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 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.

Connected Autonomous Vehicle (CAV) platoons have been extensively studied to protect against cyber and physical vulnerabilities. Faults can occur in all layers of the platoon system or could be introduced by impaired human drivers. Since different types of faults may require different fault resolution methods, identifying the fault class facilitates the selection of the best mitigation strategy. This paper introduces a Multi-Head Attention Machine Learning (MHA-ML) approach to classify a set of five different faults and abnormalities in mixed autonomous and human-driven vehicle platoons. Autonomous vehicles can face actuator faults, False Data Injection (FDI) attacks, and Denial-of-Service (DoS) attacks, while abnormalities such as drunk or distracted human drivers could occur. MHA-ML is developed to identify faulty vehicle behavior over long sequences of sensor measurements. MHA-ML is trained on a mixed platoon simulation model and then tested on mobile laboratory robots. The experiment classifies the five fault categories with 90% accuracy and outperforms a baseline recurrent neural network approach.

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.

This study proposes an algorithm for fault detection and mitigation of mixed autonomous and human-driven vehicle platoons based on transmissibility identification. This work is motivated by the fact that on-road human-drivers’ behaviour is unknown and difficult to be predicted. Transmissibility operators are mathematical operators that relate one subset of outputs to another in the same system. The transmissibility superiority is represented in the in-dependency on the system excitation signals. We reformulate the system dynamics to render the system inputs, external disturbances, as well as the human-drivers’ behaviour along with any other nonlinearities as independent excitation signals on the system. Therefore, the transmissibility operators become independent of the human-drivers’ behaviour and robust against external disturbances. Transmissibilities are then applied to detect and localize physical and cyber faults within the platoon. Then these faults are mitigated using a transmissibility-based sliding mode controller. The controller stability and the string stability are investigated while the controller is active and the faults are mitigated. We validate the proposed algorithm on a model of the platoon obtained using the bond graph approach. Moreover, we apply the proposed algorithm experimentally to a platoon consisting of three robots (i.e. two autonomous robots and a human-driven robot), that is connected using wireless communications.Note to Practitioners—The existence of connected autonomous vehicles depends greatly on the smooth transition between the current on-road human-driven vehicles to autonomous vehicles. The typical methods of securing dynamic systems depend on estimating the system behaviour and responses. Increasing the number of autonomous vehicles on roads necessitates the typical securing techniques to estimate the human-drivers’ behaviour. Thus, securing the connected autonomous vehicles during this transition is challenging since the on-road human-driver behavior is unknown and difficult to be estimated. Moreover, connected autonomous vehicles should adapt to their environment while maintaining their role within the autonomous platoon. This adaptation includes adapting to the unknown human-driver behaviour. This inspired the authors to develop the proposed transmissibility-based fault mitigation. The proposed technique is shown to be able to handle unknown human-driver behaviors, different driving conditions such as road irregularities and different weather conditions, and different physical and cyber faults (i.e., in the vehicles or in the communication links). The platoon stability is then investigated while the faults are mitigated, and shown to guarantee the platoon stability.

An autonomous vehicle platoon is a network of autonomous vehicles that communicate together to move in a desired way. One of the greatest threats to the operation of an autonomous vehicle platoon is the failure of either a physical component of a vehicle or a communication link between two vehicles. This failure affects the safety and stability of the autonomous vehicle platoon. Transmissibility-based health monitoring uses available sensor measurements for fault detection under unknown excitation and unknown dynamics of the network. After a fault is detected, a sliding mode controller is used to mitigate the fault. Different fault scenarios are considered including vehicle internal disturbances, cyber attacks, and communication delays. We apply the proposed approach to a bond graph model of the platoon and an experimental setup consisting of three autonomous robots.