Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Machine Intelligence Research
Erschienen in: International Journal of Automation and Computing 4/2014

01.08.2014 | Regular paper

Observer-based Variable Universe Adaptive Fuzzy Controller Without Additional Dynamic Order

verfasst von: Hai-Gang Guo, Bao-Jie Zhang

Erschienen in: Machine Intelligence Research | Ausgabe 4/2014

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

A high-precision fuzzy controller, based on a state observer, is developed for a class of nonlinear single-input-single-output (SISO) systems with system uncertainties and external disturbances. The state observer is introduced to resolve the problem of the unavailability of state variables. Assisted by the observer, a variable universe fuzzy system is designed to approximate the ideal control law. Being auxiliary components, a robust control term and a state feedback control term are designed to suppress the influence of the lumped uncertainties and remove the observation error, respectively. Different from the existing results, no additional dynamic order is required for the control design. All the adaptive laws and the control law are built based on the Lyapunov synthesis approach, and the signals involved in the closed-loop system are guaranteed to be uniformly ultimately bounded. Simulation results performed on Duffing forced oscillation demonstrate the advantages of the proposed control scheme.
Vorheriger Artikel Distributed Power Control in Cooperative Cognitive Ad hoc Networks
Nächster Artikel Robust Global Stabilization of Spacecraft Rendezvous System via Gain Scheduling

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
[1]
L. X. Wang, J. M. Mendel. Fuzzy basis functions, universal approximation, and orthogonal least-squares learning. IEEE Transactions on Neural Networks, vol. 3, no. 5, pp. 807–814, 1992.CrossRef
[2]
A. Boulkroune, M. Tadjine, M. M’Saad, M. Farza. Fuzzy adaptive controller for MIMO nonlinear systems with known and unknown control direction. Fuzzy Sets and Systems, vol. 161, no. 6, pp. 797–820, 2010.CrossRefMATHMathSciNet
[3]
H. F. Ho, Y. K. Wong, A. B. Rad. Robust fuzzy tracking control for robotic manipulators. Simulation Modelling Practice and Theory, vol. 15, no. 7, pp. 801–816, 2007.CrossRef
[4]
Y. C. Chang. Adaptive fuzzy-based tracking control for nonlinear SISO systems via VSS and H ∞ approaches. IEEE Transactions on Fuzzy Systems, vol. 9, no. 2, pp. 278–292, 2001.CrossRef
[5]
S. Purwar, I. N. Kar, A. N. Jha. Adaptive control of robot manipulators using fuzzy logic systems under actuator constraints. Fuzzy Sets and Systems, vol. 152, no. 3, pp. 651–664, 2005.CrossRefMATHMathSciNet
[6]
I. Rojas, H. Pomares, J. Gonzalez, L. J. Herrera, A. Guillen, F. Rojas, O. Valenzuela. Adaptive fuzzy controller: Application to the control of the temperature of a dynamic room in real time. Fuzzy Sets and Systems, vol. 157, no. 16, pp. 2241–2258, 2006.CrossRefMATHMathSciNet
[7]
M. Roopaei, M. Zolghadri Jahromi, R. John, T. C. Lin. Unknown nonlinear chaotic gyros synchronization using adaptive fuzzy sliding mode control with unknown dead-zone input. Communications in Nonlinear Science and Numerical Simulation, vol. 15, no. 9, pp. 2536–2545, 2010.CrossRefMATHMathSciNet
[8]
H. X. Li. Interpolation mechanism of fuzzy control. Science in China Series E-Technological Sciences, vol. 41, no. 3, pp. 312–320, 1998.CrossRefMATHMathSciNet
[9]
A. Sala, T. M. Guerra, R. Babuška. Perspectives of fuzzy systems and control. Fuzzy Sets and Systems, vol. 156, no. 3, pp. 432–444, 2005.CrossRefMATHMathSciNet
[10]
H. T. Yau, J. J. Yan. Adaptive sliding mode control of a high-precision ball-screw-driven stage. Nonlinear Analysis: Real World Applications, vol. 10, no. 3, pp. 1480–1489, 2009.CrossRefMATHMathSciNet
[11]
D. Driankov, H. Hellendoorn, R. Palm. Some research directions in fuzzy control. Theoretical Aspects of Fuzzy Control, John Wiley and Sons, Inc., New York, America, pp. 281–312, 1995.
[12]
A. Green, J. Z. Sasiadek. Heuristic design of a fuzzy controller for a flexible robot. IEEE Transactions on Control Systems Technology, vol. 14, no. 2, pp. 293–300, 2006.CrossRef
[13]
A. Green, J. Z. Sasiadek. A new optimization method for fuzzy controller’s design. Control Theory and Application, vol. 19, no. 2, pp. 279–283, 2002.
[14]
Z. K. Zhang, J. Chang. A fuzzy control algorithm with high controlling precision. Fuzzy Sets and Systems, vol. 140, no. 2, pp. 375–385, 2003.CrossRefMATHMathSciNet
[15]
H. X. Li. To see the success of fuzzy logic from mathematical essence of fuzzy control. Fuzzy Systems and Mathematics, vol. 9, no. 4, pp. 1–14, 1995. (in Chinese)MATH
[16]
H. X. Li, Z. H. Miao, J. Y. Wang. Variable universe adaptive fuzzy control on the quadruple inverted pendulum. Science in China Series E-Technological Sciences, vol. 45, no. 2, pp. 213–224, 2002.CrossRefMATHMathSciNet
[17]
H. X. Li, J. Y. Wang, Y. B. Feng, Y. D. Gu. Hardware implementation of the quadruple inverted pendulum with single motor. Progress in Natural Science, vol. 14, no. 9, pp. 822–827, 2004.CrossRef
[18]
W. Fang, C. S. Jiang. Adaptive variable universe fuzzy predictive control for aerospace vehicle. Control and Decision, vol. 23, no. 12, pp. 1373–1377, 1388, 2008. (in Chinese)MathSciNet
[19]
J. Wang, G. D. Qiao, B. Deng. H ∞ variable universe adaptive fuzzy control for chaotic system. Chaos, Solitons and Fractals, vol. 24, no. 4, pp. 1075–1086, 2005.CrossRefMATHMathSciNet
[20]
H. X. Li, Z. H. Miao, E. S. Lee. Variable universe stable adaptive fuzzy control of a nonlinear system. Computers and Mathematics with Applications vol. 44, no. 5, pp. 799–815, 2002.CrossRefMATHMathSciNet
[21]
J. Wang, Z. Zhang, H. Y. Li. Synchronization of FitzHugh-Nagumo systems in EES via H ∞ variable universe adaptive fuzzy control. Chaos, Solitons and Fractals, vol. 36, no. 5, pp. 1332–1339, 2008.CrossRefMATH
[22]
Y. F. Wang, C. S. Jiang. Direct adaptive fuzzy sliding mode control with variable universe for near space vehicle. Systems Engineering and Electronics, vol. 33, no. 3, pp. 633–637, 2011. (in Chinese).
[23]
S. C. Tong, H. X. Li, W. Wang. Observer-based adaptive fuzzy control for SISO nonlinear systems. Fuzzy Sets and Systems, vol. 148, no. 3, pp. 355–376, 2004.CrossRefMATHMathSciNet
[24]
Y. G. Leu, T. T. Lee, W. Y. Wang. Observer-based adaptive fuzzy-neural control for unknown nonlinear dynamical systems. IEEE Transactions on Systems, Man, and Cybernetics. Part B: Cybernetics, vol. 29, no. 5, pp. 583–591, 1999.CrossRef
[25]
Y. G. Leu, W. Y. Wang, T. T. Lee. Observer-based direct adaptive fuzzy-neural control for nonaffine nonlinear systems. IEEE Transactions on Neural Networks, vol. 16, no. 4, pp. 853–861, 2005.CrossRef
[26]
W. Y. Wang, C. Y. Cheng, Y. G. Leu. An online GA-based output-feedback direct adaptive fuzzy-neural controller for uncertain nonlinear systems. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 34, no. 1, pp. 334–345, 2004.CrossRef
[27]
J. H. Park, G. T. Park, S. H. Kim, C. J. Moon. Output-feedback control of uncertain nonlinear systems using a self-structuring adaptive fuzzy observer. Fuzzy Sets and Systems, vol. 151, no. 1, pp. 21–42, 2005.CrossRefMATHMathSciNet
[28]
A. Boulkroune, M. Tadjine, M. MSaad, M. Farza. How to design a fuzzy adaptive controller based on observers for uncertain affine nonlinear systems. Fuzzy Sets and Systems, vol. 159, no. 8, pp. 926–948, 2008.CrossRefMATHMathSciNet
[29]
A. Boulkroune, M. MSaad. On the design of observer-based fuzzy adaptive controller for nonlinear systems with unknown control gain sign. Fuzzy Sets and Systems, vol. 201, pp. 71–85, 2012.CrossRefMATHMathSciNet
[30]
C. C. Kung, T. H. Chen. Observer-based indirect adaptive fuzzy sliding mode control with state variable filters for unknown nonlinear dynamical systems. Fuzzy Sets and Systems, vol. 155, no. 2, pp. 292–308, 2005.CrossRefMATHMathSciNet
[31]
J. Wang, Q. D. Qiao, B. Deng. Observer-based robust adaptive variable universe fuzzy control for chaotic system. Chaos, Solitons and Fractals, vol. 23, no. 3, pp. 1013–1032, 2005.CrossRefMATHMathSciNet
[32]
H. X. Li. Adaptive fuzzy controllers based on variable universe. Science in China Series E: Technological Sciences, vol. 42, no. 1, pp. 10–20, 1999.CrossRefMATHMathSciNet
[33]
H. G. Guo, H. X. Li, W. J. Zhao, Z. K. Song. Direct adaptive fuzzy sliding mode control with variable universe fuzzy switching term for a class of MIMO nonlinear systems. Mathematical Problems in Engineering, vol. 2012, Article ID 543039, pp. 1–21, 2012.MathSciNet
[34]
W. W. Shan, D. M. Jin, Y. Liang. Variable universe adaptive fuzzy logic controller CMOS analog chip implementation. Acta Electronica Sinica, vol. 37, no. 5, pp. 913–917, 2009. (in Chinese)
[35]
M. Chemachema. Output feedback direct adaptive neural network control for uncertain SISO nonlinear systems using a fuzzy estimator of the control error. Neural Networks, vol. 36, pp. 25–34, 2012.CrossRefMATH
[36]
J. H. Park, G. T. Park. Adaptive fuzzy observer with minimal dynamic order for uncertain nonlinear systems. IEE Proceedings: Control Theory and Application, vol. 150, no. 2, pp. 189–197, 2003.
[37]
P. A. Ioannou, J. Sun. Robust Adaptive Control, Prentice-Hall, Englewood Cliffs, NJ, pp. 126–134, 545–576, 1996.MATH
[38]
H. G. Guo, H. X. Li, B. J. Zhang. Observer-based direct adaptive fuzzy controller with no additional dynamic order. ICIC Express Letters, vol. 7, no. 12, pp. 3183–3189, 2013.
[39]
Y. P. Pan, M. J. Er, D. P. Huang, Q. R. Wang. Adaptive fuzzy control with guaranteed convergence of optimal approximation error. IEEE Transactions on Fuzzy Systems, vol. 19, no. 5, pp. 807–818, 2011.CrossRef
[40]
A. Boulkroune, M. Tadjine, M. MSaad, M. Farza. Adaptive fuzzy observer for uncertain nonlinear systems. Control and Intelligent Systems, vol. 39, no. 3, pp. 145, 2011.CrossRefMATHMathSciNet
[41]
J. Na, X. M. Ren, G. Herrmann, Z. Qiao. Adaptive neural dynamic surface control for servo systems with unknown dead-zone. Control Engineering Practice, vol. 19, no. 11, pp. 1328–1343, 2011.CrossRef
[42]
A. Boulkroune, M. MSaad. A practical projective synchronization approach for uncertain chaotic systems with dead-zone input. Communications in Nonlinear Science and Numerical Simulation, vol. 16, no. 11, pp. 4487–4500, 2011.CrossRefMATHMathSciNet
[43]
A. Boulkroune, M. MSaad. A fuzzy adaptive variable-structure control scheme for uncertain chaotic MIMO systems with sector nonlinearities and dead-zones. Expert Systems with Applications, vol. 38, no. 12, pp. 14744–14750, 2011.CrossRef
[44]
Y. J. Liu, N. Zhou. Observer-based adaptive fuzzy-neural control for a class of uncertain nonlinear systems with unknown dead-zone input. ISA Transactions, vol. 49, no. 4, pp. 462–469, 2010.CrossRef
Metadaten
Titel
Observer-based Variable Universe Adaptive Fuzzy Controller Without Additional Dynamic Order
verfasst von
Hai-Gang Guo
Bao-Jie Zhang
Publikationsdatum
01.08.2014
Verlag
Springer-Verlag
Erschienen in
Machine Intelligence Research / Ausgabe 4/2014
Print ISSN: 2731-538X
Elektronische ISSN: 2731-5398
DOI
https://doi.org/10.1007/s11633-014-0808-3

Weitere Artikel der Ausgabe 4/2014

International Journal of Automation and Computing 4/2014 Zur Ausgabe

Regular paper

Modeling and Analysis for Streaming Service Systems

Regular paper

Chaos Control of a Chemical Chaotic System via Time-delayed Feedback Control Method

Regular paper

Adaptive H ∞ Control for Nonlinear Hamiltonian Systems with Time Delay and Parametric Uncertainties

Regular paper

Reduced Order Modeling & Controller Design for Mass Transfer in a Grain Storage System

Regular paper

Robust Iterative Learning Control of Single-phase Grid-connected Inverter

Regular paper

Distributed Power Control in Cooperative Cognitive Ad hoc Networks