scholarly journals Practical Modeling and Comprehensive System Identification of a BLDC Motor

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Changle Xiang ◽  
Xiaoliang Wang ◽  
Yue Ma ◽  
Bin Xu
Keyword(s):  
System Identification ◽  
Time Domain ◽  
Simple Procedure ◽  
Estimation Method ◽  
Bldc Motor ◽  
Rotor Speed ◽  
Least Squares Approximation ◽  
Unknown Parameters ◽  
Domain Identification ◽  
Identification Techniques

The aim of this paper is to outline all the steps in a rigorous and simple procedure for system identification of BLDC motor. A practical mathematical model for identification is derived. Frequency domain identification techniques and time domain estimation method are combined to obtain the unknown parameters. The methods in time domain are founded on the least squares approximation method and a disturbance observer. Only the availability of experimental data for rotor speed and armature current are required for identification. The proposed identification method is systematically investigated, and the final identified model is validated by experimental results performed on a typical BLDC motor in UAV.

Mass, Stiffness, and Damping Matrix Identification: An Integrated Approach

1992 ◽  
Vol 114 (3) ◽  
pp. 358-363 ◽  
Author(s):  
M. J. Roemer ◽  
D. J. Mook
Keyword(s):  
Steady State ◽  
Time Domain ◽  
Estimation Method ◽  
Integrated Approach ◽  
Modal Identification ◽  
Mode Shapes ◽  
Steady State Condition ◽  
Frequency Domain Techniques ◽  
Identification Techniques ◽  
Stiffness And Damping

Accurate estimates of the mass, stiffness, and damping characteristics of a structure are necessary for determining the control laws best suited for active control methodologies. There are several modal identification techniques available for determining the frequencies, damping ratios, and mode shapes of a structure. However, modal identification methods in both the frequency and time domains have difficulties for certain circumstances. Frequency domain techniques which utilize the steady-state response from various harmonic inputs often encounter difficulties when the frequencies are closely distributed, the structure exhibits a high degree of damping, or the steady-state condition is hard to establish. Time domain techniques have produced successful results, but lack robustness with respect to measurement noise. In this paper, two identification techniques and an estimation method are combined to form a time-domain technique to accurately identify the mass, stiffness, and damping matrices from noisy measurements.

scholarly journals Structural System Identification in the Time Domain using Evolutionary and Behaviorally Inspired Algorithms and their Hybrids

2009 ◽  
Vol 6 (2) ◽  
pp. 64
Author(s):  
S. Sandesh ◽  
Abhishek Kumar Sahu ◽  
K. Shankar
Keyword(s):  
System Identification ◽  
Time Domain ◽  
Numerical Models ◽  
Parametric Identification ◽  
Lumped Mass ◽  
Unknown Parameters ◽  
Mass System ◽  
The Time Domain ◽  
Structural System Identification ◽  
Speed And Accuracy

 In this study, parametric identification of structural properties such as stiffness and damping is carried out using acceleration responses in the time domain. The process consists of minimizing the difference between the experimentally measured and theoretically predicted acceleration responses. The unknown parameters of certain numerical models, viz., a ten degree of freedom lumped mass system, a nine member truss and a non-uniform simply supported beam are thus identified. Evolutionary and behaviorally inspired optimization algorithms are used for minimization operations. The performance of their hybrid combinations is also investigated. Genetic Algorithm (GA) is a well known evolutionary algorithm used in system identification. Recently Particle Swarm Optimization (PSO), a behaviorally inspired algorithm, has emerged as a strong contender to GA in speed and accuracy. The discrete Ant Colony Optimization (ACO) method is yet another behaviorally inspired method studied here. The performance (speed and accuracy) of each algorithm alone and in their hybrid combinations such as GA with PSO, ACO with PSO and ACO with GA are extensively investigated using the numerical examples with effects of noise added for realism. The GA+PSO hybrid algorithm was found to give the best performance in speed and accuracy compared to all others. The next best in performance was pure PSO followed by pure GA. ACO performed poorly in all the cases. 

Development of Control System by Nonlinear Compensation Using Digital Acceleration Control

2010 ◽  
Author(s):  
Takanori Emaru ◽  
Kazuo Imagawa ◽  
Yohei Hoshino ◽  
Yukinori Kobayashi
Keyword(s):  
Control System ◽  
System Identification ◽  
Mechanical System ◽  
Pid Control ◽  
Estimation Method ◽  
Proportional Integral Derivative ◽  
Inertia Term ◽  
Modeling Errors ◽  
Nonlinear Compensation ◽  
Simulation Results

Proportional-Integral-Derivative (PID) control has been most commonly used to operate mechanical systems. In PID control, however, there are limits to the accuracy of the resulting movement because of the influence of gravity, friction, and interaction of joints. We have proposed a digital acceleration control (DAC) that is robust over these modeling errors. One of the most practicable advantages of DAC is robustness against modeling errors. However, it does not always work effectively. If there are modeling errors in the inertia term of the model, the DAC controller cannot control a mechanical system properly. Generally an inertia term is easily modeled in advance, but it has a possibility to change. Therefore, we propose an online estimation method of an inertia term by using a system identification method. By using the proposed method, the robustness of DAC is considerably improved. This paper shows the simulation results of the proposed method using 2-link manipulator.

scholarly journals A Data Driven RUL Estimation Framework of Electric Motor Using Deep Electrical Feature Learning from Current Harmonics and Apparent Power

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3156
Author(s):  
Tanvir Alam Shifat ◽  
Rubiya Yasmin ◽  
Jang-Wook Hur
Keyword(s):  
Reactive Power ◽  
Short Term Memory ◽  
Estimation Method ◽  
Feature Learning ◽  
Remaining Useful Life ◽  
Total Power ◽  
Bldc Motor ◽  
Current Harmonics ◽  
Power Monitoring ◽  
Brushless Dc

An effective remaining useful life (RUL) estimation method is of great concern in industrial machinery to ensure system reliability and reduce the risk of unexpected failures. Anticipation of an electric motor’s future state can improve the yield of a system and warrant the reuse of the industrial asset. In this paper, we present an effective RUL estimation framework of brushless DC (BLDC) motor using third harmonic analysis and output apparent power monitoring. In this work, the mechanical output of the BLDC motor is monitored through a coupled generator. To emphasize the total power generation, we have analyzed the trend of apparent power, which preserves the characteristics of real power and reactive power in an AC power system. A normalized modal current (NMC) is used to extract the current features from the BLDC motor. Fault characteristics of motor current and generator power are fused using a Kalman filter to estimate the RUL. Degradation patterns for the BLDC motor have been monitored for three different scenarios and for future predictions, an attention layer optimized bidirectional long short-term memory (ABLSTM) neural network model is trained. ABLSTM model’s performance is evaluated based on several metrics and compared with other state-of-the-art deep learning models.

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