A Nonlinear Reduced Order Observer for Rotor Position Estimation of Sensorless Permanent Magnet Brushless DC Motor Drive

Kumar, Rajesh; Singh, Nirbhow Jap; Singh, Jaswant; Padmanaban, Santhosh Vijaya

doi:10.1109/iecon.2006.347600

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…In these works they obtain a high power factor and minimum current distortions associated with static and dynamic loads [6][7][8]. In addition, several predictive and adaptive control algorithms have been proposed to improve the power conversion in this type of system [2,[9][10][11][12][13][14]. Also, recently a buck-boost AC-DC converter has been used to obtain adjustable DC link voltages for the control scheme of a single-phase motor speed control [15].…”

Section: Introductionmentioning

confidence: 99%

A comparison between the algebraic and the reduced order observer approaches for on‐line load torque estimation in a unit power factor rectifier‐DC motor system

Linares-Flores

Sira‐Ramírez

Mendoza

et al. 2012

Asian Journal of Control

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In this article, a comparison between the performances of an online algebraic estimator and a reduced order observer is established in the context of adaptive load torque estimation in a controlled unit power factor rectifier-DC motor system combination. A passivity-based output feedback controller was constructed to regulate the angular speed while correcting the power factor of the mono phase boost rectifier. The proposed controller allowed us to compare the results of both on-line estimation techniques. The experimental results show a superiority of the algebraic approach in which the angular velocity was maintained at the desired reference value by the adapted controller, even under severe load changes on the motor shaft, while the power factor was also kept at high values in the power supply system.

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Section: Introductionmentioning

confidence: 99%

A comparison between the algebraic and the reduced order observer approaches for on‐line load torque estimation in a unit power factor rectifier‐DC motor system

Linares-Flores

Sira‐Ramírez

Mendoza

et al. 2012

Asian Journal of Control

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“…Fig. 7 Schematic of sensorless control PMBLDC drive Kumar et al (2006b) presents a sensorless scheme for rotor position estimation of permanent magnet brushless dc motor using Artificial Neural Networks with the formulation and comprehensive analysis of vector control PMBLDC drive. Kumar and Radmanaban (2006) also presented a sensorless scheme for rotor position estimation of permanent magnet brushless dc motor using Artificial Neural Networks with the formulation and comprehensive analysis of vector control PMBLDC drive.…”

Section: Sensorless Drivementioning

confidence: 99%

Artificial intelligence applications in Permanent Magnet Brushless DC motor drives

2009

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Permanent Magnet Brushless DC (PMBLDC) machines are more popular due its simple structure and low cost. Improvements in permanent magnetic materials and power electronic devices have resulted in reliable, cost effective PMBLDC drives, for many applications. Advances in artificial intelligent applications like neural network, fuzzy logic, Genetic algorithm etc. have made tremendous impact on electric motor drives. The brushless DC motor is a multivariable and non-linear system. In conventional PMBLDC drives speed and position sensing of brushless DC motors require high degree of accuracy. Unfortunately, traditional methods of control require detailed modelling of all the motor parameters to achieve this. The Intelligent control techniques like, fuzzy logic control/Neural network control etc. uses heuristic input-output relations to deal with vague and complex situations. This paper presents a literature survey on the intelligent control techniques for PMBLDC motor drives. Various AI techniques for PMBLDC motor drives are described. Attempt is made to provide a guideline and quick reference for the researchers and practicing engineers those are working in the area of PMBLDC motor drives.

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“…And the ideal commutation point is 30 electrical degree shift the zero-crossing point (ZCP) of the non-excited phase back-EMF. There are many methods based on this idea [7], including detecting back-EMF directly, back-EMF integration, building back-EMF observers like Kalman observer [8] or sliding-mode observer [9] and so on [10,11]. However, most of these methods work well only in a narrow range of speed as the amplitude of Back-EMF is related to speed.…”

Section: Introductionmentioning

confidence: 99%

Sensorless Control of Brushless DC Motors based on TMS320F2812

Zhang¹,

Wang²

2014

Journal of international Conference on Electrical Machines and

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-A new sensorless control strategy for brushless DC( BLDC) motors is proposed in this paper. This technique builds a rotor position-related flux function, which is independent of the speed. By observing the flux function, the information of rotor position and commutation can be obtained. As the function is not speed-related, this control process can be used in a wider range of speed and improves the motor's performance during startup. An experimental test bad based on TMS320F2812 has been built, and the experimental result indicates that the motor achieves a smooth starting-up and stable phase commutation with the proposed control strategy.

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A Nonlinear Reduced Order Observer for Rotor Position Estimation of Sensorless Permanent Magnet Brushless DC Motor Drive

Cited by 7 publications

References 11 publications

A comparison between the algebraic and the reduced order observer approaches for on‐line load torque estimation in a unit power factor rectifier‐DC motor system

A comparison between the algebraic and the reduced order observer approaches for on‐line load torque estimation in a unit power factor rectifier‐DC motor system

Artificial intelligence applications in Permanent Magnet Brushless DC motor drives

Sensorless Control of Brushless DC Motors based on TMS320F2812

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