Permanent magnet DC motor parameters estimation via universal adaptive stabilization

Abstract This paper presents a quick and effective adaptive estimation methodology for parameters estimation of a permanent magnet (PM) DC motor. The proposed technique uses a universal adaptive stabilizer (UAS). This technique estimates PMDC motor parameters in a single experimental run using input voltage, current and speed. Over time, due to aging and wear, a motor’s parameters values do not match those in the datasheet. Mathematical proofs, experimental results supporting the proposed approach are presented. Despite the persistence of excitation condition not being imposed, the proposed technique produces good results, and is verified in earlier work on Li-ion battery parameters estimation.

[1]  D. Puangdownreong,et al.  Application of flower pollination algorithm to parameter identification of DC motor model , 2017, 2017 International Electrical Engineering Congress (iEECON).

[2]  Jaime Álvarez-Gallegos,et al.  Bio-inspired adaptive control strategy for the highly efficient speed regulation of the DC motor under parametric uncertainty , 2019, Appl. Soft Comput..

[3]  Abdulah Aksamovic,et al.  Parameter identification and digital control of speed of a permanent magnet DC motors , 2011, 2011 XXIII International Symposium on Information, Communication and Automation Technologies.

[4]  S. Sastry Nonlinear Systems: Analysis, Stability, and Control , 1999 .

[5]  Romeo Ortega,et al.  Performance Enhancement of Parameter Estimators via Dynamic Regressor Extension and Mixing* , 2017, IEEE Transactions on Automatic Control.

[6]  Romeo Ortega,et al.  On dynamic regressor extension and mixing parameter estimators: Two Luenberger observers interpretations , 2018, Autom..

[7]  Romeo Ortega,et al.  A parameter estimation approach to state observation of nonlinear systems , 2015, 2015 54th IEEE Conference on Decision and Control (CDC).

[8]  Wei Wu DC motor identification using speed step responses , 2010, Proceedings of the 2010 American Control Conference.

[9]  Mohammad A. Jaradat,et al.  A comparison of adaptive trajectory tracking controllers for wheeled mobile robots , 2015, 2015 10th International Symposium on Mechatronics and its Applications (ISMA).

[10]  Jing Na,et al.  Adaptive Estimation of Time-Varying Parameters With Application to Roto-Magnet Plant , 2021, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[11]  Yan Li,et al.  Experimental Studies of a Fractional Order Universal Adaptive Stabilizer , 2008, 2008 IEEE/ASME International Conference on Mechtronic and Embedded Systems and Applications.

[12]  Dorin Sendrescu,et al.  Parameter identification of a DC motor via distribution based approach , 2012, 2012 17th International Conference on Methods & Models in Automation & Robotics (MMAR).

[13]  Ahmed Rubaai,et al.  Online identification and control of a DC motor using learning adaptation of neural networks , 2000 .

[14]  M. F. da Silva,et al.  Parameters Identification and Analysis of Brushless Direct Current Motors , 2016, IEEE Latin America Transactions.

[15]  Romeo Ortega,et al.  Enhanced Parameter Convergence for Linear Systems Identification: The DREM Approach* , 2018, 2018 European Control Conference (ECC).

[16]  Yangquan Chen,et al.  When is a Mittag-Leffler function a Nussbaum function? , 2009, Autom..

[17]  Shuang Hu,et al.  Research on adaptive identification methods for time-variant parameters of rare earth brushless DC motors , 2015, The 27th Chinese Control and Decision Conference (2015 CCDC).

[18]  H. Sira-Ramírez,et al.  Open-loop algebraic identification method for a DC motor , 2007, 2007 European Control Conference (ECC).

[19]  Habibur Rehman,et al.  Universal Adaptive Stabilizer Based Optimization for Li-Ion Battery Model Parameters Estimation: An Experimental Study , 2018, IEEE Access.

[20]  Habibur Rehman,et al.  UAS based Li-ion battery model parameters estimation , 2017 .

[21]  M Hadef,et al.  Parameter Identification of a DC Motor via Moments Method , 2008 .

[22]  Shayok Mukhopadhyay,et al.  Experimental verification of UAS based battery terminal voltage collapse detection on a simple embedded platform , 2018, 2018 11th International Symposium on Mechatronics and its Applications (ISMA).

[23]  Griselda Saldaña-González,et al.  A DC/DC Buck-Boost Converter–Inverter–DC Motor System: Sensorless Passivity-Based Control , 2018, IEEE Access.

[24]  Romeo Ortega,et al.  Relaxing the conditions for parameter estimation-based observers of nonlinear systems via signal injection , 2018, Syst. Control. Lett..

[25]  P. Geethanjali,et al.  PMDC Motor Parameter Estimation Using Bio-Inspired Optimization Algorithms , 2017, IEEE Access.

[26]  Fumin Zhang,et al.  A high-gain adaptive observer for detecting Li-ion battery terminal voltage collapse , 2014, Autom..

[27]  Ramon Silva-Ortigoza,et al.  DC/DC Buck Power Converter as a Smooth Starter for a DC Motor Based on a Hierarchical Control , 2015, IEEE Transactions on Power Electronics.