Intelligent Closed-Loop Fluxgate Current Sensor Using Digital Proportional–Integral–Derivative Control with Single-Neuron Pre-Optimization

This paper presents a microcontroller-controlled closed-loop fluxgate current sensor utilizing digital proportional–integral–derivative (PID) control with a single-neuron-based self-pre-optimization algorithm. The digital PID controller within the microcontroller (MCU) regulates the drive circuit to...

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Main Authors: Qiankun Song, Jigou Liu, Marcelo Lobo Heldwein, Stefan Klaß
Format: Article
Language:English
Published: MDPI AG 2025-03-01
Series:Signals
Subjects:
Online Access:https://www.mdpi.com/2624-6120/6/2/14
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author Qiankun Song
Jigou Liu
Marcelo Lobo Heldwein
Stefan Klaß
author_facet Qiankun Song
Jigou Liu
Marcelo Lobo Heldwein
Stefan Klaß
author_sort Qiankun Song
collection DOAJ
description This paper presents a microcontroller-controlled closed-loop fluxgate current sensor utilizing digital proportional–integral–derivative (PID) control with a single-neuron-based self-pre-optimization algorithm. The digital PID controller within the microcontroller (MCU) regulates the drive circuit to generate a feedback current in the feedback winding based on the zero-flux principle in a closed-loop system. This feedback current is proportional to the measured external current, thereby achieving magnetic compensation. Although PID parameters can be determined using heuristic approaches, empirical formulas, or model-based methods, these techniques are often labor-intensive and time-consuming. To address this challenge, this study implements a single-neuron-based self-pre-optimization algorithm for PID parameters, which autonomously identifies the optimal values for the closed-loop system. Once the PID parameters are optimized, a conventional positional PID algorithm is employed for the closed-loop control of the fluxgate current sensor. The experimental results show that the developed digital closed-loop fluxgate sensor has a non-linearity within 0.1% at the full scale in the measuring ranges of 0–1 A and 0–10 A DC current, with an effective response time of approximately 120 ms. The limitation of the sensors’ response time is found to be ascribed to its open-loop measuring circuit.
format Article
id doaj-art-1672c36b0c4f44d6aa66a1aa36b741a6
institution Kabale University
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publishDate 2025-03-01
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series Signals
spelling doaj-art-1672c36b0c4f44d6aa66a1aa36b741a62025-08-20T03:27:43ZengMDPI AGSignals2624-61202025-03-01621410.3390/signals6020014Intelligent Closed-Loop Fluxgate Current Sensor Using Digital Proportional–Integral–Derivative Control with Single-Neuron Pre-OptimizationQiankun Song0Jigou Liu1Marcelo Lobo Heldwein2Stefan Klaß3ChenYang Technologies GmbH & Co. KG, Markt Schwabener Str. 8, 85464 Finsing, GermanyChenYang Technologies GmbH & Co. KG, Markt Schwabener Str. 8, 85464 Finsing, GermanyChair of High-Performance Inverter Systems, Technical University of Munich, Arcisstr. 21, 80333 Munich, GermanyChair of High-Performance Inverter Systems, Technical University of Munich, Arcisstr. 21, 80333 Munich, GermanyThis paper presents a microcontroller-controlled closed-loop fluxgate current sensor utilizing digital proportional–integral–derivative (PID) control with a single-neuron-based self-pre-optimization algorithm. The digital PID controller within the microcontroller (MCU) regulates the drive circuit to generate a feedback current in the feedback winding based on the zero-flux principle in a closed-loop system. This feedback current is proportional to the measured external current, thereby achieving magnetic compensation. Although PID parameters can be determined using heuristic approaches, empirical formulas, or model-based methods, these techniques are often labor-intensive and time-consuming. To address this challenge, this study implements a single-neuron-based self-pre-optimization algorithm for PID parameters, which autonomously identifies the optimal values for the closed-loop system. Once the PID parameters are optimized, a conventional positional PID algorithm is employed for the closed-loop control of the fluxgate current sensor. The experimental results show that the developed digital closed-loop fluxgate sensor has a non-linearity within 0.1% at the full scale in the measuring ranges of 0–1 A and 0–10 A DC current, with an effective response time of approximately 120 ms. The limitation of the sensors’ response time is found to be ascribed to its open-loop measuring circuit.https://www.mdpi.com/2624-6120/6/2/14closed loopdigital proportional–integral–derivativefluxgate current sensormicrocontrollerpositional PID algorithmsingle-neuron-based self-pre-optimization algorithm
spellingShingle Qiankun Song
Jigou Liu
Marcelo Lobo Heldwein
Stefan Klaß
Intelligent Closed-Loop Fluxgate Current Sensor Using Digital Proportional–Integral–Derivative Control with Single-Neuron Pre-Optimization
Signals
closed loop
digital proportional–integral–derivative
fluxgate current sensor
microcontroller
positional PID algorithm
single-neuron-based self-pre-optimization algorithm
title Intelligent Closed-Loop Fluxgate Current Sensor Using Digital Proportional–Integral–Derivative Control with Single-Neuron Pre-Optimization
title_full Intelligent Closed-Loop Fluxgate Current Sensor Using Digital Proportional–Integral–Derivative Control with Single-Neuron Pre-Optimization
title_fullStr Intelligent Closed-Loop Fluxgate Current Sensor Using Digital Proportional–Integral–Derivative Control with Single-Neuron Pre-Optimization
title_full_unstemmed Intelligent Closed-Loop Fluxgate Current Sensor Using Digital Proportional–Integral–Derivative Control with Single-Neuron Pre-Optimization
title_short Intelligent Closed-Loop Fluxgate Current Sensor Using Digital Proportional–Integral–Derivative Control with Single-Neuron Pre-Optimization
title_sort intelligent closed loop fluxgate current sensor using digital proportional integral derivative control with single neuron pre optimization
topic closed loop
digital proportional–integral–derivative
fluxgate current sensor
microcontroller
positional PID algorithm
single-neuron-based self-pre-optimization algorithm
url https://www.mdpi.com/2624-6120/6/2/14
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AT jigouliu intelligentclosedloopfluxgatecurrentsensorusingdigitalproportionalintegralderivativecontrolwithsingleneuronpreoptimization
AT marceloloboheldwein intelligentclosedloopfluxgatecurrentsensorusingdigitalproportionalintegralderivativecontrolwithsingleneuronpreoptimization
AT stefanklaß intelligentclosedloopfluxgatecurrentsensorusingdigitalproportionalintegralderivativecontrolwithsingleneuronpreoptimization