Abstract
As the performance of PI controllers can deteriorate rapidly for highly nonlinear systems, nonlinear PI controllers are developed. An approach to design these controllers is to switch between several linear PI controllers using fuzzy logic based on the Takagi-Sugeno model. Following this approach, nonlinear PI controllers are derived in this paper using B-spline neurofuzzy networks. Design guidelines and on-line training of the proposed controller are devised, and the performance is illustrated by a simulated two-tank water level control rig. Comparison with conventional PI controllers is also made.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ho, W.K., Hang, C.C., Zhou, J.H.: Performance and gain and phase margins of well-know PI tuning formulas. IEEE Transactions on Control Systems Technology 3(2), 245–248 (1995)
Gawthrop, P.J.: Self-tuning PID controllers: Algorithms and Implementation. IEEE Transaction on Automatic Control 31(3), 201–209 (1986)
Bittanti, S., Piroddi, L.: GMV technique for nonlinear control with neural networks. IEE Proceedings-D Control Theory and Applications 141(2), 57–69 (1994)
Zhang, T., Ge, S.S., Hang, C.C.: Neural-based direct adaptive control for a class of general nonlinear systems. International Journal of Systems Science 28(10), 1011–1020 (1997)
Narendra, K.S., Balakrishnan, J.: Adaptive control using multiple models. IEEE Transactions on Automatic Control 42(2), 171–187 (1997)
Chan, C.W., Liu, X.J., Yeung, W.K.: Neurofuzzy network based self-tuning control with offset eliminating. International Journal of Systems Science 34(2), 111–122 (2003)
Johansen, T.A., Foss, B.A.: Constructing NARMAX models using ARMAX models. International Journal of Control 58(5), 1125–1153 (1993)
Hong, X., Harris, C.J.: A neurofuzzy network knowledge extraction and extended Gram-Schmidt algorithm for model subspace decomposition. IEEE Transactions on Fuzzy Systems 11(4), 528–541 (2003)
Chan, C.W., Cheung, K.C., Yeung, W.K.: A computation-efficient on-line training algorithm for neurofuzzy networks. International Journal of Systems Science 31(3), 297–306 (2000)
Mok, H.T., Chan, C.W., Yeung, W.K.: Neurofuzzy network based adaptive nonlinear PI controllers. Control and Intelligent Systems 34(3), 216–224 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chan, Y.F., Chan, C.W., Mok, H.T. (2009). Adaptive Neurofuzzy Network Based PI Controllers with Multi-objective Functions. In: Chien, BC., Hong, TP., Chen, SM., Ali, M. (eds) Next-Generation Applied Intelligence. IEA/AIE 2009. Lecture Notes in Computer Science(), vol 5579. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02568-6_61
Download citation
DOI: https://doi.org/10.1007/978-3-642-02568-6_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02567-9
Online ISBN: 978-3-642-02568-6
eBook Packages: Computer ScienceComputer Science (R0)