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.

The spherical joint is an effective solution to design parallel micro-robotic systems with rotation capabilities in the three-dimensional space. This type of joint has however some non-linear characteristics, such as the clearance, which affect the positioning accuracy in micro-robotic tasks. The starting point of this study lies in experimental observations of rotation errors from a 3-PPPS 6-DOF parallel micro-robotic systems operating inside a scanning electron microscope. The objective of the paper is to assess the role of the spherical joints in the rotation errors and to evaluate whether the joints non-linearities can cause errors with the same order of magnitude as those observed experimentally. To this end, the first part of the study addresses the modeling of 3-PPPS 6-DOF parallel micro-robotic systems with spherical joints including the clearance. This model allows for analysing the effect of the clearance on position and rotation accuracies of the micro-robotic system. It is found by simulations that the same positioning behavior as in the experiments occurs when the clearance of the spherical joint is included in the model, supporting the hypothesis. Therefore, it is concluded that clearance in spherical joints has a significant effect on the precision of parallel type micro-robotic systems which opens new challenges in the control of poly-articulated micro-robotic systems with clearance compensation.