Physical Actuators Research Articles

Static actuator-sharing algorithm for concurrent control of multiple plasma properties

Abstract Simultaneous regulation of multiple properties in next-generation tokamaks like ITER and Fusion Pilot Plant (FPP) may require the integration of different plasma control algorithms. Such integration requires the conversion of individual controller commands into physical actuator requests while accounting for the coupling between different plasma properties. This work proposes a tokamak and scenario-agnostic actuator-sharing algorithm (ASA) to perform the above-mentioned command-request conversion and, hence, integrate multiple plasma controllers. The proposed algorithm implicitly solves a quadratic programming (QP) problem formulated to account for the saturation limits and the relation between the controller commands and physical actuator requests. Since the constraints arising in the QP program are linear, the proposed ASA is highly computationally efficient and can be implemented in the tokamak plasma control system (PCS) in real time. Furthermore, the proposed algorithm is designed to handle real-time changes in the control objectives and actuators’ availability. Nonlinear simulations carried out using the Control Oriented Transport SIMulator (COTSIM) illustrate the effectiveness of the proposed algorithm in achieving multiple control objectives simultaneously.

Actuation Performance and Versatility of Photothermally Driven Organic Crystals.

Photomechanical crystals exhibit mechanical motion upon light irradiation and may thus find applications as actuators. Over the last decades, many photomechanical organic crystals have been developed, commonly via photochemical reactions, particularly photoisomerization. However, photochemical crystal actuation is associated with several drawbacks, including a limited number of available crystals, slow actuation speed (< 5 Hz), and narrow wavelength range (< 550 nm). Such constraints have hindered the widespread use of crystals as actuation materials. In this minireview, we focus on crystal actuation by employing more universal physical phenomena (the photothermal effect and photothermally resonated natural vibration) and quantitatively evaluate actuation performance. Both mechanisms, particularly the latter, outperformed conventional photomechanical crystal activation in terms of both speed (maximum: 1,350 Hz) and the useful wavelength range (ultraviolet to near-infrared). The oscillation frequencies of the crystals exceeded those of polymers, efficiently filling the gap between soft and hard materials. Both the photothermal effect and natural vibration can actuate any crystal that absorbs light. These two versatile physical actuation mechanisms could expand 40 years of research on photomechanical crystals-which had been based on photochemical reactions-from the realm of chemistry into engineering and lead to their practical applications in actuators and soft robots.

Actuation Performance and Versatility of Photothermally Driven Organic Crystals

Photomechanical crystals exhibit mechanical motion upon light irradiation and may thus find applications as actuators. Over the last decades, many photomechanical organic crystals have been developed, commonly via photochemical reactions, particularly photoisomerization. However, photochemical crystal actuation is associated with several drawbacks, including a limited number of available crystals, slow actuation speed (&lt; 5 Hz), and narrow wavelength range (&lt; 550 nm). Such constraints have hindered the widespread use of crystals as actuation materials. In this minireview, we focus on crystal actuation by employing more universal physical phenomena (the photothermal effect and photothermally resonated natural vibration) and quantitatively evaluate actuation performance. Both mechanisms, particularly the latter, outperformed conventional photomechanical crystal activation in terms of both speed (maximum: 1,350 Hz) and the useful wavelength range (ultraviolet to near‐infrared). The oscillation frequencies of the crystals exceeded those of polymers, efficiently filling the gap between soft and hard materials. Both the photothermal effect and natural vibration can actuate any crystal that absorbs light. These two versatile physical actuation mechanisms could expand 40 years of research on photomechanical crystals—which had been based on photochemical reactions—from the realm of chemistry into engineering and lead to their practical applications in actuators and soft robots.

Lightweight Privacy Preserving Scheme for IoT based Smart Home

Background: The Internet of Things (IoT) is the interconnection of physical devices, controllers, sensors and actuators that monitor and share data to another end. In a smart home network, users can remotely access and control home appliances/devices via wireless channels. Due to the increasing demand for smart IoT devices, secure communication also becomes the biggest challenge. Hence, a lightweight authentication scheme is required to secure these devices and maintain user privacy. The protocol proposed is secure against different kinds of attacks and as well as is efficient. Methods: The proposed protocol offers mutual authentication using shared session key establishment. The shared session key is established between the smart device and the home gateway, ensuring that the communication between the smart devices, home gateway, and the user is secure and no third party can access the information shared. Results: Informal and formal analysis of the proposed scheme is done using the AVISPA tool. Finally, the results of the proposed scheme also compare with existing security schemes in terms of computation and communication performance cost. The results show that the proposed scheme is more efficient and robust against different types of attacks than the existing protocols. Conclusion: In the upcoming years, there will be a dedicated network system built inside the home so that the user can have access to the home from anywhere. The proposed scheme offers secure communication between the user, the smart home, and different smart devices. The proposed protocol makes sure that security and privacy are maintained since the smart devices lack computation power which makes them vulnerable to different attacks.

On Distinguishability of Anomalies as Physical Faults or Actuation Cyberattacks

Abstract Increased automation has created an impetus to integrate infrastructure with wide-spread connectivity in order to improve e ciency, sustainability, autonomy, and security. Nonetheless, this reliance on connectivity and the inevitability of complexity in this system increase the vulnerabilities to physical faults or degradation and external cyber-threats. However, strategies to counteract faults and cyberattacks would be widely di erent and thus it is vital to not only detect but also to identify the nature of the anomaly that is present in these systems. In this work, we propose a mathematical framework to distinguish between physical faults and cyberattack using a sliding mode based unknown input observer. Finally, we present simulation case studies to distinguish between physical faults and cyberattacks using the proposed distinguishability metric and criterion. The simulation results show that the proposed framework successfully distinguishes between faults and cyberattacks.

Global joint position regulation of a class of torque-driven robot manipulators within actuators prescribed safe operating torque–speed area

This paper addresses the joint position regulation of a class of n degrees of freedom torque-driven robot manipulators under a novel practical motivated control objective: Simultaneous Regulation & Prescribed Safe Operating Area (SR&PSOA). More specifically, torque–speed characteristic safe limit functions associated to each actuator are taking into account to ensure that besides of global joint positioning, the requested torque control inputs to robot actuators remain inside user prescribed safe operating torque–speed areas, so avoiding thermal and physical actuator damages. A static smooth nonlinear controller is proposed to achieve global position regulation under prescribed speed-depending bound torque input. Numerical simulations and experimental results are included to illustrate the effectiveness of the proposed controller.

Adaptive backstepping control for a class of uncertain systems with actuator delay and faults

Unknown actuator failures are inevitable in practical systems. At the same time, time delay exists in many physical actuators, and the system performance will be affected by such actuator delay and faults. However, the results of studies that attempt to compensate for unknown failures of actuators with time delay are still very limited. In this paper, such a problem is studied, and an adaptive control scheme is proposed based on backstepping approaches. First, the input delay of actuator faults and output disturbances are transformed into unknown effects on the output signal. In the backstepping recursive design, these unknown effects will accumulate to the last step of the controller design. Then, a new Lyapunov function is constructed by introducing auxiliary signals to prove the stability of the system. It is shown that the proposed control scheme can compensate for the effects caused by unknown actuator failures and input delays. The stability of the closed-loop system can be guaranteed by this adaptive controller. Finally, simulation studies are used to verify the effectiveness of the proposed scheme.

Kinematic analysis of a finger-mimicking pneumatic network actuator consisting of parallelogram-shaped chambers

Fast pneumatic network (fast Pneu-Net, fPN) actuators made of silicone materials have aroused increasing attention these years for their promising applications in soft robotics. To enrich current research, we present a novel fPN actuator that consists of parallelogram-shaped chambers and aluminum endoskeleton that takes the shape of human finger. The proposed actuator can bend at multi-positions when inflated, bringing about more degrees of freedom controllability. Additionally, the performed finite element analysis indicates that the parallelogram-shaped chamber that tilts 30° towards the distal end exhibits an increase in deformation for about 11 % compared to the rectangle-shaped counterpart, offering an improved bending performance. An analytical model that based on the pressurized hyperelastic membrane theory is developed to estimate the actuator’s bending angles/profiles at varied pressures. Geometric relation of the chamber interactions upon deformation is discussed depending on whether the lateral walls are contact or not. The stress-strain relation of the elastomer is described using 3-term Yeoh constitutive model. Experimental validations on the theoretical calculations are carried out, from which the coefficient of proportionality that takes into account the effect on deformation caused by the embedded endoskeleton is determined. Furthermore, the Modified Denavit-Hartenberg (MDH) notation is employed to establish the forward kinematics of the actuator, and the numerical calculations are compared with the physical actuator.

A configuration-optimisation method for passive-active-combined suspension design

Active control can improve the performance of a passive vehicle suspension, but it comes with a high power consumption and large actuator forces. To balance dynamic performance and power/force needs, both the passive and active parts in the suspension should be designed together and work synergistically. Previous approaches that combine passive and active design have been limited to a narrow range of suspension structures, such as passive-active-parallel layouts. This leaves many other structures unable to be explored (e.g., passive-active-series layouts), and thus the current identified design may be far from optimal. Another limitation is that these previous approaches assume an ideal active part and do not consider the parasitic effects of a physical active actuator and its transmission system, such as backlash, friction and inertia. This simplification would hinder their practical application in real-life situations. To address these two limitations, this work introduces a novel design method for passive-active-combined suspensions. Firstly, it allows for the enumeration of all possible suspension designs consisting of a pre-determined number of stiffness, damping, inertance and active actuator elements. Secondly, the method takes into account the parasitic effects that arise from physical realisation when identifying the optimal suspension design. The effectiveness of this method is demonstrated through a quarter-car case study, where the skyhook controller is adopted as an example control strategy. It is found that compared to the traditional combined suspension, the identified design achieves a significant improvement in the trade-off between ride comfort and required active force. The obtained results are verified experimentally.

Incremental Nonlinear Dynamic Inversion Attitude Control for Helicopter with Actuator Delay and Saturation

In this paper, an incremental nonlinear dynamic inversion (INDI) control scheme is proposed for the attitude tracking of a helicopter with model uncertainties, and actuator delay and saturation constraints. A finite integral compensation based on model reduction is used to compensate the actuator delay, and the proposed scheme can guarantee the semi-globally uniformly ultimately bounded tracking. The overall attitude controller is separated into a rate, an attitude, and a collective pitch controller. The rate and collective pitch controllers combine the proposed method and INDI to enhance the robustness to actuator delay and model uncertainties. Considering the dynamic of physical actuators, pseudo-control hedging (PCH) is introduced both in the rate and attitude controller to improve tracking performance. By using the proposed controller, the helicopter shows good dynamics under the multiple restrictions of the actuators.

On the Virtualization of Audio Transducers.

In audio transduction applications, virtualization can be defined as the task of digitally altering the acoustic behavior of an audio sensor or actuator with the aim of mimicking that of a target transducer. Recently, a digital signal preprocessing method for the virtualization of loudspeakers based on inverse equivalent circuit modeling has been proposed. The method applies Leuciuc's inversion theorem to obtain the inverse circuital model of the physical actuator, which is then exploited to impose a target behavior through the so called Direct-Inverse-Direct Chain. The inverse model is designed by properly augmenting the direct model with a theoretical two-port circuit element called nullor. Drawing on this promising results, in this manuscript, we aim at describing the virtualization task in a broader sense, including both actuator and sensor virtualizations. We provide ready-to-use schemes and block diagrams which apply to all the possible combinations of input and output variables. We then analyze and formalize different versions of the Direct-Inverse-Direct Chain describing how the method changes when applied to sensors and actuators. Finally, we provide examples of applications considering the virtualization of a capacitive microphone and a nonlinear compression driver.

An Inverse Kinematics Approach for the Analysis and Active Control of a Four-UPR Motion-Compensated Platform for UAV–ASV Cooperation

In the present day, unmanned aerial vehicle (UAV) technology is being used for a multitude of inspection operations, including those in offshore structures such as wind-farms. Due to the distance of these structures to the coast, drones need to be carried to these structures via ship. To achieve a completely autonomous operation, the UAV can greatly benefit from an autonomous surface vehicle (ASV) to transport the UAV to the operation location and coordinate a successful landing between the two. This work presents the concept of a four-link parallel platform to perform wave-motion synchronization to facilitate UAV landings. The parallel platform consists of two base floaters connected with rigid rods, linked by linear actuators to a top mobile platform for the landing of a UAV. Using an inverse kinematics approach, a study of the position of the cylinders for greater range of motion and a workspace analysis is achieved. The platform makes use of a feedback controller to reduce the total motion of the landing platform. Using the robotic operating system (ROS) and Gazebo to emulate wave motions and represent the physical model and actuator system, the platform control system was successfully validated.

Development of an optimally designed real-time automatic citrus fruit grading–sorting​ machine leveraging computer vision-based adaptive deep learning model

Conventional automation approaches for postharvest operations are plagued by time and data inefficiency seldom leading to suboptimal solutions. Automatic machines often require highly skilled software professionals for calibration and reconfiguration thus making the technology prone to high costs. Contemporary sensors and smart devices capable of handling deep learning image analytics have been employed in the present study for the development of an automatic machine that performs postharvest operations, like—washing, vision-based sorting and weight-based grading of citrus fruits with much reduced human effort while achieving excellent performance for the designated tasks. Accuracy of performance was ensured by the optimal design of mechanical components carried out by kinematic synthesis and dimensional analysis. The machine was equipped with an effective custom lightweight CNN model “SortNet” that was designed and tuned to carry out vision-based classification of citrus fruits into “accept” and “reject” based on surface characteristics. SortNet was less complex and took less computational time while exhibiting comparable accuracy with respect to existing state-of-the-art pre-trained deep learning models. An embedded system operated by a single-board computer was used in the weight grading section for segregating fruits based on three weight categories. Evaluation, realization and transferability of the above said strategy was demonstrated by the real hardware with physical actuators working in real-time to serve as proof-of-concept for a sustainable solution to postharvest automation of citrus fruits.

Flexoelectric Effect of Ferroelectric Materials and Its Applications

The flexoelectric effect, which exists in all dielectrics, is an electromechanical effect that arises due to the coupling of strain gradients (or electric field gradients) with electric polarization (or mechanical stress). Numerous experimental studies have demonstrated that ferroelectric materials possess a larger flexoelectric coefficient than other dielectric materials; thus, the flexoelectric response becomes significant. In this review, we will first summarize the measurement methods and magnitudes of the flexoelectric coefficients of ferroelectric materials. Theoretical studies of the flexoelectric coefficients of ferroelectric materials will be addressed in this review. The scaling effect, where the flexoelectric effect dramatically increases when reducing the material dimension, will also be discussed. Because of their large electromechanical response and scaling effect, ferroelectric materials offer vast potential for the application of the flexoelectric effect in various physical phenomena, including sensors, actuators, and transducers. Finally, this review will briefly discuss some perspectives on the flexoelectric effect and address some pressing questions that need to be considered to further develop this phenomenon.

Optimal fractional-order proportional–integral–derivative control enabling full actuation of decomposed rotary inverted pendulum system

Allowing for a “virtual” full actuation of a rotary inverted pendulum (RIP) system with only a single physical actuator has been a challenging problem. In this paper, a hybrid control scheme that involves a pole-placement feedback controller and an optimal proportional–integral–derivative (PID) or fractional-order PID (FOPID) controller is proposed to simultaneously enable the tracking control of the rotary arm and the stabilization of the pendulum arm in an input–output feedback linearized RIP system. The PID controller is optimized first with the particle swarm optimization (PSO) to obtain three optimal gains, and then the other two parameters of the FOPID controller are optimized with the PSO. Compared to the optimized PID controller, the optimized FOPID controller improves the tracking and stabilizing accuracy by 53% and 29%, respectively, and demonstrates better adaptability for tracking different reference signals. Moreover, the hybrid FOPID controller exhibits 74.8% and 53% higher tracking accuracy than previous optimized model reference adaptive control PID (MRAC-PID) and linear–quadratic regulator (LQR) controllers, respectively. The proposed hybrid controllers are also digitized with different rules and sampling times, showing a closer performance between the discrete-time and continuous-time hybrid controllers under smaller sampling times.

Event-triggered adaptive control of hypersonic vehicles subject to actuator nonlinearities

This paper investigates the event-triggered adaptive control problem for air-breathing hypersonic vehicles (AHVs) subject to physical actuator nonlinearities. In contrast with abundant AHV control strategies belonging to the traditional sample-data framework, the presented adaptive control works with an event-triggered mechanism, which naturally reduces the executing frequency of scramjet fuel-to-air equivalency ratio (FER) and aerodynamic control surface (ACS). During the event-triggered controller design, practical actuator characteristics including magnitude-rate composite constraints and servo transient dynamics are synthetically considered. In the velocity loop, the built-in FER saturation, which is a fusion of physical FER magnitude and rate constraints, is handled by reshaping the velocity reference with the help of an adaptive reference governor. Meanwhile in the altitude loop, a neural control allocator directly generates feasible ACS commands via a neural network that is well trained by means of the optimal control allocation. As the result, ACS magnitude-rate composite constraints and servo transient dynamics can be accommodated without implementing any time-consuming optimization searching process. Theoretical analysis indicates the guaranteed closed-loop stability and tracking performance while excluding the undesired Zeno behavior. Simulations validate the effectiveness of the proposed event-triggered adaptive control.

Building Blocks for an Automated Quality Assurance Concept in High Throughput Battery Cell Manufacturing

The increasing demand for sustainable energy raises the request for battery cells. Industry and research are faced with challenges like complex processes, complex machinery, and many intra-process interactions within the field of battery cell production. Major problems, such as low process stability and quality fluctuations, lead to high scrap rates. Result is a reduced sustainability in the production process. To address these challenges, the main content of this paper is a conceptual design of a virtual Quality Gate (QG) system, for quality assurance and quality prediction. The concept of virtual QG combines physical and digital elements. On the physical side actuators, sensors, and measurement technologies are included to provide the raw data. The digital side of virtual QG includes necessary elements for data acquisition, data processing, data analysis and information provision for the decision-making process in the real world. Focus of the presented approach is illustrating how to select the appropriate location of QG for quality decisions in conjunction with the derivation of necessary decisions, process information and evaluation of measurement technology that is needed.

Active spatial control of photothermal heating and thermo-actuated convective flow by engineering a plasmonic metasurface with heterodimer lattices

Thermo-plasmonics, using plasmonic structures as heat sources, has been widely used in biomedical and microfluidic applications. However, a metasurface with single-element unit cells, considered as the sole heat source in a unit cell, functions at a fixed wavelength and has limited control over the thermo-plasmonically induced hydrodynamic effects. Plasmonic metasurfaces with metal disk heterodimer lattices can be viewed to possess two heat sources within a unit cell and are therefore designed to photo-actively control thermal distributions and fluid dynamics at the nanoscale. The locations of heat sources can be switched, and the direction of the convective flow in the central region of the unit cell can be reversed by shifting the wavelength of the excitation source without any change in the excitation direction or physical actuation of the structural elements. The temperature and velocity of a fluid are spatiotemporally controlled by the wavelength selectivity and polarization sensitivity of the plasmonic metasurface. Additionally, we investigate the effects of geometric parameters on the surface lattice resonances and their impact on the temperature and fluid velocity of the optofluidic system. Our results demonstrate excellent optical control of these plasmonic metasurface heating and thermal convection performances to design flexible platforms for microfluidics.

Automated function development for emission control with deep reinforcement learning

The conventional automotive development process for embedded systems today is still time- and data-inefficient, and requires highly experienced software developers and calibration engineers. Consequently, it is cost-intensive and at the same time prone to sub-optimal solutions. Reinforcement Learning offers a promising approach to address these challenges. The evolved agents have proven their ability to master complex control tasks in a close-to-optimal manner without any human intervention, but the training procedures are hardly compatible with current development processes. As a result, Reinforcement Learning has rarely been used in powertrain development until now. This work describes an integration of Reinforcement Learning in the embedded system development process to automatically train and deploy agents in transient driving cycles. Using the example of exhaust gas re-circulation control for a Diesel engine, an agent is successfully trained in a fully virtualized environment, achieving emission reductions of up to 10% in comparison to a state-of-the-art controller. Further investigations are carried out to quantify the impact of the driving cycle and ambient conditions on the agent’s performance. To demonstrate the transferability between different levels of virtualization, the experienced agent is then tested in closed-loop with a real hardware controller to operate the physical actuator. By confirming the reproducibility of the learned strategy on real hardware, this article serves as proof-of-concept for a sustainable, Reinforcement Learning based path to automatically develop embedded controllers for complex control problems.

WSN Architectures for Environmental Monitoring Applications

Wireless sensor networks (WSNs) have become ubiquitous, permeating every aspect of human life. In environmental monitoring applications (EMAs), WSNs are essential and provide a holistic view of the deployed environment. Physical sensor devices and actuators are connected across a network in environmental monitoring applications to sense vital environmental factors. EMAs bring together the intelligence and autonomy of autonomous systems to make intelligent decisions and communicate them using communication technologies. This paper discusses the various architectures developed for WSNs in environmental monitoring applications and the support for specific design goals, including machine learning in WSNs and its potential in environmental monitoring applications.