Motor Parameter-Free Predictive Current Control of Synchronous Motors by Recursive Least-Square Self-Commissioning Model

The paper proposes a new speed control method to improve control quality and expand the Permanent Magnet Synchronous Motors speed range. The Permanent Magnet Synchronous Motors (PMSM) speed range enlarging is based on the newly proposed power control principle between two voltage sources instead of winding current control as the conventional Field Oriented Control method. The power management between the inverter and PMSM motor allows the Flux-Weakening obstacle to be overcome entirely, leading to a significant extension of the motor speed to a constant power range. Based on motor power control, a new control method is proposed and allows for efficiently reducing current and torque ripple caused by the imbalance between the power supply of the inverter and the power required through the desired stator current. The proposed method permits for not only an enhanced PMSM speed range, but also a robust stability in PMSM speed control. The simulation results have demonstrated the efficiency and stability of the proposed control method.

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Kernel Recursive Least Square Approach for Power System Harmonic Estimation

Electric Power Components and Systems ◽

10.1080/15325008.2021.1908457 ◽

2021 ◽

pp. 1-16

Author(s):

Omar Avalos ◽

Erik Cuevas ◽

Héctor G. Becerra ◽

Jorge Gálvez ◽

Salvador Hinojosa ◽

...

Keyword(s):

Power System ◽

Least Square ◽

Recursive Least Square ◽

Least Square Approach

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An Effective Joint Soft-Sensing Strategy for Multi-Information under Diverse Vehicle Driving Scenarios

Electronics ◽

10.3390/electronics10040505 ◽

2021 ◽

Vol 10 (4) ◽

pp. 505

Author(s):

Jianfeng Chen ◽

Jiantian Sun ◽

Shulin Hu ◽

Yicai Ye ◽

Haoqian Huang ◽

...

Keyword(s):

Time Delay ◽

Fast Response ◽

Least Square ◽

Accurate Information ◽

Recursive Least Square ◽

Soft Sensing ◽

The Road ◽

Vehicle Mass ◽

Cubature Kalman Filter ◽

Road Slope

A variety of accurate information inputs are of great importance for automotive control. In this paper, a novel joint soft-sensing strategy is proposed to obtain multi-information under diverse vehicle driving scenarios. This strategy is realized by an information interaction including three modules: vehicle state estimation, road slope observer and vehicle mass determination. In the first module, a variational Bayesian-based adaptive cubature Kalman filter is employed to estimate the vehicle states with the time-variant noise interference. Under the assumption of road continuity, a slope prediction model is proposed to reduce the time delay of the road slope observation. Meanwhile, a fast response nonlinear cubic observer is introduced to design the road slope module. On the basis of the vehicle states and road slope information, the vehicle mass is determined by a forgetting-factor recursive least square algorithm. In the experiments, a contrasted strategy is introduced to analyse and evaluate performance. Results declare that the proposed strategy is effective and has the advantages of low time delay, high accuracy and good stability.

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