The Model Reference Adaptive Control of the DC Electric Drive System

Marian Gaiceanu; Cristian Eni; Mihaita Coman; Romeo Paduraru

doi:10.4028/www.scientific.net/AMR.875-877.2030

Paper Titles

A Novel Image Segmentation Enhance Technology for Motion Human Body
p.2006

Higher Order Sliding Mode Control for the Tracking Trajectory - The Twisting Algorithm Applied to the Vehicle Model
p.2014

Recognition Model Based Feature Extraction and Kernel Extreme Learning Machine for High Dimensional Data
p.2020

Feature Selection Approach based on Mutual Information and Partial Least Squares
p.2025

The Model Reference Adaptive Control of the DC Electric Drive System
p.2030

A New Design of Variable Traction Tracked System
p.2036

Monotone Constraint Model Applied to the Reliability Growth Evaluation of Electromechanical Products
p.2042

Magnetic Circuit Design and Analysis Used in Magnetic Sealing Drive Device
p.2047

Underwater Motor Sealing Based on Magnetic Force Coupling
p.2051

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 875-877The Model Reference Adaptive Control of the DC...

The Model Reference Adaptive Control of the DC Electric Drive System

Abstract:

Due to the parametric and structural uncertainty of the DC drive system, an adaptive control method is necessary. Therefore, an original model reference adaptive control (MRAC) for DC drives is proposed in this paper. MRAC ensures on-line adjustment of the control parameters with DC machine parameter variation. The proposed adaptive control structure provides regulating advantages: asymptotic cancellation of the tracking error, fast and smooth evolution towards the origin of the phase plan due to a sliding mode switching k-sigmoid function. The reference model can be a real strictly positive function (the tracking error is also the identification error) as its order is relatively higher than one degree. For this reason, the synthesis of the adaptive control will use a different type of error called augmented or enhanced error. The DC machine with separate excitation is fed at a constant flux. This adaptive control law assures robustness to external perturbations and to unmodelled dynamics.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 875-877)

Pages:

2030-2035

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.875-877.2030

Citation:

Cite this paper

Online since:

February 2014

Authors:

Marian Gaiceanu, Cristian Eni, Mihaita Coman, Romeo Paduraru

Keywords:

DC Motor (DCM), Gradient Adjustment, Model Reference Adaptive Control, Variable Structure

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] IEEE Standard Test Procedure for Polyphase Induction Motors and Generators (2004). IEEE Std 112-2004 (Revision of IEEE Std 112-1996).

DOI: 10.1109/ieeestd.1997.82360

Google Scholar

[2] K. S. Narendra, A. M. Annaswamy: Stable Adaptive Systems, Englewood Cliffs, NJ, Pretince Hall (1989).

Google Scholar

[3] A. Filipescu: Robustness in Compound Adaptive Controlling with Sigmoid Function for Supraunitary relative degree of the Plant Model, The 9'th Symposium on Modelling and Identification systems, SIMSIS9'96, Galati, Romania, pp.45-42 (1996).

Google Scholar

[4] T. Cheng-Hung, and Y. Ming-Feng: Application of CMAC neural network to the control of induction motor drives", Elsevier, Applied Soft Computing 9, p.1187–1196 (2009).

DOI: 10.1016/j.asoc.2009.05.001

Google Scholar

[5] L. Zheng: Model Reference Adaptive Controller Design Based on Fuzzy Inference System, Journal of Information & Computational Science 8: 9, 2011, 1683–1693, http: /www. joics. com (2011).

Google Scholar