This study examines a novel setup of a micropositioning trajectory manipulator in$X \theta$, energized by a reluctance actuator (RA) and two accompanying moving magnet actuators (MMA). The design is characterized by a C -core RA, which features asymmetrical air gaps between the mover and the stator elements when under angular$\theta$rotation. When the stator coil is energized, a magnetic flux induces a force in the mover. Two MMAs can add force and torque dynamics to the system via solenoid and permanent magnet (PM) pairs to offer additional corrective actions. Facilitating control of a translational ($x$) and rotational ($\theta$) two-degree-of-freedom (2DOF) actuation system. Flexure hinges aid in the retraction force of the mover element and provide needed stiffness to the system without frictional effects. This was modeled analytically and optimized to achieve outlined performance objectives. The system was validated experimentally through triangle, and sinusoidal trajectories in open loop control. The most relevant application is scanning mirror systems where specific targeted rotational and translational trajectories can benefit light beam positioning. This system allows both translation and rotation specifications of a selected trajectory to be realized in one actuation unit, opening up more design possibilities for controlling precision positioning systems.

The reliability of precision motion systems, such as semiconductor wafer scanners, is often influenced by nonlinear dynamics originating from components such as cable slabs. This paper introduces a data-driven framework for early fault diagnosis in these systems. Koopman operator theory is employed to derive a linear state-space model from experimental data, capturing the complex, hysteretic behavior of the cable slab. This model serves as a digital twin, and by comparing its predictions with real-time sensor measurements, operational anomalies can be detected. A systematic process for selecting observable functions yields a high-fidelity model with a tracking error of approximately ±1% across the operational range. When the proposed approach is tested against a state-of-the-art neural network model, it demonstrates a 75.4% reduction in reaction force prediction error. The framework successfully identifies an injected sensor noise fault (SNR of 20) in just 0.35 s using only force data, validating its potential to improve wafer scanner reliability.

This study investigates a novel Xθ actuation system driven by a reluctance actuator (RA) and two accompanying moving magnet actuators (MMAs). The system enables precise control of both translational (x) and rotational (θ) motion, offering a two-degree-of-freedom (2DOF) solution for high-precision applications. The two MMAs introduce additional force and torque dynamics through the solenoid and permanent magnet (PM) pairs. Flexure hinges assist with the retraction force of the mover element, providing the necessary stiffness without introducing frictional effects. The system was modeled analytically, optimized, and validated experimentally with a developed feedback and feedback control, achieving steady-state errors of approximately ±7 µm in x translation and ±0.3 mrad in θ rotation which can be attributed to systematic errors in the sensor itself. The most relevant application is the fastscan mirror in extreme ultraviolet (EUV) lithography where specific targeted rotational and translational trajectories can benefit light beam positioning, such as wavefront corrections. This system allows translation and rotation specifications to be realized in one actuation unit, opening up more design possibilities for controlling precision motion systems.

A novel method for analyzing the dynamics and bend geometry of a cable slab via trained neural networks is introduced. Neural networks are trained from real-time visual feedback capture via a high-speed camera during cyclic motion to track the positions of multiple markers affixed to the cable slab through image processing techniques. Experimental parameters are systematically varied to ensure a diverse range of training patterns. Consequently, two distinct data-driven neural network models are developed: a coupled model and a decoupled model. These models accurately predict the two-dimensional positions of the markers, even during non-cyclic motion profiles. Subsequently, the marker positions are utilized as waypoints to generate a cubic spline curve with time-varying coefficients, approximating the spatiotemporal solution of the cable slab dynamics. Notably, this spline can be segmented into smaller sections tailored to specific research objectives. Experimental results validate the effectiveness of the proposed methodology.

This paper introduces a position control system for a motion stage driven by a low-energy C-core reluctance actuator. The central concept explored here is the utilization of a variable air gap to enable energy-efficient operation of the motion stage. First, we show the design and mathematical model of the reluctance-actuated motion system (RAMS). Then, by analyzing open-loop responses of the RAMS under various conditions including variable air gaps and different excitation voltages, we show that using variable air gap can reduce the required current. Finally, the paper formulates a control approach that combines a feedforward controller to linearize the RAMS’s dynamic behavior and a state feedback controller to achieve tracking performance. Experimental results demonstrate the effectiveness of this control approach in achieving tracking objectives with errors that are less than 2% for constant desired displacement and less than 10% for tracking a sinusoidal reference signal.

This paper presents a design and characterization of a micropositioning stage driven by a reluctance actuator. The stage is constructed with a C-core reluctance actuator and four compression springs. The design of the stage is presented using a CAD model, followed by the fabrication process of the prototype. The mathematical model is formulated to present the interaction among the stage's electrical, magnetic, and mechanical dynamic behaviour. Next, the force-current and force-gap characteristics are obtained by measuring the force under different applied currents and air gaps. After that, the system is analyzed to determine the maximum applied voltage that stabilizes the system in an open-loop configuration, followed by the time-domain and frequency-domain response. Finally, the feedforward controller is presented to linearize the dynamic behavior of the stage over a specific range of motion. The experimental results under the feedforward controller show a linear characteristic between the desired force and the output displacement.