Confidence Weighted Learning Entropy (CWLE)

Citation Author(s):
Zdenek
Novak
Submitted by:
Zdenek Novak
Last updated:
Tue, 06/13/2023 - 07:12
DOI:
10.21227/pcem-zx93
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Abstract 

There are several non-idealities that can degrade magnetic Hall-effect sensors performance and impact related applications. Thus, a confidence weighted learning entropy (CWLE) is proposed as a fault-tolerant control strategy for field-oriented control (FOC) of permanent magnet synchronous machines (PMSM). It combines sensorless and sensor-based control, while capitalizing on their major advantages, such as operation from standstill and at lower speeds, fast dynamic response, and fault tolerance to encoder errors. Encoder fault detection is based on learning entropy, that monitors weights increments of two predictive filters of angular displacement. If the two observed systems behave similarly, the variance of the weights increments is similar. A higher variance, on the other hand, reflects unforeseen misbehavior of a particular system, leading to a decrease in its confidence. A voting mechanism based on confidence weighted average then decides which of the two systems should be used for FOC of PMSM. The method has been experimentally verified on a high-speed PMSM, achieving more reliable control performance with fast response to encoder failures.

Instructions: 

These are the supplementary files of submission for IEEE Transactions on Industrial Electronics. It includes Matlab code and datasets for the proposed "Confidence Weighted Learning Entropy for Fault-Tolerant Control of PMSM with High-Resolution Hall Encoder". The code is accompanied with interactive settings. Additionally, measured datasets are included to allow the Confidence Weighted Learning Entropy (CWLE) script to be tested on real data. The output of the script are Matlab plots that demonstrate the computational results of the proposed algorithm when applied to a specific selected dataset.

Please, unpack the contents of the file "CWLE_Code_Dataset.zip" into a single directory. The main code file is "CWLE_algorithm.m". It can be used to read all datasets that are stored as binary files. Detailed instructions how to use the script can be found in the attached file "Readme.pdf".

Any feedback related to the code, data or the CWLE is always welcomed.

Funding Agency: 
Technology Agency of the Czech Republic
Grant Number: 
CK03000028