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.

This paper introduces a control approach for a motion system driven by a class of actuators with multiple nonlinearities. The proposed approach presents a combination of a feedforward controller and an output feedback controller to achieve a tracking performance of the motion system. The feedforward controller is mainly proposed to address the actuator dynamics and provide a linearization without requiring measurements from the actuator. Subsequently, the output feedback controller is designed using the measured position to achieve a tracking objective for a desired reference signal, considering the unknown nonlinearities in the system and the error due to the open-loop compensation using feedforward control. The efficacy of the proposed control approach is validated through three applications: reluctance actuator, electrostatic microactuator, and magnetic levitation system. Both simulation and experimental results demonstrate the effectiveness of the proposed control approach in achieving the desired reference signal with minimal tracking error, considering that the actuator and system nonlinearities are unknown. Note to Practitioners—In precision-driven motion applications, the control of the motion system plays a pivotal role in attaining the desired motion profile with exceptional accuracy. Recently, modern actuators have garnered attention from industries and academia as they aim to develop the next generation of motion systems for various advanced applications. For instance, reluctance actuators are designed to drive the wafer scanner in lithography machines, and electrostatic actuators are used to drive the mirror optic systems in smartphones. However, the multiple nonlinearities and position dependency inherent in such actuators, where the mover of the actuator is part of the motion system, introduce unstable behavior, limit performance, and pose challenges for controllers. This paper presents a control approach combining feedforward and output feedback control based on the extended high-gain observer (EHGO). The proposed controller offers several advantages, including enhanced performance of motion systems driven by such actuators and increased robustness by estimating unknown nonlinearities or external disturbances. This results in more accurate and reliable motion profiles, particularly in precision applications. Moreover, the proposed control approach is easy to implement since it does not require adaptation, tuning, or training algorithms and involves fewer controller and observer parameters to design.

Reproductive medicine has recently witnessed significant advancements, particularly in vitro fertilization (IVF). One crucial aspect of IVF involves the extraction of cellular material and its analysis to maximize the chance of successful implantation. This work highlights the development and application of the automated system for Trophectoderm cell (TE) extraction and separation, addressing the need for precision, efficiency, and reduced manual intervention. The presented automated system is equipped with a computer vision algorithm, microliter pump, vacuum system, and micromanipulation tools to consistently and accurately biopsy TE cells. An experimental setup is developed to verify the behavior of the proposed method, in which a holding micropipette is connected to a vacuum system and holds the embryo stationary. Three steps are performed to complete the process and are controlled by a computer vision algorithm. The coordinates of the Zona Pellucida (ZP) perforation (perforated in a previous step) are used as a feedback signal to a simple proportional controller to control the biopsy pipette motion. The computer vision monitors the amount of TE cells aspirated inside the biopsy pipette and controls the microliter pump. The aspirated TE cells were separated away using a laser cutting system. Experimental results demonstrate that the system can relocate the biopsy pipette, TE cell extraction, and separation.

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.

Cooperative adaptive cruise control (CACC) is a smart transportation solution to alleviate traffic congestion and enhance road safety. The performance of CACC systems can be remarkably affected by communication time delays, and traditional control methods often compromise control performance by adjusting control gains to maintain system stability. In this article, we present a study on the stability of a CACC system in the presence of time delays and highlight the tradeoff between control performance and tuning controller gains to address increasing delays. We propose a novel approach incorporating a neural network module called the deep time delay filter (DTDF) to overcome this limitation. The DTDF leverages the assumption that time delays primarily originate from the communication layer of the CACC network, which can be subject to adversarial delays of varying magnitudes. By considering time-delayed versions of the car states and predicting the present (un-delayed) states, the DTDF compensates for the effects of communication delays. The proposed approach combines classical control techniques with machine learning, offering a hybrid control system that excels in explainability and robustness to unknown parameters. We conduct comprehensive experiments using various deep learning architectures to train and evaluate the DTDF models. Our experiments utilize a robot platform consisting of MATLAB, Simulink, the Optitrack motion capture system, and the Qbot2e robots. Through these experiments, we demonstrate that when appropriately trained, our system can effectively mitigate the adverse effects of constant time delays and outperforms a traditional CACC baseline in control performance. This experimental comparison, to the best of the authors’ knowledge, is the first of its kind in the context of a hybrid machine learning CACC system. We thoroughly explore initial conditions and range policy parameters to evaluate our system under various experimental scenarios. By providing detailed insights and experimental results, we aim to contribute to the advancement of CACC research and highlight the potential of hybrid machine learning approaches in improving the performance and reliability of CACC systems.

This paper presents a robust output feedback controller for a n-link serial robotic manipulator with unknown dynamics and external disturbances. First, the robotic manipulator's model is formulated with unknown dynamics, including joint coupling, nonlinearities, and external disturbances. Second, an output feedback controller is proposed by combining a backstepping controller and an extended high-gain observer to estimate the unknown dynamic and external disturbances in addition to the system states. Experiments on 4 DOF robotic manipulators verify the proposed control approach. The proposed control approach achieved the end-effector's desired trajectory under unknown system dynamics and disturbances.

Mainly, the integration of fine positioning piezo-actuated stages in precision motion systems is considered, which results in multi-stage configurations. Mostly, in such configurations, the fine stages are attached to the coarse positioning stages- that do not meet required precision- by mechanical means. Once the motion is synchronized, the fine stages enhance the overall precision of the multi-stage system. Undesirably, mechanical, and electromagnetic interference between the in-volved stages take place, which may limit the possible attainable precision. To control the fine stages, we propose the use of feedforward control based on the Prandtl-Ishlinskii model inverse in an attempt to accommodate related piezoceramics dynamic behavior and hysteresis. Targeting the semiconductor manufacturing, the needed multi-stage design steps according to the herein proposed approach are outlined. Also, the performance of a representative precision motion system comprising a planner coarse stage, and a uni-axial fine stage under step-and-scan trajectories is assessed. The results show that the proposed piezo-actuated fine stage improves the scanning accuracy of the overall motion system.

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.