Sensor Actuator Research Articles

Based on STM32 SCM Intelligent Greenhouse

With the continuous growth of the global population and the continuous development of agricultural production, there are many problems in the traditional agricultural production mode, such as resource waste, low production efficiency, environmental pollution, and the urgent need for innovative solutions to meet the growing food demand and sustainable development goals. This paper introduces an intelligent greenhouse system based on STM 32 single-chip microcomputer, using advanced technology and automation functions, in improving the efficiency, quality and sustainability of agricultural production. The hardware composition of the intelligent greenhouse system includes STM 32 MCU, various sensor actuators, ESP8266 module and user interface module.

Method of Construction of Greenhouse Infrastructure for Sensor Data Acquation and Actuator Control in Smart Farm

Method of Construction of Greenhouse Infrastructure for Sensor Data Acquation and Actuator Control in Smart Farm

Using reinforcement learning to autonomously identify sources of error for agents in group missions

When deploying agents to execute a mission with collective behavior, it is common for accidental malfunctions to occur in some agents. It is challenging to distinguish whether these malfunctions are due to actuator failures or sensor issues based solely on interactions with the affected agent. However, we humans know that if we cause a group behavior where other agents collide with a suspected malfunctioning agent, we can monitor the presence or absence of a positional change and identify whether it is the actuator (position changed) or the sensor (position unchanged) that is broken. We have developed artificial intelligence that can autonomously deploy such “information acquisition strategies through collective behavior” using machine learning. In such problems, the goal is to plan collective actions that result in differences between the hypotheses for the state [e.g., actuator or sensor]. Only a few of the possible collective behavior patterns will lead to distinguishing between hypotheses. The evaluation function to maximize the difference between hypotheses is therefore sparse, with mostly flat values across most of the domain. Gradient-based optimization methods are ineffective for this, and reinforcement learning becomes a viable alternative. By handling this maximization problem, our reinforcement learning surprisingly gets the optimal solution, resulting in collective actions that involve collisions to differentiate the causes. Subsequent collective behaviors, reflecting this situation awareness, seemed to involve other agents assisting the malfunctioning agent.

DMATCH: A novel integrated testing method for floating wind turbine dynamics – Potential error and its impact

With floating offshore wind turbines (FOWT) steadily advancing towards large-scale deployments in deep water, the traditional wave basin model testing method shows its shortage to fulfill the testing requirement. To enhance the effectiveness of model tests for FOWT, this paper introduces a distributed model testing method, dMATCH (distributed-Mooring-Aerodynamics-Testing-Control-Hydrodynamics). The dMATCH method separates the experiment into aerodynamic testing in the wind tunnel and hydrodynamic testing in the wave basin, achieving integrated coupling through real-time data transmission using a data exchange system. Errors in sensor acquisition and actuator actuation during data communication may impact the accuracy of experiments. To investigate the potential error and its impact within dMATCH, an integrated model testing system in the wave basin was constructed. Four load cases were established, introducing varying magnitudes of normally distributed random errors to simulate acquisition and actuation inaccuracies. The investigation revealed a positive correlation between errors in experimental results and those in acquisition and actuation. As error increased, result fluctuations became more pronounced. Within the current experimental framework, acquisition error had a greater influence on results compared to actuation error, with aerodynamic thrust being more sensitive to error than the pitch and surge motions of platform. When acquisition and actuation errors were controlled below 5%, the relative RMSE of the results remained below 5%. The dMATCH method proposed in this paper offers a new approach for integrated model testing of FOWT, and the study of potential error impact is crucial to ensuring the reliability of this innovative testing method.

Optimal Design of a Sensor Network for Guided Wave-Based Structural Health Monitoring Using Acoustically Coupled Optical Fibers.

Guided waves (GW) allow fast inspection of a large area and hence have received great interest from the structural health monitoring (SHM) community. Fiber Bragg grating (FBG) sensors offer several advantages but their use has been limited for the GW sensing due to its limited sensitivity. FBG sensors in the edge-filtering configuration have overcome this issue with sensitivity and there is a renewed interest in their use. Unfortunately, the FBG sensors and the equipment needed for interrogation is quite expensive, and hence their number is restricted. In the previous work by the authors, the number and location of the actuators was optimized for developing a SHM system with a single sensor and multiple actuators. But through the use of the phenomenon of acoustic coupling, multiple locations on the structure may be interrogated with a single FBG sensor. As a result, a sensor network with multiple sensing locations and a few actuators is feasible and cost effective. This paper develops a two-step methodology for the optimization of an actuator-sensor network harnessing the acoustic coupling ability of FBG sensors. In the first stage, the actuator-sensor network is optimized based on the application demands (coverage with at least three actuator-sensor pairs) and the cost of the instrumentation. In the second stage, an acoustic coupler network is designed to ensure high-fidelity measurements with minimal interference from other bond locations (overlap of measurements) as well as interference from features in the acoustically coupled circuit (fiber end, coupler, etc.). The non-sorting genetic algorithm (NSGA-II) is implemented for finding the optimal solution for both problems. The analytical implementation of the cost function is validated experimentally. The results show that the optimization does indeed have the potential to improve the quality of SHM while reducing the instrumentation costs significantly.

Investigation of Innovative High-Response Piezoelectric Actuator Used as Smart Actuator–Sensor System

This paper presents an experimental analysis of a high-response piezoelectric actuator system for the modular design of hydraulic digital fluid control units. It focuses on determining static and dynamic characteristics, forming the basis for developing a smart Industry 4.0 component that incorporates both actuator and sensor function. The design process examines the main challenges, advantages, disadvantages, and working principles to define parameters that impact the actuator’s behaviour and performance. The new piezoelectric actuator system features three piezoelectric stack actuators in series, enabling simultaneous actuation and sensing by applying and measuring the electrical voltage at each piezo element. The experimental setup and test methodology are explained in detail, revealing that the new design, combined with an appropriate open-loop or closed-loop control method, offers superior actuator stroke control, high stroke resolution, and a high-dynamic step response. This paper proposes a concept of a smart piezo actuator system focused on I4.0 and an actuator administration shell, integrated with 5G and RFID technology, which will allow automatic plug-and-play functionality and efficient interconnection, communication, and data transfer between the hydraulic valve and the piezoelectric actuator system.

Tactile and kinesthetic communication glove with fusion of triboelectric sensing and pneumatic actuation

Dexterous manipulation of hand plays a crucial role in daily life, industrial productivity, and research laboratory, etc. Evolution of humanoid robot and virtual training technique raise the demand of human machine interface with fusion of both sensing and feedback functions to realize the close loop interactions. Here, we present a modular glove which combines triboelectric sensor and pneumatic actuator for enabling multi-modal and multi-dimensional sensing and feedback. Owing to commonalities of materials and structural design, pneumatic actuator and triboelectric sensor can be seamlessly merged. The integration of fingertip module and finger module offers cutaneous and kinesthetic feedback with the corresponding sensing functions. The whole glove can perform sensing and actuation for most of the interactive activities, such as touching, twisting, grabbing, rubbing, etc. With the customized wearable signal processing and control system, the robotic hand manipulation and virtual training program were demonstrated to verify the capabilities of improving the tactile perception in different scenarios. In general, the proposed device with a minimalistic design and multi-modal sensing and feedback can improve the issue of missing or mismatch of tactile communication in robotic teleoperation, virtual training, and rehabilitation.

True Random Number Generator Based on Chaotic Oscillation of a Tunable Double-Well MEMS Resonator.

Chaotic systems have aroused interest across various scientific disciplines such as physics, biology, chemistry, and meteorology. The deterministic but unpredictable nature of a chaotic system is an ideal feature for random number generation. Microelectromechanical systems (MEMS) are a promising technology that effectively harnesses chaos, offering advantages such as a compact footprint, scalability, and low power consumption. This paper presents a true random number generator (TRNG) based on a double-well MEMS resonator integrated with an actuator and position sensor. The potential energy landscape of the proposed MEMS resonator is actively tunable with a direct current voltage. Experimental demonstrations of tunable bistability and chaotic resonance are reported in this paper. A chaotic time sequence is generated through piezoresistive sensing of the position of the MEMS resonator once it is driven into the chaotic regime. Subsequently, the randomness of the bit sequence, achieved by applying the exclusive or function to a digital chaotic sequence and its delayed differential is confirmed to meet the National Institute of Standards and Technology specifications. Moreover, the throughput and energy efficiency of the proposed MEMS-based TRNG can be adjusted from 50 kb s-1 and 0.44 pJ per bit at a low energy barrier to 167 kb s-1 and 6.74 pJ per bit at a high energy barrier by changing the MEMS device's potential well. The tunability of the proposed double-well MEMS resonator not only offers continuous adjustments in the energy efficiency of TNRG but also unveils vast and diverse research opportunities in analog computing, encryption, and secure communications.

Engineered sensor actuator modulator as aqueous humor outflow actuator for gene therapy of primary open-angle glaucoma

Glaucoma, a blinding eye disease with optic neuropathy, is usually associated with elevated intraocular pressure (IOP). The currently available pharmacological and surgical treatments for glaucoma have significant limitations and side effects, which include systemic reactions to medications, patient non-compliance, eye infections, surgical device failure, and damage to the eye. Here, we present Sensor-Actuator-Modulator (SAM), an engineered double mutant version of the bacterial stretch-activated mechanosensitive channel of large conductance (MscL) that directly senses tension in the membrane lipid bilayer of cells and in response, transiently opens its large nonspecific pore to release cytoplasmic fluid. The heterologously expressed mechanosensitive SAM channel acts as a tension-activated pressure release valve in trabeculocytes. In the trabecular meshwork (TM), SAM is activated by membrane stretch caused by elevated IOP. We have identified several SAM variants that are activated at physiologically relevant pressures. Using this barogenetic technology, we have demonstrated that SAM is functional in cultured TM cells, and successfully transduced in vivo in TM cells by use of AAV2/8. Further, it is effective in enhancing aqueous humor outflow facility leading to lowering the IOP in a mouse model of ocular hypertension.

Adaptive neural network dynamic event-triggered consensus control for nonlinear multi-agent systems subject to sensor deception attacks and actuator faults

Adaptive neural network dynamic event-triggered consensus control for nonlinear multi-agent systems subject to sensor deception attacks and actuator faults

Adjustable order fault estimation observer based fault-tolerant control for switched fuzzy stochastic systems

Based on fault estimation, fault-tolerant control problem of switching stochastic nonlinear systems with actuator fault and sensor fault are studied. The fuzzy models are invited to solve the nonlinear problems of the systems. The fault estimation observer is designed to estimate the system states, actuator and sensor faults. The order of observer can be adjusted, which can improve the design flexibility and realize a compromise between the estimated cost and estimated accuracy. Unlike some classical results, the output differential is not required in the observer, which increases the application range. Based on the observer, the fault-tolerant controller is proposed, besides, the sufficient condition is achieved to ensure that the closed-loop system is mean-squared exponential stability. Finally, the feasibility is verified by two simulations.

Synthesis of Distributed Covert Sensor–Actuator Attackers

Synthesis of Distributed Covert Sensor–Actuator Attackers

Optimization of Sensor Placement for a Measurement System for the Determination of Local Magnetic Material Properties

The aim of this work is to optimize the sensor positions of a sensor–actuator measurement system for identifying local variations in the magnetic permeability of cut steel sheets. Before solving the actual identification problem, i.e., finding the material distribution, the sensor placement of the measurement setup should be improved in order to reduce the uncertainty of the identification of the material distribution. The Fisher information matrix (FIM), which allows one to quantify the amount of information that the measurements carry about the unknown parameters, is used as the main metric for the objective function of this design optimization. The forward problem is solved by the finite element method. The results show that the proposed method is able to find optimal sensor positions as well as the minimum number of sensors to achieve a desired maximum parameter uncertainty.

L1 Adaptive Fault-Tolerant Control for Nonlinear Systems Subject to Input Constraint and Multiple Faults

This paper investigates an L1 adaptive fault-tolerant control scheme for nonlinear systems with input constraint, external disturbances, and multiple faults, which include actuator faults and sensor faults. Faults and input constraint are important factors that affect the stability and performance of a control system. Actuators and sensors are the most vulnerable components, with the former receiving more attention in comparison. In this paper, sensor faults are first transformed into pseudo-actuator faults through the augmented matrix approach, which facilitates their handling together with actuator faults. Saturation constraints on the control signal are not conducive to the design of the controller. The conversion of an input-saturated function to a time-varying linear system is completed based on function approximation and Lagrange’s mean value theorem. Moreover, a nonlinear system with unknown input gain and uncertainties is constructed using these methods. Next, an L1 adaptive fault-tolerant controller is designed to cope with uncertainties, including system uncertainties, external disturbances, faults, and approximation errors. In the L1 adaptive controller, the online estimation of the time-varying parameters allows for updating of the system state, while the combination of the two is transmitted to the control law such that it can compensate for the effects of the uncertainties. The stability and performance boundaries are further derived using the Lyapunov theory and the L1 reference system. Finally, simulations are carried out to demonstrate the effectiveness of the proposed controller.

Development of a Novel Soft Tissue Measurement Device for Individualized Finite Element Modeling in Custom-Fit CPAP Mask Evaluation.

Individual facial soft tissue properties are necessary for creating individualized finite element (FE) models to evaluate medical devices such as continuous positive airway pressure (CPAP) masks. There are no standard tools available to measure facial soft tissue elastic moduli, and techniques in literature require advanced equipment or custom parts to replicate. We propose a simple and inexpensive soft tissue measurement (STM) indenter device to estimate facial soft tissue elasticity at five sites: chin, cheek near lip, below cheekbone, cheekbone, and cheek. The STM device consists of a probe with a linear actuator and force sensor, an adjustment system for probe orientation, a head support frame, and a controller. The device was validated on six ballistics gel samples and then tested on 28 subjects. Soft tissue thickness was also collected for each subject using ultrasound. Thickness and elastic modulus measurements were successfully collected for all subjects. The mean elastic modulus for each site is Ec = 53.04 ± 20.97kPa for the chin, El = 16.33 ± 8.37kPa for the cheek near lip, Ebc = 27.09 ± 11.38kPa for below cheekbone, Ecb = 64.79 ± 17.12kPa for the cheekbone, and Ech = 16.20 ± 5.09kPa for the cheek. The thickness and elastic modulus values are in the range of previously reported values. One subject's measured soft tissue elastic moduli and thickness were used to evaluate custom-fit CPAP mask fit in comparison to a model of that subject with arbitrary elastic moduli and thickness. The model with measured values more closely resembles in vivo leakage results. Overall, the STM provides a first estimate of facial soft tissue elasticity and is affordable and easy to build with mostly off-the-shelf parts. These values can be used to create personalized FE models to evaluate custom-fit CPAP masks.

Monitoring and Reconstruction of Actuator and Sensor Attacks for Lipschitz Nonlinear Dynamic Systems Using Two Types of Augmented Descriptor Observers

Fault data injection attacks may lead to a decrease in system performance and even a malfunction in system operation for an automatic feedback control system, which has motive to develop an effective method for rapidly detecting such attacks so that appropriate measures can be taken correspondingly. In this study, a secure descriptor estimation technique is proposed for continuous-time Lipschitz nonlinear cyber physical systems affected by actuator attacks, sensor attacks, and unknown process uncertainties. Specifically, by forming a new state vector composed of original system states and sensor faults, an equivalent descriptor dynamic system is built. A proportional and derivate sliding-mode observer is presented so that the system states, sensor attack, and actuator attack can be reconstructed successfully. The observer gains are obtained by using linear matrix inequality to secure robustly stable estimation error dynamics. Moreover, a robust descriptor fast adaptive observer estimator is presented as a complement. Finally, the efficacy levels of the proposed design approaches are validated using a vertical take-off and landing aircraft system. Comparison studies are also carried out to assess the tracking performances of the proposed algorithms.

Distributed Adaptive Secure Consensus Tracking Control for Asynchronous Switching Nonlinear MASs Under Sensor Attacks and Actuator Faults

A distributed adaptive secure control algorithm is proposed for asynchronous switching nonlinear multi-agent systems (MASs) in pure-feedback form while considering the sensor attacks and actuator faults, which eliminates the effect of attack signals, actuator faults and uncertainties without knowing the sign of the control gain functions. Since the system states measured by the sensors are corrupted by the malicious attacker, only the post-attack system states can be obtained. Thus, an improved coordinate transformation is constructed by using the post-attack system states and a backstepping method based on the improved coordinate transformation is developed to solve the consensus tracking control problem of the researched systems under sensor attacks. The common Lyapunov function is designed to ensure the stability of all asynchronous switching subsystems of each agent. In the end, a simulation example is presented to illustrate the theoretical results’ validity. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper deal with the distributed adaptive secure consensus tracking control problem for asynchronous switching nonlinear MASs, whose models can describe many phenomena in nature such as biological processes, robot team formation and sensor networks. It is extremely challenging to achieve the tracking control problem of the researched asynchronous switching nonlinear system in an unsafe network environment, where the unknown sensor attacks signals, actuator faults coefficients, and randomly switched non-affine nonlinearities exist together for the followers. Furthermore, the control design and stability analysis of the researched system is based on the common Lyapunov approach, which makes the developed approach more engineering oriented.

Predicting damage location information based on single actuator sensor path information using regularised multilinear regression

In structural health monitoring with guided ultrasonic waves, probability reconstruction algorithms are a method to locate a damage. They work by calculating the probability for each actuator sensor path on whether there is a damage on the path or not. By superposition of each path and its damage probability, a damage localisation is done. The disadvantage of this is, that the damage localisation resolution is limited by the number of paths crossing each other. To overcome this, the hypothesis of this investigation is that the information of a path can not only be used to determine whether a damage is present, but that additional information about the location within the path can be calculated as well. This way a localisation resolution can be higher than by only relying on the path density. To verify this assumption, an experimental setup was chosen in which the path lengths always remain the same while the distance between damage and the direct path varies. This is implemented by a moving ultrasonic microphone simulating the sensor. The varying distance is the local information, which is determined in this study using the information of a single path. For this purpose, a prediction is calculated using regularised multilinear regression. The input features are characteristic values of five sections of the sensor signal in the time domain. The sections are manually chosen based on arriving wave events. The result confirms the hypothesis. Therefore, it is plausible to increase the detection resolution of probability reconstruction algorithms by calculating damage location estimations for each path.

Quantifying the Reliability Performance of a Guided Wave-Based SHM System Using Piezoelectric Wafer Actuator Sensors

The adoption of structural health monitoring systems can bring important benefits to the aerospace industry. However, the current implementation of SHM technologies in today's aerospace industry remains limited. One of the primary obstacles hindering the adoption of SHM technologies is the challenge associated with assessing the reliability of SHM systems. In aerospace applications, SHM hardware is exposed to harsh environments, including high or low temperatures, vibrations, moisture, and radiation. Consequently, the long-term performance of SHM systems is adversely affected. This study delves into various issues related to the evaluation of SHM system reliability and performance quantification, placing special emphasis on guided wave-based SHM using Piezoelectric Wafer Actuator Sensors. We present experimental and numerical modeling examples designed to illustrate the detrimental effects of aging at the unitary sensor and sensor network levels. Conclusions are drawn regarding the current challenges in quantifying system reliability, emphasizing relevant performance metrics such as the probability of detection, localization, and false call rates.

Finite-Time Fuzzy Adaptive Output Feedback Resilient Control of Nonlinear Cyber-Physical Systems with Sensor Attacks and Actuator Faults

Finite-Time Fuzzy Adaptive Output Feedback Resilient Control of Nonlinear Cyber-Physical Systems with Sensor Attacks and Actuator Faults