A tunable magnet reluctance actuator is an actuator that combines a hybrid reluctance actuator with a tunable permanent magnet as an alternative source of flux. Semi-static forces can be actuated by changing the magnetization of the magnet, after which a constant force can be generated without dissipating energy. In high-tech applications, this reduces heat dissipation, thereby reducing thermal expansion and increasing precision.

Tuning a magnet requires energy upfront, after which the power output of the combined reluctance actuator may be decreased. Next to semi-static situations where a constant force output is demanded, this may also be beneficial in dynamic applications.

This paper investigates optimal control of such an actuator, identifying the limits where a reduction in heat dissipation is possible. The main focus of this paper is on energy efficiency and dynamic effects while tuning the magnet.
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Lorentz and reluctance actuators are widely applied in high-precision motion systems as they provide non-contact forces without backlash, thereby enabling nanometer-scale positioning. However, sustaining gravitational offset forces requires a continuous current supply, causing heat losses and consequently thermal positioning drift. Hybrid tunable magnet actuators (HTMAs) provide adjustable offset forces without continuous power consumption. However, existing designs are single-DoF, short-stroke (0.5 - 1 mm), and require flexures to compensate for parasitic forces. This work presents an HTMA for gravity compensation in a 6-DoF levitated stage with an increased 4 mm XYZ-stroke and continuous Z-rotation. A force tuning range of -7.39 N to 7.41 N is experimentally demonstrated at the central mover position. In addition, the actuator exhibits relatively low stiffness variation and limited parasitic forces and torques over the motion range. For efficient tunable gravity compensation, the HTMA could operate in parallel with a passive magnetic system that provides the nominal force and counteracts the HTMA stiffness ... Read more

With the increasing demand for precision and accuracy in the high-tech industry, limiting thermal dissipation has become a critical challenge. In high-precision positioning systems, heat dissipation causes thermal expansion of components, which degrades positioning accuracy and ultimately compromises overall performance. These shortcomings led to the development of a Tunable Magnet Actuator(TMA). TMA can sustain the required force without a continuous current supply by utilizing the tunable magnets, which can be magnetized along different states. To minimize thermal dissipation, accurate control of the magnet’s magnetization state is essential. Existing magnetization state tuning methods, however, are subpar, falling short in efficiency, accuracy, or both simultaneously. Moreover, many of the existing state-of-the-art control algorithms utilize the look-up table, resulting in a feedforward control. To generate these look-up tables, a comprehensive data collection is required, as well as the calibration of the obtained results, which is undesirable. To mitigate this, a feedback control strategy similar to the traditional Pulse Width Modulation algorithm has been developed. While accurate, the PWM control remains highly inefficient. The aforementioned necessitates the development of a new feedback control algorithm that would be as accurate but more efficient. The neural networks used for the prediction of the hysteresis loops have been largely explored in the past years. However, the implementation of the neural network in tandem with a real-time control remains novel. This thesis proposes a new neural network-based PWM control algorithm to address the challenges in accurately controlling the remanent state of the tunable magnet. Initially, a system characterization is done to train the neural network model. Afterwards, a new control algorithm that utilizes the developed neural network models is created and implemented. ... Read more