Sensor Actuator Research Articles

Imaging Sample Acidification Triggered by Electrochemically Activated Polyaniline.

In this letter, we demonstrate 2D acidification of samples at environmental and physiological pH with an electrochemically activated polyaniline (PANI) mesh. A novel sensor–actuator concept is conceived for such a purpose. The sample is sandwiched between the PANI (actuator) and a planar pH optode (sensor) placed at a very close distance (∼0.50 mm). Upon application of a mild potential to the mesh, in contrast to previously reported acidification approaches, PANI releases a significant number of protons, causing an acid–base titration in the sample. This process is monitored in time and space by the pH optode, providing chemical imaging of the pH decrease along the dynamic titration via photographic acquisition. Acidification of samples at varying buffer capacity has been investigated: the higher the buffer capacity, the more time (and therefore proton charge) was needed to reach a pH of 4.5 or even lower. Also, the ability to map spatial differences in buffer capacity within a sample during the acid–base titration was unprecedentedly proven. The sensor–actuator concept could be used for monitoring certain analytes in samples that specifically require acidification pretreatment. Particularly, in combination with different optodes, dynamic mapping of concentration gradients will be accessible in complex environmental samples ranging from roots and sediments to bacterial aggregates.

Dynamics compensation approach to stabilization and observation for abstract linear systems

This paper considers the problem of actuator dynamics and sensor dynamics compensation for linear systems. The dynamics compensations are described by stabilizing or observing cascade systems which can be decoupled by the upper-block-triangle transform. A full state feedback is designed to stabilize the cascade system exponentially and a state observer is proposed to estimate the system state in the abstract framework. It is shown that the error based observer design for output regulation can be converted into a sensor dynamics compensation problem by the well-known regulator equations. As applications, an unstable heat equation with ODE actuator dynamics and an ODE with an unstable heat equation sensor dynamics are investigated to validate the theoretical results. The numerical simulations for both of them are carried out to validate the proposed approach visually.

Hybrid Ni–Co–Ni Structures Prepared by Magnetophoresis as Efficient Permanent Magnets for Integration into Microelectromechanical Systems

The direct integration of performant permanent magnets (PMs) within miniaturized circuits remains both a scientific and a technological challenge. Magnetophoresis‐driven capillary assembly of hard magnetic nanoparticles is a promising approach to fabricate 3D rare‐earth‐free PMs. However, this process implies the use of soft magnetic blocks to generate the magnetic field gradients required to localize the assembly directly onto silicon substrates. The impact of these soft elements onto the overall magnetic properties is evaluated using Co nanorods as hard material and 150 μm–thick Ni blocks. As expected, the presence of Ni softens the overall properties of the hybrid magnet obtained, but PM properties are preserved for reduced Ni volumes. Magnetic induction as high as 19 mT at a distance of 200 μm is generated by the hybrid Ni–Co–Ni structures, allowing for the electromagnetic actuation of a microelectromechanical resonant sensor.

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.

Fault-Tolerant Control of Nonlinear Systems With Actuator and Sensor Faults Based on T–S Fuzzy Model and Fuzzy Observer

In this work, we study simultaneous reconstructions of sensor fault and actuator fault, as well as fault-tolerant control (FTC) for nonlinear systems approximated by the T–S fuzzy model. By making the sensor fault as part of the state, a rectangular singular system is firstly given, based on which, a proportional-integral observer (PIO) is synthesized to realize the reconstructions of sensor and actuator faults simultaneously. With the support of these estimation information, an FTC scheme is well provided to maintain the stability of the closed-loop system even in the occurrence of faults. Furthermore, by resorting to linear matrix inequalities (LMIs), the stability criteria are formulated via the fuzzy Lyapunov function method. Finally, the performance of the theoretical result is verified through two real dynamics.

AdaptiveHART: An Adaptive Real-Time MAC Protocol for Industrial Internet-of-Things

Wireless sensor–actuator networks (WSAN) for real-time reliable communications are being embraced in the Industrial Internet of Things. One key property required for WSAN is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">adaptability</i> with respect to dynamic environments. Most existing studies assume that a network manager computes global schedules and disseminates schedule information to every node whenever the network parameters need to be updated. Such a centralized approach may degrade control performance due to nonreal-time response to dynamic environments. In this article, we introduce a novel time division multiple access MAC protocol, AdaptiveHART, which updates the network parameters in real-time without the dissemination intervals. AdaptiveHART achieves a reduction in adaptation latency up to 74%, maximum schedulable ratio, and control performance up to 75% than the conventional WirelessHART. We assess our protocol using a stochastic response time analysis, which provides an accurate end-to-end delay distribution and a schedulability condition that can guarantee real-time performance in WSAN.

Stretchable Sensors and Electro-Thermal Actuators with Self-Sensing Capability Using the Laser-Induced Graphene Technology.

Laser-induced graphene (LIG) represents a fast-speed and low-cost method to prepare the customizable graphene-based patterns in complex configurations with exceptional electrical performance. This paper presents the applications of LIG formed on the commercial polyimide (PI) film as the stretchable strain sensor and electrical-actuated actuators. First, the conductive performances of the LIG were systematically revealed under different fabrication conditions via investigating the effects of processing parameters, and the fluence of the laser was experimentally demonstrated as the only crucial parameter to evaluate the LIG formation, facilitating the selection of optimized manufacturing parameters to prepare the LIG with desired electrical performances. Then, the LIG-based strain sensor which can undergo over 50% tensile strain was fabricated by transfer of the LIG from the PI film to polydimethylsiloxane. The variety of LIG-based electro-thermal actuators to achieve pre-designed 3D architectures was presented, along with their parameter analysis. After incorporating the multimeter system, the actuator can even feedback its transformation from 2D precursor to 3D architecture by monitoring the resistance variation of LIG, revealing the integrated capability of our design in serving as sensors and actuators. Finally, the wearable glove with the LIG sensors was presented to demonstrate its ability to remotely control the soft robotic hand.

Screening Biophysical Sensors and Neurite Outgrowth Actuators in Human Induced-Pluripotent-Stem-Cell-Derived Neurons.

All living cells maintain a charge distribution across their cell membrane (membrane potential) by carefully controlled ion fluxes. These bioelectric signals regulate cell behavior (such as migration, proliferation, differentiation) as well as higher-level tissue and organ patterning. Thus, voltage gradients represent an important parameter for diagnostics as well as a promising target for therapeutic interventions in birth defects, injury, and cancer. However, despite much progress in cell and molecular biology, little is known about bioelectric states in human stem cells. Here, we present simple methods to simultaneously track ion dynamics, membrane voltage, cell morphology, and cell activity (pH and ROS), using fluorescent reporter dyes in living human neurons derived from induced neural stem cells (hiNSC). We developed and tested functional protocols for manipulating ion fluxes, membrane potential, and cell activity, and tracking neural responses to injury and reinnervation in vitro. Finally, using morphology sensor, we tested and quantified the ability of physiological actuators (neurotransmitters and pH) to manipulate nerve repair and reinnervation. These methods are not specific to a particular cell type and should be broadly applicable to the study of bioelectrical controls across a wide range of combinations of models and endpoints.

Observer-Based Fault Reconstruction and Fault-Tolerant Control for Nonlinear Systems Subject to Simultaneous Actuator and Sensor Faults

In this article, we pay our attention on exploring observer-based actuator fault and sensor fault reconstruction associated with fault-tolerant control (FTC) for nonlinear systems, which are approximated by the Takagi–Sugeno (T-S) fuzzy method. By designing a brand-new unknown input observer (UIO), unknown state, sensor, and actuator faults can be reconstructed simultaneously, where some constraints imposed on the actuator fault such as the first derivative of fault being equal to zero required in the previous results are not needed in our work. With the support of this estimation information, a FTC scheme is well established, by which the system may recover its performance even in the occurrence of faults. Another contribution of the developed method is that all the stability criteria are deduced via fuzzy Lyapunov functions. It makes the obtained conditions more relaxed than the ones derived by quadratic Lyapunov functions. Finally, simulation results conducted on two practical dynamics are provided to show the validity of the achieved procedure.

High resolution atomic force microscopy with an active piezoelectric microcantilever.

Active microcantilevers with on-chip sensing and actuation provide significant advantages in tapping mode Atomic Force Microscopy (AFM). Collocated transduction allows for effective manipulation of cantilever dynamics through feedback control, enabling higher scan rates. However, the adjacency of the sensing and actuation electrodes is known to result in a high level of feedthrough, leading to a low imaging resolution. Readout circuit noise further deteriorates the imaging precision. Here, we investigate the noise sources that affect AFM microcantilevers with collocated aluminum nitride (AlN) actuator-sensor pairs. We reported these cantilevers in earlier work and demonstrated that they display a very low level of feedthrough between the actuation and sensing electrodes. We present a high signal-to-noise ratio (SNR) sensing method that enables us to demonstrate high-resolution AFM on a calibration grating with nm-step silicon carbide (SiC) terraces. Measuring the Lorentzian response of the cantilever's Brownian motion with the on-chip active sensor at resonance enables us to calibrate the dynamic stiffness at the first fundamental resonance mode, without utilizing an optical sensor.

An Intelligent Controlling and Monitoring of Green House Effect for Indian Farmers

In this we propose wireless sensor based IoT system for greenhouse to control and monitoring parameter. By using this methodology, we will support our farmers to increase the growth of plants in different variety of field. An implementation of this system for greenhouse controlling and monitoring uses parameter such as, light intensity, temperature, soil moisture, relative humidity. This proposed work we will help to control and monitor these parameters which are sensed by different types of sensors like; light sensor, temperature sensor, soil moisture sensor, humidity sensor. The implementation is involving a wireless sensor system (WSS) which is based on IoT in greenhouse controlling and monitoring. In case of wireless communication, we used ESP8266E for create sensor node and actuator node for monitor parameters, sending by a wireless connection to computer via Wi-Fi communication, and give suitable command to the end device to overcome the variation of environmental parameter inside greenhouse. These implementations help farmer to grow plants in non-farmable condition of plants.

Iterative learning control for hyperbolic distributed parameter systems based on sensor–actuator networks

AbstractThis paper proposes two kinds of iterative learning control (ILC) schemes for a class of the distributed parameter systems based on sensor–actuator networks which can be described by hyperbolic partial differential equations. A D‐type ILC algorithm is first considered and the convergent condition of the output error is obtained via the contraction mapping methodology. Then, the PD‐type ILC algorithm is considered in this hyperbolic distributed parameter systems based on sensor–actuator networks. Finally, a cable equation with air and structural damping is given to illustrate the effectiveness of the proposed methods.

Bioinspired Flexible Film as Intelligent Moisture-Responsive Actuators and Noncontact Sensors

Inspired by nature organisms, flexible moisture-responsive actuators and sensors fabricated by hydrophilic 2D materials have attracted increased demands by utilizing water in a green and renewable way. Graphene oxide (GO) and MXene have shown great potential in moisture-responsive actuators and sensors due to their superior properties and strong interaction with water. Bioinspired actuators with nacre-like structure have been successfully fabricated by anchoring MXene and GO into bacterial cellulose fibers, which exhibit excellent moisture-driven performance with superior mechanical properties and ambient antioxidation. The flexible actuator demonstrates sensitive response to moisture with intensive deformation. It is demonstrated that the moisture-responsive smart devices are able to function as an electrical switch and robotic gripper. Furthermore, a noncontact control system can also be implemented via monitoring the respiration and the humidity on the human skin surface. Devices with effective and sensitive response to solvent and temperature have also been demonstrated by the fabricated composite film. This work overcomes the shortcomings of the actuators prepared by MXene, offering a facile approach to fabricate flexible moisture-responsive actuators and sensors with wider applications.

Attitude adaptive control of satellites using double-gimbal magnetically suspended control moment gyros

The paper deals with the control of the satellite attitude by means of double-gimbal magnetically suspended control moment gyros (DGMSCMGs). In its first part, the paper presents the dynamics of the DGMSCMGs and the dynamics of the satellite equipped with a DGMSCMG actuator that creates a total satellite applied gyroscopic torque which is generated by the angular rate of the satellite and the angular rates applied to the gyroscopic gimbals. Then, an adaptive satellite attitude control system is designed with actuator and angular rate sensor (both DGMSCMG type), based on the use of the dynamic inversion control technique and a neural network; the latter models an adaptive component of the control law to compensate the negative influence of the dynamic inversion error. The attitude controller mainly consists of a dynamic compensator, a neural network, and a linear observer (for the estimation of the neural network training vector). The novel attitude control architecture is validated in Matlab/Simulink; the analysis of both theoretical concepts and obtained results prove the cancel of the convergence errors, an excellent angular rate precision, as well as robustness to external disturbances.

Finite-time asynchronous [formula omitted] fault-tolerant control for nonlinear hidden markov jump systems with actuator and sensor faults

This paper studies the H∞ fault-tolerant control problem for a class of conic-type nonlinear Markov jump systems with sensor and actuator faults as well as unknown disturbances. Firstly, the hidden Markov model is introduced to deal with the asynchronous problem existing in the control systems. By selecting suitable Lyapunov-Krasovskii function, linear matrix inequalities techniques are applied to obtain the H∞ fault-tolerant controller. A new condition of the state feedback H∞ fault-tolerant controller with actuator faults and sensor faults is presented. The proposed control strategy can not only ensure the finite-time boundness of the closed loop system, but also guarantee the required H∞ performance. Lastly, the availability of the design scheme is validated by a simulation example.

Adaptive Resilient Control of Cyber-Physical Systems Under Actuator and Sensor Attacks

Resilient control of cyber-physical systems (CPSs) against actuator and/or sensor attacks has been extensively researched. However, the existing research considers actuator attacks and sensor attacks separately and also designs resilient controllers based on complex nonlinear system models caused by unknown actuator and sensor attacks. This increases the difficulty in the analysis, computation, and control of CPSs under attacks. To address this issue, this article introduces an idea to deal with both actuator attacks and sensor attacks together with feedback linearization control. This simplifies the mathematical modeling of attacked CPSs, thus reducing the difficulty of resilient controller design. Then, from the simplified modeling, a composite controller is designed to enhance system resilience. It ensures the dynamic and steady-state performance of CPSs under attacks. Simulation studies are undertaken to demonstrate the effectiveness of the proposed method.

Assisted Navigation Algorithm for Wireless Sensor Actuator and Robot Networks

Wireless Sensor, Actuator and Robot Networks (WSARNs) are made of mobile and static sensor nodes that interact in order to collaboratively perform specific tasks, such as supporting assisted navigation for mobile robotic nodes that carry out requested operations in hostile environments, where the human presence is impracticable. In this regard, it is worth noting that assisted navigation algorithms have a highly dynamic nature, and are implemented by sensor nodes that are characterized by limited transmission power and lean autonomy in terms of computing and memory capacity. This paper presents an improved version of the assisted navigation algorithm based on the concept of “credit field”. The main aim of the proposed algorithm is to reduce and balance the energy consumption among the static sensor nodes when running the algorithm to manage the presence of obstacles and adversary areas, thus extending the lifetime of WSARNs. The algorithm has been tested on a hybrid sensor network that employs Mica2 Motes as static sensor nodes and Lego Mindstorms robots integrated with a Stargate board developed by Crossbow as mobile nodes.

Enabling Tunable Water‐Responsive Surface Adaptation of PDMS via Metal–Ligand Coordinated Dynamic Networks

AbstractPolymers are at the core of emerging flexible sensor and soft actuator technology. Ideal candidates not only respond to external stimuli but also have programmable response intensity and speed. Incorporating dynamic interactions into polymers has been widely studied. However, most research has focused on synthesis methods and on optical and mechanical effects of these interactions. Here, a new and tunable method of enabling environmentally adaptive polymers are introduced. Specifically, polar functionalities are “hidden” within polydimethylsiloxane (PDMS). When unveiled, these polar functionalities change the hydrophilicity of the polymer. Water is the stimulus that triggers the exposure of polar functionalities by disrupting interfacial equilibrium and driving surface reconstruction. This reversible adaptation is governed by the dynamics of a metal–ligand coordinated polymer, and therefore can be easily tailored by the choice of network structure, metal cations, and counter anions. PDMS is a particularly exciting polymer for this functionality because of the negative consequences of its intrinsically high hydrophobicity in many applications. However, this design concept is applicable to a wide range of polymers and can be expanded to other dynamic interactions such as reversible covalent bonding, hydrogen bonding, and ionic bonding, to empower programmable responsive functionality and therefore enhance material performance.

Single channel digital controller design for a high spinning rate rolling airframe missile

AbstractSpinning/Rolling Airframe Missiles (RAM) mostly use an analog (ON-OFF type) control approach that deflects control surfaces (fins) at minimum and maximum positions continuously, and control is achieved by applying phase shift between the minimum and maximum deflections during a complete roll cycle. Therefore, the control signal shape changes continuously during the roll cycle. In this study, a novel single channel digital controller is designed and tested for a high spinning rate (10–20 Hz) RAM. The digital controller adjusts the amplitude of fin deflections instead of applying a phase shift. In this way, control signal shapes are predetermined in design to completely decouple yaw and pitch dynamics. At the beginning of each roll cycle, the algorithm decides on the control signal shape and amplitude to apply throughout the cycle. Delays on the actuator system and sensor measurements which might lead to instability at high spinning rates are handled effectively thanks to the predetermined control signal shapes that are changing with 90-degree intervals. Detailed geometry of a surface-to-air spinning missile is used to obtain aerodynamic coefficients for the entire flight regime (i.e. from launch to terminal phase) via Missile DATCOM. The 6-DOF flight dynamics model and the controller algorithms are built in MATLAB/Simulink environment. The proposed digital controller is tested systematically for various scenarios and the performance is compared with the conventional analog control approach. The digital controller gives better performance compared to the analog approach under the influence of servo delays and sensor noise.

Neural Network Based Finite-Time Attitude Tracking Control of Spacecraft With Angular Velocity Sensor Failures and Actuator Saturation

This article investigates the finite-time attitude tracking control problem of spacecraft with angular velocity sensor failures and actuator saturation. A model-free angular velocity observer is proposed first to reconstruct the unmeasurable angular velocity in finite time. Invoking the reconstructed information, a saturated finite-time attitude tracking controller is then designed via the fast terminal sliding mode control technique, while the Legendre polynomial-based neural network is incorporated to approximate the unknown nonlinear dynamics. The finite-time stability of the closed-loop attitude tracking system from the proposed observer and the controller is proved despite the external disturbances. The main feature of the proposed controller is that it can accomplish the attitude tracking maneuver without model and angular velocity information. Numerical and experimental results are presented to verify the effectiveness of the proposed scheme.