A Novel and Effective Procedure for an Undergraduate First Control Laboratory Education in State Space-based Motor Identification

State space systems and experimental system identification are essential components of control education. This paper suggests an undergraduate physical experiment for directly identifying the state space model of a DC motor in the laboratory. The experiment is designed using standard undergraduate control laboratory equipment. It does not require advanced knowledge in control and it does not place simplifying assumptions on the motor's model. It is easy to understand and has a good informative component that gracefully introduces an undergraduate student to advanced mathematical tools with direct tangible laboratory outcome.

[1]  Hugues Garnier,et al.  Direct continuous-time approaches to system identification. Overview and benefits for practical applications , 2015, Eur. J. Control.

[2]  R. Monzingo On approximating the step response of a third-order linear system by a second-order linear system , 1968 .

[3]  V. K. Aatre,et al.  Pulse-width-modulated speed control of d.c. motors , 1974 .

[4]  Andreas Griewank,et al.  Evaluating derivatives - principles and techniques of algorithmic differentiation, Second Edition , 2000, Frontiers in applied mathematics.

[5]  George W. Younkin Industrial servo control systems : fundamentals and applications , 1996 .

[6]  M. Salah,et al.  Parameters Identification of a Permanent Magnet DC Motor , 2009 .

[7]  Wei-wu DC Motor Parameter Identification Using Speed Step Responses , 2015 .

[8]  Thorsten Gerber,et al.  Handbook Of Mathematical Functions , 2016 .

[9]  Naresh K. Sinha,et al.  Modern Control Systems , 1981, IEEE Transactions on Systems, Man, and Cybernetics.

[10]  Stergios Stergiopoulos,et al.  Advanced Signal Processing Handbook: Theory and Implementation for Radar, Sonar, and Medical Imaging Real-Time Systems , 2000 .

[11]  Arif A. Al-Qassar,et al.  EXPERIMENTAL DETERMINATION OF ELECTRICAL AND MECHANICAL PARAMETERS OF DC MOTOR USING GENETIC ELMAN NEURAL NETWORK , 2008 .

[12]  K-L. Areerak,et al.  Parameters identification of separately excited DC motor using adaptive tabu search technique , 2010, 2010 International Conference on Advances in Energy Engineering.

[13]  João Carlos Basilio,et al.  State-space parameter identification in a second control laboratory , 2004, IEEE Transactions on Education.

[14]  P. Clement A note on third-order linear systems , 1960 .

[15]  Mats Viberg,et al.  Subspace-based methods for the identification of linear time-invariant systems , 1995, Autom..

[16]  D. Sendrescu,et al.  DC Motor Identification Based on Distributions Method , 2012 .

[17]  P. M. Makila State space identification of stable systems , 1999 .