An Improved Torque Feed-forward Control with Observer-based Inertia Identification in PMSM Drives

Zhao, Shufang; Cui, Lin Hai; Liu, Guiying; Chen, Yangsheng

doi:10.11142/jicems.2013.2.1.69

Cited by 6 publications

(5 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In accordance with the actual situation in this paper, l is chosen as 0.9. By means of (15)- (17), inertia J and torque T L can be estimated, andĴ andT L are utilised to represent their estimations. Hence, estimation errorsJ andT L can be written as follows…”

Section: Estimation Of Inertia and Torque For Mees Devicementioning

confidence: 99%

“…A proportional–integral (PI) controller [15] and Takagi‐Sugeno PI fuzzy controller [16] are presented for a DC electric drive system. In [17], load torque observer and inertia identification are, respectively, designed to observe parameters and suppress perturbation simultaneously. Two separate observers are introduced to observe inertia and torque in [18].…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, implementation of these intelligent algorithms is relatively difficult, and these control processes are more complicated. Moreover, when load disturbance rejection becomes an issue of great significance, the conventional hot‐spot issue of low‐speed control for PMSM tends to be frequently ignored in existing literatures [14, 17].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Low speed control and implementation of permanent magnet synchronous motor for mechanical elastic energy storage device with simultaneous variations of inertia and torque

Guo

et al. 2016

IET Electric Power Applications

View full text Add to dashboard Cite

The spiral torsion spring-based mechanical elastic energy storage (MEES) device presented previously with inherent characteristic of simultaneous variations of inertia and torque is disadvantage to be actuated by conventional control method. This study proposes an improved non-linear backstepping control scheme based on combination of recursive least squares (RLS) and differential evolution (DE) optimisation to regulate permanent magnet synchronous motor (PMSM) at a very low speed and control MEES device effectively. For this presented control scheme, first, a single RLS method with forgetting factor is presented to simultaneously estimate the time-varying inertia and torque of MEES device. Second, on the basis of estimation results, an improved non-linear backstepping control algorithm considering parameter variations in derivation process is proposed to drive PMSM to operate at a very low speed. Finally, a self-adaptive parameter DE method is designed to optimise the several control parameters. The performance of the proposed methodology is verified through simulations and experiments.

show abstract

Section: Estimation Of Inertia and Torque For Mees Devicementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Low speed control and implementation of permanent magnet synchronous motor for mechanical elastic energy storage device with simultaneous variations of inertia and torque

Guo

et al. 2016

IET Electric Power Applications

View full text Add to dashboard Cite

show abstract

“…Permanent magnet synchronous drive systems have been widely used in industrial applications such as flexible robot arms, paper machines, and computerized numerical control (CNC) machine tools Ha et al, 2015; Soreshjani, 2015; Zhao and Chen et al, 2013). In general, these drive systems considered as two-mass or multi-mass systems, and many processes of drive systems can be described as finite stiff coupled two-mass systems (Nian and Deng, 2015; Szabat et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Mechanical parameter identification of two-mass drive system based on variable forgetting factor recursive least squares method

Chen

2018

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

Alternating current (AC) motor drive systems are widely used and their performances are greatly affected by motor parameters and load disturbances, so it is necessary to identify and observe their moment of inertia, the viscous friction coefficient and load torque. This paper presents an identification method combined a Luenberger observer and the variable forgetting factor recursive least squares method (FFRLSM), which does not require the torque sensor but a position encoder and can identify the moment of inertia of the drive system, the viscous friction coefficient, and the load torque, simultaneously. The Luenberger observer takes the position and the actual current to estimate speed and angular position. This information, together with variable FFRLSM is used to identify the moment of inertia, viscous friction coefficient and load torque. The identified moment of inertia of the system is substituted into the Luenberger observer model to correct the error of the system model. According to a probability density function of a normal distribution, the proposed method estimates the identification target in a short time, with stable and high identification precision. Finally, a comparison is made between the proposed algorithm and the FFRLSM. Results show that the proposed identification method achieves intended purpose and promise application values with its short consuming-time and high identification accuracy.

show abstract

“…However, parameters of speed controllers are highly vulnerable to parameter variations and external disturbances, such as load variation, speed change, external unpredictable disturbance, and mechanical parameters of the motor's nonlinear change in PMSM drives. So it is important to identify the motor mechanical parameters and load torque for autotuning parameters of speed controller to run in the better condition [6,7].…”

Section: Introductionmentioning

confidence: 99%

A Novel Online Multivariate Identification for Autotuning Speed Control in PMSM Drives

Chen

et al. 2016

Mathematical Problems in Engineering

View full text Add to dashboard Cite

A novel online algorithm to identify the moment of inertia, viscous friction coefficient, and load torque of PMSM (Permanent Magnet Synchronous Motor) drives and a distinctive autotuning speed control scheme are presented. The proposed identification algorithm does not require motors run in a particular trajectory and only needs a short identification time. A Luenberger speed observer is introduced to eliminate noises which are generated by the detection of position signal and to improve the accuracy of identified parameters. Parameters of the speed controller are optimized by analyzing the mathematical model of the system and the formula of the PI controller. Compared to a standard recursive least squares method (RLSM) and traditional PI algorithm, the effectiveness of the proposed identification algorithm and autotuning speed control scheme are validated through simulations and experiments.

show abstract

An Improved Torque Feed-forward Control with Observer-based Inertia Identification in PMSM Drives

Cited by 6 publications

References 8 publications

Low speed control and implementation of permanent magnet synchronous motor for mechanical elastic energy storage device with simultaneous variations of inertia and torque

Low speed control and implementation of permanent magnet synchronous motor for mechanical elastic energy storage device with simultaneous variations of inertia and torque

Mechanical parameter identification of two-mass drive system based on variable forgetting factor recursive least squares method

A Novel Online Multivariate Identification for Autotuning Speed Control in PMSM Drives

Contact Info

Product

Resources

About