)”. Int. J of Control, Vol. 73, pp.
1560–1571, 2000.
[42] Willsky, A. “A survey of design methods for failure
detection systems”, Automatica, Vol. 12, pp. 601-611, 1976.
[43] Wenxin, et al, Fault diagnosis and tolerant control of
control systems, Mechanical
[44] Wolkenhauer, O. Possibility Theory with Applications to Data
Analysis, John Wiley & Sons
[45] Yu W. and A. Pozilyak, “Indirect adaptive control via parallel
dynamic neural networks”, IEE.
Proc. Control theory Appl., Vol. 146 (4), pp.
25-30, 1999.
[46] Zayed, A; A.
Hussain and M. Grimble, “Novel non-linear PID based Multiple-Controller
incorporating a Learning sub-model”, to appear, International Journal of Control & Intelligent Systems, Special
Issue: Non-linear Adaptive PID Control, vol.33, 2004.