Create New Account
Login
Search or Buy Articles
Browse Journals
Browse Proceedings
Submit your Paper
Submission Information
Journal Review
Recommend to Your Library
Call for Papers
Local Linear PID Controllers for Nonlinear Control
J. Lan, J. Cho, D. Erdogmus, J.C. Principe, M.A. Motter, and J. Xu
References
[1] O. Nelles, Nonlinear system identification (New York: Springer,2001).
[2] I.J. Leontaritis & S.A. Billings, Input-output parametric modelsfor nonlinear systems, Part 1: Deterministic nonlinear systems,International Journal of Control, 41(2), 1985, 303–328.
[3] K.S. Narendra & K. Parthasarathy, Identification and controlof dynamical systems using neural networks, IEEE Trans. onNeural Networks, 1(1), 1990, 4–27.
doi:10.1109/72.80202
[4] T.A. Johansen & B.A. Foss, Constructing NARMAX modelsusing ARMAX models, International Journal of Control, 58(5),1993, 1125–1153.
doi:10.1080/00207179308923046
[5] X. Ni, M. Verhaegen, A.J. Krijgsman, & H.B. Verbruggen, Newmethod for identification and control of nonlinear dynamicsystems, Engineering Applications of Artificial Intelligence,9(3), 1996, 231–243.
doi:10.1016/0952-1976(96)00015-2
[6] D.M. Walker, N.B. Tufillaro, & P. Gross, Radial-basis modelsfor feedback systems with fading memory, IEEE Trans. onCircuits and Systems, 48(9), 2001, 1147–1151.
doi:10.1109/81.948445
[7] B.S. Kim & A.J. Calise, Nonlinear flight control using neuralnetworks, Journal of Guidance, Control, and Dynamics, 20(1),1997, 26–33.
[8] C.H. Lee & M.J. Chung, Gain-scheduled state feedback controldesign technique for flight vehicles, IEEE Trans. on Aerospaceand Electronic Systems, 37(1), 2001, 173–182.
doi:10.1109/7.913676
[9] R.C. Dorf & R.H. Bishop, Modern control systems, 8th ed.(New York: Addison Wesley, 1998).
[10] J.S.R. Jang, ANFIS: Adaptive-network-based fuzzy inferencesystem, IEEE Trans. on Systems, Man and Cybernetics, PartB, 23(3), 1993, 665–685.
doi:10.1109/21.256541
[11] J. Lan, J.C. Principe, & M.A. Motter, Identification of dynamical systems using GMM with VQ initialization, Proc. IJCNN’03, 1, 2003, 764–768.
doi:10.1109/IJCNN.2003.1223478
[12] J.C. Principe, L. Wang, & M.A. Motter, Local dynamic modeling with self-organizing maps and applications to nonlinear system identification and control, Proc. IEEE, 86(11), 1998, 2240–2258.
doi:10.1109/5.726789
[13] T. Kohonen, Self-organizing maps (New York: Springer, 1995).
[14] G.J. Mclachlan & D. Peel, Finite mixture models (New York:Wiley, 2001).
[15] J. Stark, D.S. Broomhead, M.E. Davies, & J. Huke, Takensembedding theorems for forced and stochastic systems, Nonlinear Analysis, Theory Methods, and Applications, 30(8), 1997, 5303–5314.
doi:10.1016/S0362-546X(96)00149-6
[16] M. Casdagli, Nonlinear prediction of chaotic time series, Physica D, 35(3), 1989, 335–356.
doi:10.1016/0167-2789(89)90074-2
[17] F. Takens, On numerical determination of the dimension of an attractor, in D. Rand & L.S. Young (Eds), Dynamical systemsand turbulance, Warwick 1980, Lecture Notes in Mathematics,vol. 898, (Berlin: Springer-Verlag, 1981), 366–381.
[18] D.T. Magill, Optimal adaptive estimations of sampled stochastic processes, IEEE Trans. on Automation and Control, AC-10, 1965, 434–439.
doi:10.1109/TAC.1965.1098191
[19] D.G. Lainiotis, Partitioning: A unifying framework for adaptivesystems—I: Estimation—II: Control, Proc. IEEE, 64, 1976,1126–1143, 1182–1197.
[20] B. Martensson, Adaptive stabilization, doctoral diss., LundInstitute of Technology, Lund, Sweden, 1986.
[21] K.S. Narendra & J. Balakrishnan, Adaptive control usingmultiple models, IEEE Trans. on Automatic Control, 4(2),1997, 171–187.
doi:10.1109/9.554398
[22] R.A. Jacobs, M.I. Jordan, S.J. Nowlan, & G.E. Hinton, Adaptive mixtures of local experts, Neural Computation, 3, 1991, 79–87.
doi:10.1162/neco.1991.3.1.79
[23] R. Murray-Smith & T.A. Johansen, Multiple model approachesto modeling and control (New York: Taylor & Francis, 1997).
[24] K.S. Narendra & C. Xiang, Adaptive control of discrete-timesystems using multiple models, IEEE Trans. on AutomaticControl, 45(9), 2000, 1669–1686.
doi:10.1109/9.880617
[25] A.S. Morse, Supervisory control of families of linear set-pointcontroller, Part 1: Exact matching, IEEE Trans. on AutomaticControl, 42, 1996, 1413–1431.
doi:10.1109/9.539424
[26] M.A. Motter, Control of the NASA Langley 16-foot TransonicTunne; with the Self-Organizing Feature Map, doctoral diss.,University of Florida, Gainesville, FL, 1997.
[27] G. Thampi, J.C. Principe, M.A. Motter, J. Cho, & J. Lan,Multiple model based flight control design, Proc. MWSCAS’02,vol. 3, Tulsa, OK, 2002, 133–136.
[28] J. Cho, J. Lan, G. Thampi, J.C. Principe, & M.A. Motter,Identification of aircraft dynamics using a SOM and local linearmodels, Proc. MWSCAS’02, vol. 2, Tulsa, OK, 2002, 148–151.
[29] S. Chen & S. Billings, Representations of non-linear systems:The NARMAX model, International Journal of Control, 49(3),1989, 1013–1032.
[30] R.E. Brown, G.N. Maliotis, & J.A. Gibby, PID self-tuningcontroller for aluminum rolling mill, IEEE Trans. on IndustryApplications, 29(3), 1993, 578–583.
doi:10.1109/28.222430
[31] K.M. Vu, Optimal setting for discrete PID controllers, IEEProc.-D, 139(1), 1992, 31–40.
[32] J. Bao, J.F. Forbes, & P.J. McLellan, Robust MultiloopPID controller design: A successive semidefinite programmingapproach, Industrial and Engineering Chemistry Research, 38,1999, 3407–3419.
doi:10.1021/ie980746u
[33] X. He & H. Asada, A new method for identifying ordersof input-output models for nonlinear dynamic systems, Proc.ACC, 1993, 2520–2524.
[34] S. Haykin, Neural networks: A comprehensive foundation (Englewood Cliffs, NJ: Prentice-Hall, 1998).
Important Links:
Abstract
DOI:
10.2316/Journal.201.2005.1.201-1541
From Journal
(201) Mechatronic Systems and Control (formerly Control and Intelligent Systems) - 2005
Go Back