Design and control of compact mechatronic systems based on magnetic shape memory alloys
Kulagin, Ivan (2026-08-14)
Väitöskirja
14.08.2026
Lappeenranta-Lahti University of Technology LUT
Acta Universitatis Lappeenrantaensis
School of Energy Systems
School of Energy Systems, Konetekniikka
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In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of Lappeenranta-Lahti University of Technology LUT's products or services. Internal or personal use of this material is permitted. If interested in reprinting/republishing IEEE copyrighted material for advertising or promotional purposes or for creating new collective works for resale or redistribution, please go to http://www.ieee.org/publications_ standards/publications/rights/rights_link.html to learn how to obtain a License from RightsLink.
Julkaisun pysyvä osoite on
https://urn.fi/URN:ISBN:978-952-412-497-3
https://urn.fi/URN:ISBN:978-952-412-497-3
Kuvaus
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Tiivistelmä
Magnetic shape memory (MSM) alloys offer unique actuation capabilities, combining large reversible strain, high power density and a fast response enabled by magnetically induced motion. These properties make MSM-based actuators attractive for compact and high-performance mechatronic systems. However, practical deployment remains limited due to intrinsic material hysteresis, restricted stroke, and sensitivity to operating conditions. Moreover, most existing MSM actuators operate at relatively low frequencies and employ minimal or no mechanical transmission, leaving the potential of more complex actuator architectures and advanced control strategies largely unexplored. This dissertation presents a novel strain wave gearing MSM (SWG-MSM) actuator concept that addresses these limitations by combining multiple MSM elements with strain wave gearing mechanism.
A comprehensive model of the SWG-MSM actuator is developed, capturing the mechanical interactions and MSM dynamics governing actuator behaviour. The model is used to analyse actuator performance, identify key design parameters, and derive force-velocity characteristics. A laboratory prototype is designed and experimentally characterised, including measurements of the magnetic circuit, rack displacement, velocity, and output force. The experimental results confirm the technical feasibility of the proposed concept and show good agreement with the simulation. The observed deviations are primarily attributed to non-stabilised MSM elements, mechanical backlash, and simplifying assumptions in the MSM material model.
Beyond open-loop characterization, this work presents the first investigation of closed-loop control for SWG-MSM actuators. Classical PID control and several reinforcement learning–based controllers are evaluated in simulation for velocity and position control under varying external loads and internal disturbances. PID control demonstrates reliable and accurate tracking, making it well suited for initial practical implementation. Reinforcement learning-based controllers, particularly the proximal policy optimization (PPO), achieve superior performance and robustness to non-linearities and discrete actuation effects, highlighting their potential for advanced control of SWG-MSM actuators.
A comprehensive model of the SWG-MSM actuator is developed, capturing the mechanical interactions and MSM dynamics governing actuator behaviour. The model is used to analyse actuator performance, identify key design parameters, and derive force-velocity characteristics. A laboratory prototype is designed and experimentally characterised, including measurements of the magnetic circuit, rack displacement, velocity, and output force. The experimental results confirm the technical feasibility of the proposed concept and show good agreement with the simulation. The observed deviations are primarily attributed to non-stabilised MSM elements, mechanical backlash, and simplifying assumptions in the MSM material model.
Beyond open-loop characterization, this work presents the first investigation of closed-loop control for SWG-MSM actuators. Classical PID control and several reinforcement learning–based controllers are evaluated in simulation for velocity and position control under varying external loads and internal disturbances. PID control demonstrates reliable and accurate tracking, making it well suited for initial practical implementation. Reinforcement learning-based controllers, particularly the proximal policy optimization (PPO), achieve superior performance and robustness to non-linearities and discrete actuation effects, highlighting their potential for advanced control of SWG-MSM actuators.
Kokoelmat
- Väitöskirjat [1215]