[Paper Review] Tunable Magnets: modeling and validation for dynamic and precision applications

Silvan Viëtor, Spronck, Jo W.+1 more|arXiv (Cornell University)|Sep 30, 2022

Electric Motor Design and Analysis4 citations

TL;DR

This paper proposes a tunable magnet system using AlNiCo with in-situ magnetization control to eliminate actuator self-heating in quasi-static applications. By combining a magnetic circuit model, measured BH curves, and air-gap flux feedback, the method achieves precise tuning with a maximum error of 15.86 mT and minimum precision of 0.67 mT across a 200 µm air-gap range, enabling near-zero static power dissipation.

ABSTRACT

Actuator self-heating limits the achievable force and can cause unwanted structural deformations. This is especially apparent in quasi-static actuation systems that require the actuator to maintain a stable position over an extended period. As a solution, we use the concept of a Tunable Magnet. Tunable magnets rely on in-situ magnetization state tuning of AlNico to create an infinitely adjustable magnetic flux. They consist of an AlNiCo low coercivity permanent magnet together with a magnetizing coil. After tuning, the AlNiCo retains its magnetic field without further energy input, which eliminates the static heat dissipation. To enable implementation in actuation systems, the AlNiCo needs to be robustly tunable in the presence of a varying system air-gap. We achieve this by implementing a magnetization state tuning method, based on a magnetic circuit model of the actuator, measured AlNiCo BH data and air-gap flux feedback control. The proposed tuning method consists of 2 main steps. The prediction step, during which the required magnet operating point is determined, and the demagnetization step, where a feedback controller drives a demagnetization current to approach this operating point. With this method implemented for an AlNiCo 5 tunable magnet in a reluctance actuator configuration, we achieve tuning with a maximum error of 15.86 "mT" and a minimum precision of 0.67 "mT" over an air-gap range of 200 "μm". With this tuning accuracy, actuator heating during static periods is almost eliminated. Only a small bias current is needed to compensate for the tuning error.

Motivation & Objective

• Address actuator self-heating due to continuous current in quasi-static systems that maintain position for long durations.
• Overcome limitations of conventional permanent magnets by enabling dynamic, energy-free magnetic flux tuning.
• Ensure robust tunability despite varying air-gap conditions in real-world actuator environments.
• Minimize residual error in magnetic flux control to reduce bias current and eliminate static power dissipation.
• Develop a feedback-based tuning method that is both accurate and practical for integration into reluctance actuator systems.

Proposed method

• Develop a magnetic circuit model of the actuator to predict the required magnet operating point for desired flux output.
• Utilize experimentally measured AlNiCo BH hysteresis data to define the magnetization state space and target operating points.
• Implement a two-step tuning process: first, predict the required current profile using the magnetic model and BH data.
• Second, apply a feedback controller that drives a demagnetization current to converge toward the predicted operating point.
• Incorporate real-time air-gap flux feedback to adapt the control input and compensate for mechanical and material variations.
• Use a low-power bias current to correct residual tuning errors, ensuring stable flux maintenance without continuous high current.

Experimental results

Research questions

• RQ1 Can in-situ magnetization tuning of AlNiCo eliminate static power dissipation in quasi-static actuators?
• RQ2 How accurately can magnetic flux be controlled across a variable air-gap using feedback and magnetic modeling?
• RQ3 What is the achievable tuning accuracy and robustness of the proposed method under real-world mechanical variations?
• RQ4 To what extent does the feedback controller mitigate errors caused by air-gap changes and material hysteresis?
• RQ5 Can the system maintain precise flux levels with minimal bias current after initial tuning?

Key findings

• The proposed tuning method achieves a maximum tuning error of 15.86 mT across a 200 µm air-gap range, demonstrating high control accuracy.
• The minimum precision of the system is 0.67 mT, indicating fine control resolution suitable for precision applications.
• Actuator self-heating during static operation is almost entirely eliminated, as the magnet retains its field without continuous current.
• Only a small bias current is required to compensate for residual tuning errors, significantly reducing energy consumption.
• The feedback-based approach effectively maintains target flux levels despite variations in air-gap and mechanical tolerances.
• The integration of measured AlNiCo BH data with a magnetic circuit model enables reliable prediction and control of magnetization states.