Multi-Optimization Objective Online Tracking-Based Parameter Self-Tuning Method for Sensorless PMSM Drives

Bi, Guangdong; Wang, Qiwei; Ding, Dawei; Li, Binxing; Zhang, Guoqiang; Wang, Gaolin; Xu, Dianguo

doi:10.1109/tte.2022.3200368

Cited by 7 publications

(3 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ensuring the accuracy of motor parameters is an indispensable prerequisite for achieving high performance control of PM-SWG. However, in actual working conditions, the electrical parameters [5][6][7][8] of PMSWG are easily affected by magnetic saturation, external temperature, and other factors. In order to realize efficient motor performance, it is increasingly important that motor parameters are accurately recognized.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Learning Co-evolutionary Variational Particle Swarm Optimization Algorithm for Parameter Identification of PMSWG

Zhang,

Zhou,

Luo

et al. 2024

PIER C

View full text Add to dashboard Cite

Targeting the problems of traditional particle swarm algorithm easily falling into local optimum and low recognition accuracy, an adaptive learning co-evolutionary variational particle swarm optimization algorithm (ALCEVPSO) is proposed in this paper to identify the parameters of permanent magnet synchronous wind generator (PMSWG). At first, an adaptive learning strategy is adopted for the inertia weights of the PSO, and the global optimization seeking ability of the PSO is improved. After that, multiple swarm co-evolution strategies are introduced to share the best positions within sub-populations, and by this method, the algorithm's falling into local optimality is avoided. Finally, Cauchy Gaussian mixed variants are introduced, and the population diversity is enriched. The proposed method has the advantages of strong optimization ability and high search accuracy compared with the traditional particle swarm algorithm, which is shown by simulated and experimental results. By this method, the motor parameters of the permanent magnet synchronous motor can be accurately identified.

show abstract

Section: Introductionmentioning

confidence: 99%

An Adaptive Learning Co-evolutionary Variational Particle Swarm Optimization Algorithm for Parameter Identification of PMSWG

Zhang,

Zhou,

Luo

et al. 2024

PIER C

View full text Add to dashboard Cite

show abstract

“…In conventional PMSMs, sensors are used to identify the rotor position and speed for precise control. However, sensorless PMSMs utilize motor controllers and algorithms to infer the rotor position and speed, thus not requiring sensors [2]. Despite the numerous benefits of sensorless velocity control, its practical application poses challenges.…”

Section: Introductionmentioning

confidence: 99%

Sensorless Speed Control for SPMSM Using a Nonlinear Observer and Enhanced Super-Twisting ADRC

Ahn

Chung

et al. 2023

Mathematics

View full text Add to dashboard Cite

In this article, a novel strategy called enhanced super-twisting active disturbance rejection control (ESTADRC), as well as a nonlinear observer (NOB), is used to implement a speed control scheme for permanent-magnet synchronous motors with intricate internal dynamics, and it exhibits nonlinearity and variable parameters. A new reaching law is formulated within a super-twisting sliding mode control (STSMC) framework, and a comprehensive procedure for finite convergence time analysis is provided. The convergence region of the state variables of the system is obtained using a Lyapunov function. ESTADRC is developed by integrating STSMC and linear active disturbance rejection control (LADRC), whereas the NOB is employed to estimate the motor’s position or angle value. Simulations demonstrated that the proposed approach is valid and effective compared with super-twisting active disturbance rejection control and LADRC.

show abstract

“…PMSM has the advantages of high efficiency, high power density, and high reliability and has been widely used in industrial fields [1][2][3]. To meet the different control requirements of different applications on the performance of the transmission system, control strategies with different advantages have been proposed.…”

Section: Introductionmentioning

confidence: 99%

Hybrid-vector Model Predictive Flux Control for PMSM Considering Narrow Pulse

Xiao,

Yu,

Zhang

et al. 2023

PIER C

View full text Add to dashboard Cite

Multi-vector model predictive control (MPC) of permanent magnet synchronous motors (PMSM) has two issues: selecting the optimal voltage vector (VV) combination is very complicated, and multiple prediction calculations to minimize the cost function result in a heavy computational burden; applying a VV with a short duration may generate narrow pulses, while the effect of reducing torque ripples and stator current harmonics is not obvious. The hybrid-vector model prediction flux control (HV-MPFC) strategy considering narrow pulse suppression is proposed in this paper. First, the optimal VV combination is quickly identified by the sector where the stator flux error vector is located, which lowers the control complexity and computational burden. Secondly, by the relationship between the action time of three VVs and the set time threshold, the hybrid-vector strategy to switch among three VVs, two VVs, and a single VV is employed to prevent the generation of narrow pulses. Finally, experimental results show that, compared with the existing three-vector MPC strategy, the HV-MPFC strategy effectively suppresses the generation of narrow pulses and achieves smaller torque ripples and stator current harmonics at the same switching frequency.

show abstract

Multi-Optimization Objective Online Tracking-Based Parameter Self-Tuning Method for Sensorless PMSM Drives

Cited by 7 publications

References 31 publications

An Adaptive Learning Co-evolutionary Variational Particle Swarm Optimization Algorithm for Parameter Identification of PMSWG

An Adaptive Learning Co-evolutionary Variational Particle Swarm Optimization Algorithm for Parameter Identification of PMSWG

Sensorless Speed Control for SPMSM Using a Nonlinear Observer and Enhanced Super-Twisting ADRC

Hybrid-vector Model Predictive Flux Control for PMSM Considering Narrow Pulse

Contact Info

Product

Resources

About