‘Large’ Cluster Proposal: “Novel Computational Framework for Control
of Complex Engineering Systems”
References used in
Case for Support:
[1] Alber S & G Montague, “Hybrid Neural Network - First
Principles Models Applied to a Food Processes”, IF AC Symp, ADCHEM'97,
1997.
[2] Amann N, Owens D &
Rogers E, “Predictive Optimal Iterative Learning Control”, Int. J. Control, 69(2),
pp 203-226, 1998.
[3]
[4] Aström K & Furuta K,
“Swinging
up a pendulum by energy control”.,
IFAC World Congress,
[5] Aström
K, Albertos
P, Blanke
M, Isidori
A, Schaufelberger
W & Sanz
R, Control of Complex Systems,
Springer-Verlag, UK, 2000.
[6] Bemborad A & M Morari,
“Control of systems integrating logic, dynamics and constraints”, Automatica,
35(3),
pp. 407 - 427, 1999.
[7] Bemporad
A, W Heemels & B De Schutter, “On Hybrid Systems and Closed-loop MPC Systems”,
Proc. IEEE CDC, pp 1645–1650,
2001
[8] Bemborad A, Morari M, Dua V & Pistikopoulos E, “The
explicit linear quadratic regulator for constrained systems”, Automatica,
38(1),
pp 3-20, 2002.
[9] Blanke M, Frei C, Kraus F, Patton
R J & Staroswiecki M, “What is fault-tolerant
control?”, Proc. of the 4th IFAC Symp. SAFEPROCESS, pp 40-51,
[10] Blanke M,
Kinnaert M, Lunze J &
Staroswiecki M, Diagnosis & Fault-Tolerant Control,
Springer, Berlin, 2003
[11] Brennan
R, Fletcher M & Norrie D, “An Agent-Based Approach
to Reconfiguration of Real-Time Distributed Control Systems”, IEEE Trans. R & A, 18(4),
2002
[12] Brown MD, Lightbody G & Irwin G W, Non-linear internal model
control using local model networks, IEE
Proc. CTA,144 (6),
pp 505-514, 1997
[13] Boutilier
C, Dean T & J Hanks, “Decision-theoretic planning: Structural assumptions
and computational leverage”, Artif. Intell. Res., 11, pp
1-94, 1999.
[14] Chen
J & Patton R J,
Robust Model-based Fault Diagnosis for Dynamic Systems, Kluwer Academic Publishers, Norwell, MS, USA,
1999.
[15] Chowdhury M & Y Li, Learning
fuzzy control by evolutionary and advantage reinforcements, Int. J. Intelligent Systems,13(10-11),
pp 949-974, 1998.
[16] Debouk R,
Lafortune S & Teneketzis
D Co-ordinated Decentralised Protocols for failure diagnosis in discrete-event
systems, “Discrete Event Dynamic Systems:
Theory and Applications”, 10,
22-86, Kluwer, 2000
[17] Ferrari-Trecate
G, Cuzzola F, Mignone D
& M Morari, “Analysis of discrete-time piecewise
affine and hybrid systems”, Automatica,
38,
pp. 2139-2146, 2002.
[18] Ferrari-Trecate
G, Cuzzola F & M Morari.
“Lagrange
stability and performance analysis of discrete-time piecewise affine systems
with logic states”, Int. J. Con.,
2003.
[19] Finin T,
Joshi A, Kagal L, Ratsimore
O, Korolev V & H Chen. “Information agents for mobile and embedded devices”, Cooperative Information Agents V, Proc. -
Lecture Notes in Artificial Intelligence, 2182, pp. 264-286,
2001.
[20] Hatonen
J, Owens D & Moore K, “An algebraic approach to Iterative Learning Control”,
Int. J. Contr., 77(1),
pp 45-54, 2004.
[21] Hatonen J
&Owens D, “Convex modification to an Iterative Learning Control Law”, Automatica,
40(7),
pp 1213 - 1220, 2004.
[22] Garcia H, “A Hierarchical Platform
for Implementing Hybrid Systems in Process Control”, Control
[23] Hadjiski
M & G Drianovski, Hybrid Model Based MISO
Non-linear Plants Predictive Control", Proc. of DYCOMANS Workshop IV,
[24] Jang J, Sun C & Mizutani E, NeuroFuzzy & Soft Computing, Prentice Hall
Publishing, 1997
[25] Joshi A, Finin T, Yesha Y. “Agents,
mobility, and M-services: Creating the next generation applications and
infrastructure on mobile ad-hoc networks”, Lecture Notes in Computer Science,
2538,
pp.
106-118, 2002
[26] Jordan D & P Smith. Non-linear Ordinary
Differential Equations.
[27] Juang C
& Lin C, “An On-line
Self-Constructing Neural Fuzzy Inference Network and Its Applications”, IEEE Trans. on Fuzzy
Systems,
6(1),
pp 12-32, 1999.
[28] Kambhampati C & Rajasekharan S, “Human motor control perspective to multiple
manipulator modelling”, Biol. Cybern., 89 (4),
pp 254-263, 2003
[29] Kambhampati C & Rajasekharan S, Multiple manipulator control from a human
motor-control perspective, IEEE Trans.
Robot. Autom., 19(3),
pp 433-442, 2003
[30] Kashiwagi
H and Y Li, “Nonparametric
nonlinear model predictive control”, Korean Journal of Chemical
Engineering,
21(2),
pp. 329-337, 2004.
[31] Kosko B,
Fuzzy Cognitive Maps, Int. J. of
Man-Machine Studies, 24, pp 65- 75, 1978
[32] Lafortune
S, Teneketzis, Sampath M,
Sengupta R & Sinnamohideen, “Failure, diagnosis of Dynamic Systems: An
approach based on discrete event systems”, Proc. American Control Conf., pp
2058-2071, 2001
[33] Li Y & A Haeussler, Artificial evolution of neural networks and its
application to feedback control, Int.
J. A I in
[34] Li Y & K Tan, Linear
approximation model network and its formation via evolutionary computation,
Sadhana - Academy Proceedings in Engineering
Sciences, The Indian Academy of Sciences, 25(12),
pp. 97-110, 2000.
[35] Lunze J
& Steffen T, Hybrid reconfigurable control, In G Frehse, S Engell & E Schnieder (eds.), Modelling, Analysis & Design of Hybrid
Systems, pp267-284, Springer, 2002
[36] Mattern
F. “The age of pervasive computing – Everything smart, everything
connected?”, Lecture Notes in Computer Science,
2802:
1-1 2004
[37] McLoone S,
Brown M D, Irwin G, et al., “A hybrid
linear/non-linear training algorithm for feed-forward neural networks”, IEEE Trans. Neural Nets, 9(4),
pp 669-684, 1998.
[38]
[39] Sampath
M, Lafortune S & Teneketzis Active diagnosis of discrete-event systems, IEEE Trans. Auto Contr., 43(7),
pp 908-929, 1998
[40] Patton R J, Fault-tolerant control:
the 1997 situation, Proc. of IFAC Symposium SAFEPROCESS, pp 1033-1055,
[41] Rauch H E, “Autonomous Control
Reconfiguration”, IEEE Cont. Sys. Mag., 15(6),
37-48, 1995.
[42] Roberts M & M Aubert. “A model of the control of Echinococcus multilocularis in
[43] Smith G & C Cheeseman. “A mathematical model for the control of diseases
in wildlife populations: culling, vaccination and fertility control”. Ecological Modelling, 150,
pp 45-53, 2002
[44] Tan K & Y Li, Evolutionary
system identification in the time-domain, J Systems and Control Eng., 211,
pp. 319-323, 1997.
[45] Tan K & Y Li, Evolutionary
L¥
identification and model reduction for robust control, J Systems and Control Eng., 214,
pp. 231-237, 2000.
[46] Tan K & Y Li, Performance-based
control system design automation via evolutionary optimization, Int. J
[47] Tan K & Y Li, Grey-box model
identification via evolutionary computing, Control
Engineering Practice,
10(7),
pp. 673-684,
2002.
[48] Uppal F J
& Patton R J, Neuro-fuzzy uncertainty de-coupling:
a multiple-model paradigm for fault-diagnosis, to appear in J. Adapt. Cont.
& Sig. Proc., Wiley, 2004
[49] http://www.isc-ltd.com/actclub/meetings/meet040527.html
[50] http://www.santafe.edu/sfi/publications/Bulletins/bulletin-spr95/10control.html
[51] http://www.iitp.ru/samara99.htm
[52] http://www.ieeecss.org/about/ABOUTindex.html
[53] http://web.mit.edu/jnt/www/complex.html
[54] http://www.ee.surrey.ac.uk/Personal/D.Jefferies/complexsim.html
[55] Wang H, “Minimum entropy control
for non-Gaussian dynamic stochastic systems”, IEEE Trans. Autom.
Control, 47,
pp 398–403, 2002.
[56] Wolf T & S Choi. “Aggregated hierarchical multicast -
A many-to-many communication paradigm using programmable networks”, IEEE Trans SMC-Part-C, 33(3), pp.
358-369, 2003.
[57] Zayed A, Hussain A & Grimble M, Novel non-linear Multiple-Controller
framework for Complex Systems incorporating a Learning sub-model, invited paper, to appear, Int. J. of Cont. & Intell. Sys., 33,
2004.