Position Sensor Research Articles

Reliability Of Acceleration Measures Using A Linear Position Transducer During Segments Of The Bench Press

Reliability Of Acceleration Measures Using A Linear Position Transducer During Segments Of The Bench Press

Reliability Of Acceleration Assessment Using A Linear Position Transducer During Segmental Thirds Of The Squat

Reliability Of Acceleration Assessment Using A Linear Position Transducer During Segmental Thirds Of The Squat

Разработка контроллера электромеханического датчика положения

Electromechanical position sensors, including sine-cosine rotary transformers, compared to sensors based on other physical principles, have greater reliability, a simple design and resistance to difficult operating conditions. This makes them attractive for use in various motion control systems that require measurement of the position of the actuator axis. However, the use of such sensors requires special hardware and software devices – controllers that ensure their operation. This article is devoted to the development of such a controller, which generates, captures, and processes signals of the sine-cosine rotary transformer. When developing the controller of the sine-cosine rotary transformer, methods of system analysis of electrical, electromagnetic and information processes and means of their implementation, methods of software development, as well as methods of experimental research were applied. A description of the controller of a sine-cosine rotary transformer in phase mode is given, taking into account the hardware and computing resources of the microcontroller which implements it. At the same time, physical processes are correlated with information processes associated with the generation, capture and procession of signals. The reasons for the decrease in the position measurement accuracy are revealed. Various technical solutions have been applied to take into account and eliminate them. Among them are the coordination of physical and information processes, taking into account the execution time of calculations by the microcontroller, analysis of the correctness of the position measurement results and their smoothing according to moving average method. The controller software has been developed, which can serve as a typical element for microprocessor-based control systems. Experimental results are obtained, illustrating the achievement of the development result. Application of various technical solutions has made it possible to ensure the accuracy of position measurement up to a tenth of a degree with a high intensity of information updating. As a result of the study, a detailed and comprehensive solution of the developing a controller for a sine-cosine rotating transformer problem is given, which is the scientific and practical value of the materials presented in the article. The results obtained can be applied in various motion control systems and serve as the basis for the development of controllers for various electromechanical position sensors.

The contribution of muscle spindles to position sense measured with three different methods

The sense of limb position is important, because it is believed to contribute to our sense of self-awareness. Muscle spindles, including both primary and secondary endings of spindles, are thought to be the principal position sensors. Passive spindles possess a property called thixotropy which allows their sensitivity to be manipulated. Here, thixotropic patterns of position errors have been studied with three commonly used methods of measurement of position sense. The patterns of errors have been used as indicators of the influence exerted by muscle spindles on a measured value of position sense. In two-arm matching, the blindfolded participant indicates the location of one arm by placement of the other. In one-arm pointing, the participant points to the perceived position of their other, hidden arm. In repositioning, one of the blindfolded participant’s arms is placed at a chosen angle and they are asked to remember its position and then, after a delay, reproduce the position. The three methods were studied over the full range of elbow angles between 5° (elbow extension) and 125° (elbow flexion). Different outcomes were achieved with each method; in two-arm matching, position errors were symmetrical about zero and thixotropic influences were large, while in one-arm pointing, errors were biased towards extension. In repositioning, thixotropic effects were small. We conclude that each of the methods of measuring position sense comprises different mixes of peripheral and central influences. This will have to be taken into consideration by the clinician diagnosing disturbances in position sense.

Develop an ECU for Controlling Fuel Injectors Using Arduino and LabVIEW for Training Purposes

The technology of electronic motor control is now advancing rapidly. Many studies and applications have been done on automatic system control in vehicles. The automatic fuel injection control system for automobile engines is a unique area of study and instruction in universities. The engine will run more efficiently, use less fuel, and emit fewer emissions when the fuel injection system is precisely controlled. It is challenging for student research to evaluate improvements to engine performance on real engine controller units (ECUs). Since their ECUs are already set for specific automotive duties, it is challenging to run tests by modifying the ECU's input parameters. In this study, a programmable ECU was created to explore the characteristics of the fuel injection system using the open-source, low-cost Arduino microcontroller. Using LabVIEW software, the algorithmic program computes and graphically simulates the injector characteristics. This study determines the injector opening time and fuel volume injected using the data from the intake manifold pressure sensor, lambda signal, engine temperature, throttle position, engine speed, and exhaust gas sensor. The primary objective of this study was not to replace a real ECU. It was more beneficial for students to be able to equip themselves with tools and apply methodologies to perform many experiments at home rather than utilizing commercially available ECUs at exorbitant rates, thanks to the development of a low-cost ECU based on Arduino and LabVIEW in this study.

Performance analysis of PID controller-based metaheuristic optimisation algorithms for BLDC motor

ABSTRACT Today, the use of permanent magnet brushless DC (PMBLDC) motors in vehicles is increasing due to the characteristics of sensorless operation. PMBLDC motor controllers can control the speed and position in the closed-loop feedback system without the need for a position sensor mounted on the shaft. Proportional – Integral – Derivative (PID) controller is one of the most common feedback control algorithms used in PMBLDC motor controllers. Optimizing problems using deterministic methods such as Lagrange and simplex methods requires basic information and complex calculations. Meta-heuristic algorithms are a type of stochastic algorithm that is used to find the optimal solution. Meta-heuristic algorithms are divided into three general categories: evolutionary algorithms, swarm intelligence algorithms, and stochastic algorithms. In this paper, using 14 metaheuristic optimisation algorithms, PID control parameters including settling time, time rise, overshoot, and stability of step response of the mentioned system are optimised. In this paper, 14 meta-heuristic algorithms are simulated and evaluated to optimise the PID coefficients of the controller, including settling time, rising time, excessive increase, and step response stability. The simulation result shows that the genetic algorithm (GA) has the best performance in terms of cost function and biogeography-based optimisation (BBO) in terms of settling time and rising time parameters. Finally, the simulation results are confirmed using experimental results.

Innovative Smart Drilling with Critical Event Detection and Material Classification

This work presents a cyber-physical drilling machine that incorporates technologies discovered in the fourth industrial revolution. The machine is designed to realize its state by detecting whether it hits or breaks through the workpiece, without the need for additional sensors apart from the position sensor. Such self-recognition enables the machine to adapt and shift the controllers that handle position, velocity, and force, based on the workpiece and the drilling environment. In the experiment, the machine can detect and switch controls that follow the drilling events (HIT and BREAKHTROUGH) within 0.1 and 0.5 s, respectively. The machine’s high visibility design is beneficial for classification of the workpiece material. By using a support-vector-machine (SVM) on thrust force and feed rate, the authors are seen to achieve 92.86% accuracy for classification of material, such as medium-density fiberboard (MDF), acrylic, and glass.

A Detailed Comparison of Two Different Switched Reluctance Motor’s Parameters and Dynamic Behaviors by applying PID Control in Matlab Simulink

The Switched Reluctance Motor (SRM) is one of the oldest types of electric motors. SRMs have high torque fluctuations as they have a salient pole structure. The popularity of Switched Reluctance Motors has increased in recent years due to their simple structure, ruggedness, reliability, as well as being inexpensive to manufacture and having a high torque to mass ratio. However, there are some disadvantages, such as high torque ripple, noise and the need for an advanced control system. So they are not extensively used in industry. This study focused on the torque ripple and the speed control aspects. Here, the two most popular and similar Switched Reluctance Motors with regard to motor size, called the 6/4 and 8/6 SRM, have been compared according to the dynamic behavior of their motor parameters. Also, the converters, loads and given power values are the same for the two motor systems. The motor parameters were controlled via MatLab Simulink software. Although the 6/4 and 8/6 models are identical and have the same power converters systems and position sensors, they show different motor behavior due to their dissimilar magnetic structures.

Direct-Drive Electro-Hydraulic Servo Valve Performance Characteristics Prediction Based on Big Data and Neural Networks.

Direct-drive electro-hydraulic servo valves play a key role in aerospace control systems, and their operational stability and safety reliability are crucial to the safety, stability, and efficiency of the entire control system. Based on the prediction of the performance change of the servo valve and the resulting judgement and prediction of its life, this can effectively avoid serious accidents and economic losses caused by failure due to performance degradation in the work. On the basis of existing research, factors such as opening, oil contamination, and pressure difference are used as prerequisites for the operation of direct-drive electro-hydraulic servo valves. In addition to the current research on pressure gain and leakage, the performance parameters of servo valves, such as overlap, threshold, and symmetry, are also expanded and selected as research objects, combined with pressure design servo valve performance degradation experiments for testing instruments such as flow and position sensors, and data are obtained on changes in various performance parameters. The experimental data are analyzed and a prediction model is built to predict the performance parameters of the servo valve by combining the existing popular neural networks, and the prediction error is calculated to verify the accuracy and validity of the model. The experimental results indicate that as the working time progresses, the degree of erosion and wear on the valve core and valve sleeve of the servo valve increases. Overall, it has been observed that the performance parameters of the servo valve show a slow trend of change under different working conditions, and the rate of change is generally higher under high pollution (level 9) conditions than under other conditions. The prediction results indicate that the predicted values of various performance parameters of the servo valve by the prediction model are lower than 0.2% compared to the experimental test set data. By comparing the two dimensions of the accuracy and prediction trend, this model meets industrial needs and outperforms deep learning algorithm models such as the exponential smoothing algorithm and ARIMA model. The experiments and results of this study provide theoretical support for the life prediction model of servo valves based on neural networks and machine learning in artificial intelligence, and provide a reference for the development of direct-drive electro-hydraulic servo valves in aerospace and other industrial fields for use and failure standards.

The Extended H∞ Particle Filter for Attitude Estimation Applied to Remote Sensing Satellite CBERS-4

An extension of the linear H∞ filter, presented here as the extended H∞ particle filter (EH∞PF), is used in this work for attitude estimation, which presents a process and measurement model with nonlinear functions. The simulations implemented use orbit and attitude data from CBERS-4 (China–Brazil Earth Resources Satellite-4), making use of the robustness characteristics of the H∞ filter. The CBERS-4 is the fifth satellite of an advantageous international scientific interaction between Brazil and China for the development of remote sensing satellites used for strategic application in monitoring water resources and controlling deforestation in the Legal Amazon. In the extended H∞ particle filter (EH∞PF) the nature of the system, composed of dynamics and noises, seeks to degrade the state estimate. The EH∞PF deals with this by aiming for robustness, using a performance parameter in its cost function, in addition to presenting an advantageous feature of using a reduced number of particles for state estimation. The justification for the application of this method is because the non-Gaussian uncertainties that appear in the attitude sensors impair the estimation process and the EH∞PF minimizes in signal estimation the worst effects of disturbance signals without a priori knowledge of them, as shown in the results, in addition to presenting good precision within the prescribed requirements, with 100 particles representing a processing time 2.09 times less than the PF with 500 particles.

Effect of TaN buffer layer on the sensitivity of ASIC-integrated AMR sensors

This study investigates the impact of a TaN buffer layer on the performance of ASIC-integrated Anisotropic Magnetoresistive (AMR) position sensors. Our findings demonstrate that the inclusion of a TaN buffer layer significantly enhances the sensitivity and stability of the sensors, with a measured sensitivity of 0.678 ± 0.03 Gauss/mV. In contrast, devices without the buffer layer exhibit a notable decline in sensitivity, decreasing to 0.165 ± 0.02 Gauss/mV after 450℃ annealing. In addition, AMR sensors fabricated on non-ASIC substrates showed no such deterioration. Magneto-optical Kerr effect (MOKE) measurements reveal that the change in sensitivity is primarily attributed to variations in the anisotropy field. We propose that the increased anisotropy field is a result of magnetostatic energy induced by alterations in the PECVD-deposited SiO2 surface granular structure, supported by surface morphology characterization and X-ray diffraction experiments. These findings highlight the critical role of the TaN buffer layer in optimizing the performance and stability of ASIC-integrated AMR sensors.

A Convolutional Neural Network for Electrical Fault Recognition in Active Magnetic Bearing Systems.

Active magnetic bearings are complex mechatronic systems that consist of mechanical, electrical, and software parts, unlike classical rolling bearings. Given the complexity of this type of system, fault detection is a critical process. This paper presents a new and easy way to detect faults based on the use of a fault dictionary and machine learning. The dictionary was built starting from fault signatures consisting of images obtained from the signals available in the system. Subsequently, a convolutional neural network was trained to recognize such fault signature images. The objective of this study was to develop a fault dictionary and a classifier to recognize the most frequent soft electrical faults that affect position sensors and actuators. The proposed method permits, in a computationally convenient way that can be implemented in real time, the determination of which component has failed and what kind of failure has occurred. Therefore, this fault identification system allows determining which countermeasure to adopt in order to enhance the reliability of the system. The performance of this method was assessed by means of a case study concerning a real turbomachine supported by two active magnetic bearings for the oil and gas field. Seventeen fault classes were considered, and the neural network fault classifier reached an accuracy of 93% on the test dataset.

An open-source smart fraction collector for isocratic preparative liquid chromatography

Preparative liquid chromatography is a technique for separating complex samples or isolating pure compounds from complex extracts. It involves eluting samples through a packed column and selectively collecting or isolating the separated bands in a sequence of fractions. Depending on the column length and the sample complexity, a large number of fractions may be obtained, making fraction collection a laborious and time-consuming process. Manual fraction collection is also tedious, error-prone, less reproducible, and susceptible to contamination. Several commercial and lab-made solutions are available for automated fraction collection, but most systems do not synchronize with the instrument detector and collect fractions at fixed volumes or time intervals. We have assembled a low-cost Arduino-based smart fraction collector that can record the signal from the UV–vis detector of the chromatography instrument and enable the automated selective collection of the targeted bands. The system consists of a robot equipped with position sensors and a 3-way solenoid valve that switches the column effluent between the waste or collection positions. By proper programming, an Arduino board records the detector response and actuates the solenoid valve, the position sensors, and the stepper motors to collect the target chromatographic bands.

Digital twin exploration of a blended-wing-body underwater glider skeleton in the laboratory environment

ABSTRACT Due to the inherent unpredictability of marine trial conditions, safety issues in the launching and recovery process are commonplace. For these issues, digital twin (DT) maintenance offers a novel idea. In this paper, a blended-wing-body underwater glider skeleton is selected as a research object. And a simple demo is created to investigate the major technologies that may be involved in DT maintenance. According to this specimen, the necessary development environments are developed to obtain physical information and produce a DT model in cyberspace respectively. An attitude sensor is used as the physical data collector, while real-time structural field prediction and virtual reality visualisation are employed. By using the attitude angles and structural data, it is possible to achieve real-time monitoring of the skeleton strength. Through the means of this straightforward case study, the key technologies are supported and can be applied to far more complex inquiries.

Design of sensorless permanent magnet synchronous motor controller based on improved sliding mode observer

Because PMSM needs to install a position sensor in practical application, and the buffeting issue in sensorless control stems from an old SMO (sliding mode observer), an improved SMO is proposed. Stemming from the improvement of the switching function and low-pass filter of the traditional SMO, the chattering problem of the conventional SMO is optimized. Considering the reality that the traditional PI control strategy unable to respond accurately and quickly in the case of current coupling, the current complete decoupling integrates the traditional PI control method is adopted. It can speed up system response and improve system performance. The experimental consequence demonstrates that the improved SMO can effectively restrain the chattering phenomenon and realize the fast and precise control of the PMSM.

Study on position tracking algorithm of large-inertia artillery with terminal sliding mode control

The large-inertia artillery has complex structure and high coupling between components. Due to the processing technology, the error between motor shaft position and the actual load position is inevitable and hardly to be compensated. The situation get worse when the system running in high speed, where the error changes more violently and so-called "vibration" phenomenon shows up. The study establishes a detailed mode of servo system including double-motor control, position-loop control, moment model of gears, and feedback of position sensors. Then a position tracking algorithm with terminal sliding mode control is proposed and verified in artillery system. The experiment shows that this algorithm effectively eliminates the "vibration" phenomenon, enables the servo computer to directly use the load position of the artillery as the control variable, and improves the position tracking accuracy and operational stability of artillery system.

Detection and Analysis of Pavement-Section Based on Laser Displacement Sensor.

The section detection of the pavement is the data basis for measuring the road smoothness, rutting, lateral slope, and structural depth. The detection of the Pavement-Section includes longitudinal-section inspection and cross-section inspection. In this paper, based on multiple laser displacement sensors, fused accelerometers and attitude sensors, and using vehicle-mounted high-speed detection, we design a sensor-fused pavement section data acquisition method, establish the relevant mathematical model, and realize the automatic acquisition of pavement longitudinal and transverse sections. The acceleration sensor is filtered to improve the accuracy of data acquisition, and the error of the detection system is calculated and analyzed. Through the actual measurement, the vehicle-mounted high-speed pavement profile detection method adopted in this paper can not only accurately detect the profile of the pavement profile, but also improve the detection efficiency, providing a cost-effective detection mode for road surface detection.

Data Fusion and Pattern Classification in Dynamical Systems Via Symbolic Time Series Analysis

Abstract Symbolic time series analysis (STSA) plays an important role in the investigation of continuously evolving dynamical systems, where the capability to interpret the joint effects of multiple sensor signals is essential for adequate representation of the embedded knowledge. This technical brief develops and validates, by simulation, an STSA-based algorithm to make timely decisions on dynamical systems for information fusion and pattern classification from ensembles of multisensor time series data. In this context, one of the most commonly used methods has been neural networks (NN) in their various configurations; however, these NN-based methods may require large-volume data and prolonged computational time for training. An alternative feasible method is the STSA-based probabilistic finite state automata (PFSA), which has been shown in recent literature to require significantly less training data and to be much faster than NN for training and, to some extent, for testing. This technical brief reports a modification of the current PFSA methods to accommodate (possibly heterogeneous and not necessarily tightly synchronized) multisensor data fusion and (supervised learning-based) pattern classification in real-time. Efficacy of the proposed method is demonstrated by fusion of time series of position and velocity sensor data, generated from a simulation model of the forced Duffing equation.

The velocity of resistance exercise does not accurately assess repetitions-in-reserve

ABSTRACT This study assessed the reliability of mean concentric bar velocity from 3- to 0-repetitions in reserve (RIR) across four sets in different exercises (bench press and prone row) and with different loads (60 and 80% 1-repetition maximum; 1RM). Whether velocity values from set one could be used to predict RIR in subsequent sets was also examined. Twenty recreationally active males performed baseline 1RM testing before two randomised sessions of four sets to failure with 60 or 80% 1RM. A linear position transducer measured mean concentric velocity of repetitions, and the velocity associated with each RIR value up to 0-RIR. For both exercises, velocity decreased between each repetition from 3- to 0-RIR (p ≤ 0.010). Mean concentric velocity of RIR values was not reliable across sets in the bench press (mean intraclass correlation coefficient [ICC] = 0.40, mean coefficient of variation [CV] = 21.3%), despite no significant between-set differences (p = 0.530). Better reliability was noted in the prone row (mean ICC = 0.80, mean CV = 6.1%), but velocity declined by 0.019–0.027 m·s−1 (p = 0.032) between sets. Mean concentric velocity was 0.050–0.058 m·s−1 faster in both exercises with 60% than 80% 1RM with (p < 0.001). At the individual level, the velocity of specific RIR values from set one accurately predicted RIR from 5- to 0-RIR for 30.9% of repetitions in subsequent sets. These findings suggest that velocity of specific RIR values vary across exercises, loads and sets. As velocity-based RIR estimates were not accurate for 69.1% of repetitions, alternative methods to should be considered for autoregulating of resistance exercise in recreationally active individuals.

Saturation and duty cycle tolerant self-sensing for active magnetic dampers

Traditional active magnetic damper (AMD) systems rely on external position sensors to measure the mover’s radial position, though these sensors add cost, complexity, failure modes, and potential sources of error due to the axial non-colocation of actuator and sensor. An alternate, so-called “self-sensing” approach seeks to exploit displacement-induced changes in electromagnet inductance to determine mover position; such techniques have been described in active magnetic bearing (AMB) and AMD literature since the 1980s. This paper details a novel, magnetic-saturation tolerant self-sensing approach for AMDs. The theoretical basis is presented; it is validated both experimentally and via a transient magnetic finite-element model. A position estimation error of 4.17 µm (0.164 mils) RMS is experimentally demonstrated over a typical mover position range and a theoretical bandwidth of 250 Hz is achieved.