Tase tsk model based inverse model controller and its application

Intelligent Automation and Soft Computing

Volumn 16, Issue 1, 2010, Pages 13-24

  Wang, Yaonan ^a   Yuan, Xiaofang ^a   Wu, Lianghong ^a  

^a HUNAN UNIVERSITY   (China)

Author keywords

Function approximation; Fuzzy logic system; Identification; Inverse control; Nonlinear control systems

Indexed keywords

EID: 75349091430     PISSN: 10798587     EISSN: 2326005X     Source Type: Journal    
DOI: 10.1080/10798587.2010.10643060     Document Type: Article

References (16)

1. C.W. Li and Feng Y.K., The inverse system method. for multi-variable nonlinear control, Beijing:Tsinghua University Press, 1991.
2. H. Deng, F.C. Sun, and Z.Q. Sun, “Robust control of robotic manipulators using fuzzy inverse model,”’ ACTA AUTOMATIC SINK, 27, pp. 521–530, 2001.
3. R. Boukezzoula, S. Galichet, and L. Foulloy, “Nonlinear internal model control:application of inverse model based fuzzy control,” IEEE Trans. on Fuzzy Systems, 11, pp. 814–829, 2003.
4. X. Dai, J. Liu, C. Feng, and D. He, “Neural network -th order inverse system method for the control of nonlinear continuous systems,” IEEE Proceedings:Control Theory and Applications, 145, pp. 519–522, 1998.
5. G. L. Plett, “Adaptive inverse control of linear and nonlinear systems using dynamic neural networks,” IEEE Trans on Neural Networks, 14, pp. 360–376, 2003.
6. H. X. Li and H. Deng, “An Approximate Internal Model-Based Neural Control for Unknown Nonlinear Discrete Processes,” IEEE Trans on Neural Networks, 17, pp. 659–669, 2006.TaSe TSK Model Based Inverse Model Controller and Its Application 23.
7. H. P. Ren, “Chaos control and anti-control via a fuzzy neural network inverse systems method,” Chinese Physics Letters, 19, pp. 1054–1057, 2002.
8. L.J. Herrera, H. Pomares, L Rojas, J. Gonzalez, and O. Valenzuela, “Function approximation through fuzzy systems using Taylor series expansion-based rules:interpretability and parameter tuning,” Lecture Notes in Computer Science, 2972, pp. 252–261, 2004.
9. L. J. Herrera, H. Pomares, L Rojas, O. Valenzuela, and A. Prieto, “TaSe, a Taylor series based fuzzy system model that combines interpretability and accuracy,” Fuzzy Sets and Systems, 153, pp. 403–427, 2005.
10. L. J. Herrera, H. Pomares, L Rojas, A. Guillen, A. Prieto, and O. Valenzuela, “Recursive prediction for long term time series forecasting using advanced models,” Neurocomputing, 70, pp. 2870–2880, 2007.
11. S. S. Ge, J. Zhang, and T. H. Lee, “Adaptive MNN control for a class of nonaffine NARMAX systems with disturbances,” System and Control Letters, 53, pp. 1–12, 2004.
12. X. F. Yuan and Y. N. Wang, “LS-SVM approximate based inverse controller with uncertainty compensation,” Journal of Dynamic Systems, Measurement and Control, 129, pp. 845–850, 2007.
13. O. Adetona, S. Sathananthan, and L. H. Keel, “Robust adaptive control of nonaffine nonlinear plants with small input signal changes,” IEEE Trans. on Neural Networks, 15, pp. 408–416, 2004.
14. Y. N. Wang and X. F. Yuan, “SVM approximate based internal model control strategy,” ACTA AUTOMATIC SINK, 34(2):172–179, 2008.
15. J. T. Spooner, M. Maggiore, R. Ordonez, and K M. Passino, Stable adaptive control and estimation for nonlinear systems, New York:Wiley, 2002.
16. O. Umit, H. Sulgi, and Y.D. Pan, “Discrete-time sliding mode control of electronic throttle valve,” In:Proc. of 40th IEEE Conf. on Decision and Control, pp. 1819–1824, 2001.