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.

Implementing Non-Uniform Rational B-Splines in motion controllers involves challenges like computational complexity, parameterization issues, high-fidelity interpolation needs, real-time processing requirements, hardware limitations, system integration, error management, and the demand for user-friendly design tools, all of which necessitate advanced algorithms, robust hardware, and effective interfaces for successful implementation. Expanding the standard sine profile allows for the creation of a generalized, symmetric, frequency-sensitive basis function for generating point-to-point motion trajectories, while other functions like polynomials, sigmoids, and harmonic models can also be utilized. This study employs the basis function at the jerk level to meet the motion system’s constraints, and time shifts create B-spline-like and NURBS-like profiles for CAD and motion control, eliminating the need for interpolation and curve fitting. Utilizing vector notation and algorithms reduces computational demands on both the CAD and the motion controller sides, enabling real-time implementation. Frequency-sensitive B-spline profiles enhance both repetitive and random walk motions, facilitate data collection, and help segment the workspace into regions with unique frequency characteristics. A model-free approach addresses position-dependent errors, and experimental results validate the effectiveness of these techniques. Robust statistical models based on Multivariate Analysis of Variance are developed to both characterize and assess the performance of a precision motion system under the proposed framework. For example, the mean positioning error of the motion system improved from 2.29 μm to 0.717 μm at the maximum operating frequency of 90 rad/s under the model-free approach.

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.

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.

Stretching the definition of the standard Sine profile allows building a generalized symmetric frequency-aware basis function that can be used to generate reference motion trajectories. Other profiles such as polynomials, sigmoid, and harmonic-based models can be equally used under the proposed technique. Despite being suitable at the level of any higher-order time derivative, in this study, the generic basis function is realized at the jerk level such that the generated signals adhere to the limitations of the driven motion system. Introducing suitable time shifts, replicas of basis functions can be obtained giving rise to B-spline like frequency-aware profiles that can be used to realize the actual motion under any desired kinematical constraints, which are neatly written to reduces the computation burden at the motion controller side. Utilizing mainly the frequency-aware B-spline profiles, frequency-dependent random walk motion is presented and used to collect information about the driven motion system to help in characterizing any position-dependent errors through the statistical means, i.e. Analysis of Variance, and Design of Experiments. This allows dividing the working space in which motion takes place into several spatial regions with preferred frequency contents. The effectiveness of these proposed profiles is shown through hardware experiments using a precision motion system.

In semiconductor manufacturing, lithography machines are becoming more and more sophisticated system of systems. As an example, a TWINSCAN wafer scanner machine is composed of a wafer, and reticle handlers, reticle, optics, and two wafer chains or systems. In previous studies, we covered the interactions between the reticle, optics, and wafer chains during the step-and-scan cycle. In this study, we focus on the interaction between the additional wafer chain responsible for aligning the wafer substrate and taking its height map during the measurement cycle, and the other chains that are active during the step-and-scan cycle. Working in parallel to increase machine throughput, the inertial forces associated motion of the two cycles induce vibration that may propagate throughout the chains in the machine if no appropriate measures are taken. In this investigation, we look at the reference trajectories responsible for steering the chains throughout the two cycles, and propose two reference trajectory orchestrations that factor in the machine design, geometry, mass distribution, and functions. Theoretically, these orchestrations lead to suppressing the induced vibration without sacrificing the machine throughput while keeping the involved control loops intact.

In this paper, an immune average consensus behavior of distributed trajectory generators given in the form of a multi-agent system is presented. Starting with the well-known results of linear consensus protocols, we propose a decomposition of the invariant consensus value to enable a distributed cyber-attacks detection and mitigation mechanism among the connected agents over mainly undirected communication links. This decomposition suggests one preferred propagation of the invariant quantity along communication links of the multi-agent systems under study. Despite its simplicity, the effectiveness of this mechanism in detecting and mitigating various types of cyber-attacks is evident through a numerical simulation. Interestingly, the resulting defense mechanism will not be passive, rather it can initiate its counter-attack measures by pretending that the attack process was a success. Moreover, the trajectory generators can operate under stealth mode where the communication links get silenced or totally disconnected without affecting the intended behavior after having the consensus value locked.

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.

The operation time of an ideal reliable wafer scanner model is defined at the die level where the actual exposure process takes place as the time unit per die, or at the wafer substrate level as the time unit per wafer substrate. Therefore, the machine throughput is given as the reciprocal of the operation time. The involved motion profiles of a machine, namely the step-and-scan trajectories, function as the heartbeats that drive its multidisciplinary elements, which suggests that a multidisciplinary design optimization should be involved when such profiles are selected or designed. This is also true when considering the traverse motion profiles among rows and columns within the wafer substrate. The step-and-scan trajectories affect the machine throughput, performance, and die yield. The effects of tracking such profiles appear as structural vibration, tracking errors, and thermal loading at various machine elements such as the actuators, the reticle, the wafer, and the projection elements specifically when the exposure high-energy duration and frequency are not taken into consideration while designing the reference motion. From the dynamics perspective, having a reference motion with nonzero and bounded higher-order derivatives is recommended since it enhances the tracking performance of the machine, however, its ability to increase the operation time is usually overlooked. In an attempt to understand such effects, we present a case study that outlines the aforementioned aspects using three step-and-scan profiles of mainly$3^{rd}$-order. Taking the dynamics of the driven stage into consideration through input shaping, both the step-and-scan and traverse motion profiles are analyzed. We provide analytical expressions that can be used to generate both types of motion profiles on the fly without additional optimization. A simulation example of a simplified wafer scanner machine shows the usefulness of the proposed framework. Note to Practitioners—Choosing the most suitable operating conditions of a lithography machine is challenging. These conditions affect machine productivity, profit margin, and maintenance. In this paper, we reveal the relation between the selection of operating conditions based on several decision variables- and the kinodynamic step-and-scan trajectory generation based on specific machine parameters and clients’ requirements. Being chart-based, the selection process of an operating point can be less practical at some points. However, using appropriate curve fitting tools, the information provided in the optimal operating charts can be put into suboptimal closed-form expressions that facilitate the selection process. Therefore, the designed trajectories parameters can be easily saved in lookup tables for ease of evaluation and future use. This helps in accommodating changes in the operation plans and flexible manufacturing systems. Also, starting with a given set of machine parameters, it is possible to calculate the optimal machine operating point when the input shaping technique is used, as illustrated in this paper.

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.

Cooperative adaptive cruise control (CACC) is a smart transportation solution that can mitigate traffic jams and improve road safety. CACC performance is heavily impacted by communication time delay; moreover, control theory solutions generally compromise control performance by tuning control gains in order to maintain plant stability. We propose a control-machine learning hybrid approach called deep time delay filter (DTDF). DTDF predicts the present (un-delayed) car states given time delayed versions. We successfully train a neural network for the DTDF method and use a physical testbed to show that DTDF can mitigate the effects of constant time delays as large as 5s while maintaining superior control performance compared to that of a baseline control algorithm.

Fault mitigation in Connected Autonomous Vehicle (CAV) platoons is faster and more reliable if the fault structure is known. In this paper we propose using transmissibility operators, which are relationships that relate a set of velocities with another in the platoon, to classify the faults. Transmissibility operators were shown to be exceptional in signals estimation; however, its also shown to be noncausal and thus can only be used offline. To this end, we propose using Data Aggregation (DAgger), which is an extension in imitation learning to transfer the classification experience from transmissibility operators to a novice machine learning agent to be used online. A heterogeneous CAV platoon was modeled with three different faults separately. These faults are actuator disturbances, false data injection attacks, and communication time delay. The proposed algorithm is then tested on the platoon model and then applied to an experimental setup that consists of three autonomous robots. The overall classification accuracy achieved was 95.8% for the experiment.

This paper extends the results in [1] considering time delays in Standard Car Following models (CFMs). In [1], connected vehicles are characterized with CFMs models and controlled using linear string analysis to stabilize single-lane car following of human-driven vehicles (HDVs). In this paper, we revisit stability and safety conditions for traffic considering time delays due to lags in the input signals. We perform plant stability and string stability analysis to derive these conditions. Then we obtain the optimal number of HDVs that can be stabilized using one autonomous vehicle. Numerical simulations are provided to implement a case study on Intelligent Driver Model to discuss the influence of time delays that arise on HDVs and the autonomous vehicle on optimal number of HDVs that can be stabilized using one autonomous vehicle.

In this paper, we consider periodic communication delays within the connected autonomous vehicles platoon. Periodic signals are fundamentally simple to create, and in this study we analyze whether certain amplitude or frequencies can cause instability. This is important as we discover in this study, the classical method of replacing time-varying delays with constant delays does not capture the complex stability boundary of periodic time delays which could be exploited by attackers to cause instability in the vehicle platoon. We use the semi-discretization method to obtain plant stability. Then, we take the average value of the time delay functions to characterize the maximal admissible delay region such that the time-delayed system remains stable and provide string stability analysis. Through numerical simulations, we verify the analytical results. We construct stability charts in different parameter spaces to explore the effects of the model parameters on the system stability. A specific range of frequencies was found that could destabilize the connected autonomous vehicle platoon.

Adopting the ideal reliable machine model, the throughput of a lithography machine can be given as the reciprocal of the operation time. This time can be defined at the die level where the actual exposure process takes place as the time unit per die. A closer look at the motion profiles, namely step-and-scan trajectories, suggests that a multi-disciplinary design optimization should be involved when such profiles are selected or designed. Being the reference motion used, the step-and-scan trajectories not only affect the machine performance, but also affect its throughput and to an extent the die yield as well. Structural vibration, and thermal loading at the actuators due to friction and repetitive motion may build up because of following the reference motion. Moreover, since the exposure process and equipment are synchronized with the reference motion, deformation and thermal stress may affect the reticle, the wafer and the projection elements if the exposure high-energy duration and frequency are not taken into consideration while designing the reference motion. From dynamics point of view, reference motion with higher-order derivatives enhances the tracking performance of the machine, however, its operational cost is usually overlooked. In this paper, we present a case-study that outlines the aforementioned aspects using three step-and-scan profiles of the same order. We conclude by posing the following research question: what is the best combination of orders of the step and the scan trajectories that jointly meet the desired performance and operating conditions?

Different health-monitoring techniques were considered in the literature to enhance the safety and stability of Connected Autonomous Vehicle (CAV) platoons. The health-monitoring processes include fault detection, localization, and mitigation. It is evident that mitigating these faults is faster and more reliable if the fault structure is known. To this end, we consider classifying the fault class using supervised machine learning. We first model a heterogeneous CAV platoon with three different common faults separately. These faults are bounded actuator disturbances (namely, engine bearing knock), False Data Injection (FDI) attack, and communication time delay. We consider two supervised machine learning classifiers, the first classifier determines whether the fault is bounded disturbances or communication delay, and the second classifier determines whether the disturbances are in the physical or cyber layer. We have compared four machine learning techniques for each classifier, Support Vector Machine (SVM), Naive Bayes (NB), Quadratic Discriminant (QD), and K-Nearest Neighbors (KNN). The classifiers are trained firstly on the simulation model, then are tested on a different set of observations and tested experimentally on a platoon of three autonomous robots. The highest accuracy was achieved by considering SVM for the first classifier and QD for the second classifier. The overall classification accuracy achieved is 96.8% for the simulation test and 92.1% for the experiment.

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.

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.

The impact and mitigation of false data injection (FDI) attacks with nonlinear dynamics targeting actuators in cyber-physical mechatronic systems (CPMSs) is investigated in this paper. Actuators in mechatronic systems exhibit vulnerabilities to inputs with well-known nonlinearities (e.g., backlash, deadzone, and saturation), where the nonlinear dynamics can affect the actuators' performance. A mitigation approach is proposed based on the retrospective cost-based adaptive control to stabilize and regulate the CPMS under such FDI cyberattack. Since mechatronic systems are implemented with actuators of different dynamical properties, this paper considers systems of linear and nonlinear dynamics. Simulation results demonstrate how the proposed adaptive control system achieves internal model control with the dynamics of the actuator systems and the nonlinearities of the backlash, deadzone, and saturation attacks. Results further show that the controller inverts and rejects the effects of attacks with unknown nonlinearities.

A new methodology that employs the rate-dependent Prandtl-Ishlinskii model (RDPI) as a model and a compensator is suggested in this study for modeling and compensation of rate-dependent hysteresis nonlinearities of a piezoelectric actuator. The technique employs a restructuration of the model that ignores the need to derive an inverse model, which avoids the additional calculations required to formulate a compensator. The simulation results are presented to demonstrate the effectiveness of the strategy on modeling and compensation of hysteresis nonlinearities at various frequencies. The simulation results were followed by experimental study on a piezoelectric actuator that exhibits rate-dependent hysteresis nonlinearities. The results demonstrate that the proposed methodology can be employed effectively for compensation of rate-dependent hysteresis nonlinearities without developing an inverse model.

Piezo micro-positioning actuators have been widely used in micro-positioning applications due to the fast expansion, high force generation, and unlimited resolution. However, these actuators exhibit some rate-dependent hysteresis effects which affect the accuracy of these micro-positioning systems and may even lead to system instability. In this paper, the rate-dependent Prandtl-Ishlinskii model is employed to characterize the rate-dependent hysteresis nonlinearities of a piezo micro-positioning stage. The analytical inverse of the rate-dependent Prandtl-Ishlinskii model is then formulated using the initial loading curve concept. This inverse is utilized as a feedforward compensator to compensate for the hysteresis nonlinearities of a piezo micro-positioning stage under excitation in the 1-50 Hz frequency range.

Smart actuators such as Shape Memory Alloy actuators, magnetostrictive actuators, and piezoceramic actuators show different symmetric and asymmetric hysteresis loops. Shape Memory Alloy actuators and magnetostrictive actuators, as an example, exhibit saturated output at maximum and/or minimum input. In this paper, a generalized Prandtl-Ishlinskii model is formulated to characterize hysteresis nonlinearities of different Smart actuators. In this model, a generalized asymmetric play hysteresis operator is proposed and integrated with a density function to characterize different asymmetric hysteresis loops of smart actuators. This modified play hysteresis operator shows the capability to generate minor and major hysteresis loops with varying slopes of ascending and descending input-output curve. Moreover, this operator exhibits saturated major and minor input-output relationships. The capability ability of the proposed model to characterize hysteresis loops of different smart actuators is demonstrated by comparing its output with measured saturated symmetric and asymmetric hysteresis loops of SMA actuators and magnetostrictive actuators.