Nonlinear discrete-time controller with unknown systems identification based on fuzzy rules emulated network

Applied Soft Computing Journal

Volumn 10, Issue 2, 2010, Pages 390-397

  Treesatayapun, Chidentree ^a,b  

^a DEPARTAMENTO DE FÍSICA   (Mexico)

^b North Chiang Mai University   (Thailand)

Author keywords

Adaptive control; Discrete time; Neuro fuzzy; Nonlinear

Indexed keywords

ADAPTIVE CONTROL; ADAPTIVE CONTROLLERS; CONTROL ALGORITHMS; CONTROL LAWS; CONTROL PARAMETERS; DISCRETE-TIME; FUZZY RULES EMULATED NETWORKS; LEARNING RATES; MODEL ERRORS; MULTIINPUT; NEURO-FUZZY; NON-LINEAR SYSTEM IDENTIFICATION; NONLINEAR DISCRETE-TIME; NONLINEAR DISCRETE-TIME SYSTEMS; OFF-LINE LEARNING; SLIDING CONDITIONS; SYSTEMS IDENTIFICATION; TRACKING ERRORS; WEIGHT VECTOR;

CONTINUOUS TIME SYSTEMS; CONTROL SYSTEM ANALYSIS; CONTROLLERS; DIFFERENTIAL EQUATIONS; DIGITAL CONTROL SYSTEMS; DISCRETE TIME CONTROL SYSTEMS; FUZZY RULES; IDENTIFICATION (CONTROL SYSTEMS); LEARNING ALGORITHMS; LYAPUNOV FUNCTIONS; NONLINEAR SYSTEMS; SIMULATORS;

ADAPTIVE CONTROL SYSTEMS;

EID: 70649105495     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2009.08.005     Document Type: Article

References (19)

1. Lee C.H., and Teng C.C. Identification and control of dynamic system using recurrent fuzzy neural networks. IEEE Trans. Fuzzy Syst. 8 August (2000) 349-366
2. Lin C.T. Neural Fuzzy Systems (1996), Prentice-Hall
3. Treesatayapun C., and Uatrongjit S. Adaptive controller with fuzzy rules emulated structure and its applications'. Eng. Appl. Artif. Intell. 18 (2005) 603-615
4. Treesatayapun C., and Uatrongjit S. Biological systems drug infusion controller using FREN with sliding bounds. IEEE Trans. Biomed. Eng. 53 11 (2006) 2405-2408 Nov
5. Treesatayapun C. Nonlinear systems identification using multi-input fuzzy rules emulated network. ITC-CSCC2006 International Technical Conference on Circuits/Systems, Computers and Communications. July 10-13, 2006, Chiangmai, Thailand (2006)
6. Treesatayapun C. The knowledge-based fuzzy rules emulated network and its applications on direct adaptative nonlinear control systems. Int. J. Uncertain. Fuzziness Knowl. Syst. 13 December (6) (2005)
7. Wang D., and Huang J. A neural-network-based approximation method for discrete-time nonlinear servomechanism problem. IEEE Trans. Neural Netw. 12 May (3) (2001) 591-597
8. Jang J.S., Sun C.T., and Mizutani E. Neuro-Fuzzy and Soft Computing (1997), Prentice-Hall
9. Chen L., and Narendra K. Identification and control of a nonlinear discrete-time system based on its linearization: a unified framework. IEEE Trans. Neural Netw. 15 May (3) (2004) 663-673
10. Chen M., and Linkens D.A. A hybrid neuro-fuzzy PID controllers. Fuzzy Sets Syst. 99 (1998) 27-36
11. He P., and Jagannathan S. Reinforcement learning neural-network-based controller for nonlinear discrete-time systems with input constraints. IEEE Trans. Syst. Man. Cybern. 37 April (2) (2007) 425-436
12. Jagannathan S. Neural Network Control of Nonlinear Discrete-Time Systems (2006), Taylor & Francis, Boca Raton, FL
13. Rudin W. Principles of Mathematical Analysis (1976), McGraw Hill, New York
14. Shi Y., and Mizumoto M. An improvement of neuro-fuzzy learning algorithm for tuning fuzzy rules. Fuzzy Sets Syst. 118 (2001) 339-350
15. Li H.X., and Deng H. An approximation internal model-based neural control for unknown nonlinear discrete processes. IEEE Trans. Neural Netw. 17 May (3) (2006) 659-670
16. Lightbody G., and Irwin G.W. Nonlinear control structures based on embedded neural system models. IEEE Trans. Neural Netw. 8 May (3) (1997) 553-567
17. Rivals I., and Personnaz L. Nonlinear internal model control using neural networks: application to processes with delay and design issues. IEEE Trans. Neural Netw. 11 January (1) (2000) 80-90
18. Alippi C., de Russis C., and Piuri V. A neural-network based control solution to air-fuel ratio control for automotive fuel-injection systems. IEEE Trans. Syst. Man. Cybern. C 33 May (2003) 259-268
19. Cabrera J.B.D., and Narendra K.S. Issues in the application of neural networks for tracking based on inverse control. IEEE Trans. Automatic Control 44 November (1999) 2007-2027