Permanent magnet DC motor (PMDC) model identification and controller design

Author:

Alkamachi Ahmed1

Affiliation:

1. University of Baghdad , Al-Khwarizmi College of Engineering, Mechatronics Engineering Department , Baghdad , Iraq

Abstract

Abstract System modeling is a set of mathematical equations that describe the dynamical behavior of a system. It is considered as a primary concern in determining a suitable controller to meet specific requirements. An autoregressive with exogenous terms (ARX) model for a PMDC motor is identified experimentally based on the recursive least square (RLS) method. Adaptive discrete pole placement controller (APPC) is proposed and designed aiming to control the motor revolving speed. For the comparison purpose, a discrete Proportional Integral (PI) controller is also considered in this work. The steady step response, transient response, and the mean squared error (MSE) is counted throughout the comparison. The e ect of the uncertainties in the PMDC model is also investigated in this paper. The result shows a superiority in the performance of the proposed controller compared to that obtained using PI controller.

Publisher

Walter de Gruyter GmbH

Reference12 articles.

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2. [2] T. Kara and I. Eker, “Nonlinear Closed-Loop Direct Identification of a DC Motor with Load for Low Speed Two-Directional Operation”, Electrical Engineering, vol. 68, no. 2, pp. 87–96, 2004.10.1007/s00202-003-0189-z

3. [3] R. Krneta, S. Antić, and D. Stojanović, “Recursive Least Squares Method in Parameters Identification of DC Motors Models”, Facta Universitatis-Series: Electronics Energetics, vol. 18, no. 3, pp. 467–478, 2005.10.2298/FUEE0503467K

4. [4] M. Ruderman, J. Krettek, F. Ho mann, and T. Bertram, “Optimal State Space Control of DC Motor”, IFAC Proceedings, vol. 41, no. 2, pp. 5796–5801, 2008.

5. [5] G. Mamani, J. Becedas, and V. Feliu-batlle, “On-Line Fast Algebraic Parameter State Estimation for a DC Motor Applied to Adaptive Control”, Proceedings of the World Congress on Engineering vol. 2, 2008.

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