A Choquet Fuzzy Integral Controller for Stabilization and Tracking Application

Article Preview

Abstract:

The stabilization, tracking and pointing systems are essential features of modern fire control and surveillance systems. The criteria for precision stabilization may vary from a few hundred micro-radians to few nano-radians for achieving jitter free image depending upon the application scenario. The Line of sight (LOS) is stabilized by mounting the optical payload on a gimbal platform and designing the control system around them. The paper describes a choquet fuzzy integral based control algorithm developed for LOS controlling and stabilizing application. In this approach, q-measure is estimated to simplify the computation of λ-measure that aggregates the information from the weighted inputs. The output of fuzzy rules, which are formulated by defining the product of weighted inputs are required to compute the fuzzy measure in the form of Choquet fuzzy integral.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 403-408)

Pages:

5000-5006

Citation:

Online since:

November 2011

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2012 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] J.A. Profeta III, W.G. Vogt, and M.H. Mickle, Torque disturbance rejection in high accuracy systems, IEEE Trans. Aerospace Electronic system. 26(2), 232-237 (1990).

DOI: 10.1109/7.53456

Google Scholar

[2] J.A. Profeta III, W.G. Vogt, and M.H. Mickle, Disturbance estimation and compensation in linear system, IEEE Trans. Aerospace Electronic system. 26(2), 225-231 (1990).

DOI: 10.1109/7.53455

Google Scholar

[3] C.C. Lee, Fuzzy logic in control systems: fuzzy logic controller-part I, IEEE Trans. Electronic system Man Cyben. 20(2), 404-418 (1990).

DOI: 10.1109/21.52551

Google Scholar

[4] C.C. Lee, Fuzzy logic in control system: fuzzy logic controller-part II, IEEE Trans. Electronic system Man Cyben. 20(2), 419-435 (1990).

DOI: 10.1109/21.52552

Google Scholar

[5] M. F Azeem, M. Hanmandlu,N. Ahmed, Structure identification of generalized adaptive neuro-fuzzy inference systems, IEEE Trans. Fuzzy system 11(5) (2003) 666-678.

DOI: 10.1109/tfuzz.2003.817857

Google Scholar

[6] Y. lin,G.A. CunninghamIII, A new approach to fuzzy neural system modeling, IEEE Trans. Fuzzy systems. 3(2) (1995) 190-198.

DOI: 10.1109/91.388173

Google Scholar

[7] J. -H. Chiang, Choquet fuzzy integral-based hierarchical networks for decision analysis, IEEE Trans. Fuzzy Syst. 7 (1) (1999) 63–71.

DOI: 10.1109/91.746311

Google Scholar

[8] M. sugeno,T. yasukawa, A fuzzy logic based approach to qualitative modeling, IEEE Trans. Fuzzy system 1(1)(1993) 7-31.

DOI: 10.1109/tfuzz.1993.390281

Google Scholar

[9] M. Grabish, H.T. Nguyenand, E.A. Walker, Fundamentals of Uncertainty Calculi with Applications to Fuzzy Inference, Kluwer Academic Publish- ers, (1995).

Google Scholar

[10] M. Grabish, T. Murofushi, M. Sugeno, Fuzzy Measures and Integrals: Theory and Applications, Physica-Verlag, Heidelberg, (2000).

Google Scholar

[11] M.A. Mohammed, Q-measures: an efficient-extension of the Sugeno λ-measure, IEEE Trans. Fuzzy Syst. 11 (3) (2003) 419–426.

DOI: 10.1109/tfuzz.2003.812701

Google Scholar

[12] K.S. Narendra, K. Parathasarathy, Identification and control of a dynamic system using neural network, IEEE Trans. Neural Network 1 (1) (1990)4–27.

Google Scholar

[13] S. Srivastava, M. Singh, A.N. Jha, Control and identification of a non linear systems affected by noise using wavelet network, in: Proceedings of International Workshop on Intelligent Systems Design and Application (ISDA-2002), Atlanta, USA, August, (2002).

Google Scholar

[14] M. Sugeno, Fuzzy measures and fuzzy integrals- a survey in M.M. Gupta, G.N. Saridis, B.R. Gaines (Eds. ), Fuzzy Automata and Decision Processes, North-Holland, Amsterdam, The Netherlands, 1977, p.89– 102.

Google Scholar

[15] M. Sugeno, G.T. Kang, Structure identification of fuzzy model, Fuzzy Sets Syst. 28 (1988) 15–33.

DOI: 10.1016/0165-0114(88)90113-3

Google Scholar

[16] S. Srivastava, M. Singh, M. Hanmandlu, A.N. Jha, New fuzzy wavelet neural networks for system identification and control, J. Appl. Soft Comput. 6 (I) (2005) 1–17.

DOI: 10.1016/j.asoc.2004.10.001

Google Scholar

[17] J.M. Zurada, Introduction to Artificial Neural Systems, West Publishing, St. Paul, MN, (1992).

Google Scholar

[18] L.A. Zadeh, Aggregation operators and fuzzy systems modeling, Fuzzy Sets Syst. 67 (1994) 129–146.

Google Scholar

[19] K. Tanaka, Modeling and control of carbon monoxide concentration using neuro-fuzzy technique, IEEE Trans. Fuzzy Syst. 3 (3) (1995) 271–279.

DOI: 10.1109/91.413233

Google Scholar

[20] L.C. Lang, J.S. Kwon, On the representation of Choquet integrals of set valued functions and null sets, Fuzzy Sets Syst. 112 (2000) 233–239.

DOI: 10.1016/s0165-0114(98)00184-5

Google Scholar