External Position Sensors Research Articles

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.

Deep Neural Network-Based Physics-Inspired Model of Self-Sensing Displacement Estimation for Antagonistic Shape Memory Alloy Actuator

The paper introduces a self-sensing feature that utilizes differential resistance measurement of an antagonistic Shape Memory Alloy (SMA) actuator to estimate linear displacement. The external position sensor used for control feedback becomes extraneous while utilizing an electrical resistance as feedback. The self-sensing capability provides additional advantages such as the overall reduction in size, weight, and interface complexity of an actuator. SMA actuator wires were used in the antagonistic configuration for the bi-directional actuation of the targeted application. A Deep Neural Network (DNN) having Long Short Term Memory (LSTM) layers were used to estimate the dynamic relation of resistance and displacement. A novel DNN model was developed for estimation purposes inspired by the physics-based description of the SMA actuation phenomenon. Accordingly, DNN was developed using the first and last layers as LSTM and a middle layer of feedforward neural network. Estimation results are presented with performance evaluation in terms of the R-squared index and mean absolute error (MAE) of the proposed model; also, the model has been compared with the generic LSTM 2-layered model.

Differential resistance based self-sensing recurrent neural network model for position estimation and control of antagonistic Shape Memory Alloy actuator

The paper presents the development and experimental investigation of recurrent neural network (RNN) based self-sensing position estimation (SSPE) model for shape memory alloy actuator (SMA). RNN is used as an estimator in the position feedback control loop to replace the external additional position sensor. The model was inspired by the physics-based analogy of the Mass-Spring-Damper (MSD) system for the antagonistic SMA wire actuator. Actuator displacement presents the hysteresis and non-linear dynamic relationship with observed differential resistance (sensing signal) during phase transformation. The resistance variation causes due to dissimilar resistivity between primary phases of SMA material. The RNN based estimation model was considered because it consists of memory elements for storing the processed information and feedback connections for dynamic modeling of the system. RNN was trained with input sensing signal and target displacement datasets. The estimation accuracy of the model was real-time evaluated during trajectory tracking of reference signals in the feedback control loop. A quantitative performance analysis is assessed in terms of correlation (ℛ), mean absolute error (MEA), and root mean square error (RSME) of the learned model along with the developed actuator system. The tracking results confirm the close agreement between estimated and measured displacement at a reasonable accuracy.

Compliant, Large-Strain, and Self-Sensing Twisted String Actuators.

Twisted string actuators (TSAs) convert rotational motion from twisting into linear motion. They are known for high energy efficiency, and large linear strain and stress outputs. Although they have been successfully applied as the moving mechanism for different robot applications, their potential in soft robotics is mainly challenged by two aspects: First, the conventional strings of TSAs are stiff and strong but not compliant. Second, precise control of TSAs predominantly relies on external position or force sensors. Because of these, TSA-driven robots are often rigid and bulky. In this study, we propose the design, modeling, and robotic application of TSAs that are compliant, can produce large strain, and are capable of self-sensing during twisting-induced actuation. The design is realized by replacing conventional stiff strings with compliant, thermally activated, and conductive supercoiled polymer strings. Experiments show that the developed TSAs have normalized stiffness of <50 N, strain >30%, and position self-sensing capability during twisting. The quasi-static actuation and self-sensing properties are accurately captured by the Preisach hysteresis operators. In particular, both the twisting-induced actuation and thermally induced actuation are considered. Finally, the proposed TSAs are successfully demonstrated in a low-cost three-dimensionally printed compliant robotic gripper.

An Overview of Piezoelectric Self-Sensing Actuation for Nanopositioning Applications: Electrical Circuits, Displacement, and Force Estimation

The industrial sector often employs piezoelectric materials as actuators for a variety of uses, some of which require a precise positioning while being limited by space and cost factors that impede the insertion of external position and force sensors. Piezoelectric actuators (PEAs) are characterized by strong nonlinearities (hysteresis and creep), badly damped oscillations, and sensitivity to the environment, especially temperature variation, which makes the measurement of the position mandatory to guarantee the required precision and repeatability of piezoelectric-based positioning systems operating at the microscale and nanoscale. Self-sensing actuation (SSA) techniques allow the implementation of precise positioning control of PEAs without the hindrance of external position sensors. This article reviews the different SSA techniques used for precise positioning control of PEAs. The principle of SSA is defined by the capability of deriving the physical state of a PEA (displacement, perceived force, and so on) without the use of external sensors to directly measure thereof, but rather by estimating it from the measurement of less intrusive and cheaper physical signals produced by the PEA itself (throughout current, voltage drop, and so on). The applicability and constraints of each SSA approach are examined in order to help in the determination of the most adequate approach for precise control of PEAs positioning and handling force control.

Development of the extension type pneumatic soft actuator with built-in displacement sensor

This paper proposes a method for a soft pneumatic actuator to measure its displacement without external position sensors, which are usually difficult for washing and sterilization. Medical systems and food industry desire disposable, precise, and human-friendly actuators. The method utilizes a metal spring which covers the cylindrical soft actuator, as the inductance of the spring changes according to the length of the metal spring. The spring also restrains the radial expansion of the actuator during pressurization. This actuator is lightweight, weighing about 30 [g] and is washable, sterilizable and disposable. An approximation of the relationship between the frequency and position was obtained, and its validity was evaluated through experiments. The displacement sensing of the proposed actuator has good accuracy for use in medical robots and other robots working with humans.

Sensorized, Flat, Pneumatic Artificial Muscle Embedded with Biomimetic Microfluidic Sensors for Proprioceptive Feedback.

In recent years, soft components, such as pneumatic artificial muscles (PAMs), have been increasingly employed to design safer wearable devices. Despite the inherent compliance of the materials used to fabricate PAMs, the actuators are able to produce relatively large forces and work when compared to their weight. However, effective operation of these systems has traditionally required bulky external force and position sensors, which limit the maneuverability of users. To overcome these issues, inspiration was taken from organic muscles, which incorporate embedded sensors, such as Golgi tendon organs and muscle spindles, to provide real-time position and force feedback for muscles. As such, a sensorized, flat, pneumatic artificial muscle (sFPAM) with embedded force and position sensors was designed and fabricated. In addition, a hyperelastic model was developed and verified through comparison with the experimentally characterized mechanical and electrical performance of the sFPAM. Furthermore, a sliding mode controller was implemented to demonstrate the feasibility of embedded sensors to provide feedback during operation. Ultimately, a lightweight, compact actuation system was designed with the ability to be seamlessly incorporated into future wearable devices.

Computer-Aided Stroke-by-Stroke Visualization of Actual and Target Power Allows for Continuously Increasing Ramp Tests on Wind-Braked Rowing Ergometers.

Ramp tests are continuously increasing exercise tests used for the determination of maximal oxygen uptake ( V ˙ O 2 max ), which is identifiable by a plateau in oxygen uptake despite increasing mechanical power output (LOAD). On wind-braked rowing ergometers (RowErg), it is hardly feasible to ensure a continuous increase in LOAD until test termination, as neither resistance nor stroke frequency is externally adjustable but depends on the rower. To enable ramp tests on RowErg, the authors produced visual stroke-by-stroke feedback showing target and actual LOAD to the rower. The software supports automatic test termination (TERMauto) if LOAD ceases to increase. The authors aimed to evaluate linearity of the LOAD increment and calculate the difference between TERMauto and test duration at subjective exhaustion. Twenty-eight highly trained male rowers performed a ramp test until subjective exhaustion on RowErg, targeting an increment of 35W·min-1. LOAD was measured as work per time via external force and position sensors and visualized on a computer screen. TERMauto was deactivated, but all data were logged. Test duration at subjective exhaustion was subsequently compared with virtual test duration at TERMauto calculated from the log files. Regression between time and LOAD was y = 167 + 34.6W (r = .99). Individual correlations ranged from .97 to 1.0. TERMauto caused 12- to 35-s-shorter test durations than subjective exhaustion in 4 rowers, leading to an underestimation in V ˙ O 2 max not higher than 1.2% or 3.7%. This setup allows one to perform ramp tests on RowErg with continuously increasing LOAD until TERMauto. In particular cases V ˙ O 2 max might be slightly underestimated at TERMauto.

Redundant unbalance compensation of an active magnetic bearing system

To achieve a good running behavior of a magnetic levitated rotor, a well-developed position controller and different compensation methods are required. Two very important structures in this context are the reduction of the gyroscopic effect and the unbalance vibration. Both structures have in common that they need the angular velocity information for calculation. For industrial applications this information is normally provided by an angle sensor which is fixed on the rotor. The angle information is also necessary for the field oriented control of the electrical drive. The main drawback of external position sensors are the case of a breakdown or an error of the motor controller. Therefore, the magnetic bearing can get unstable, because no angular velocity information is provided. To overcome this problem the presented paper describes the development of a selfsensing unbalance rejection in combination with a selfsensing speed control of the motor controller. Selfsensing means in this context that no angle sensor is required for the unbalance or torque control. With such structures two redundant speed and angle information sources are available and can be used for the magnetic bearing and the motor controller without the usage of an angle sensor.

Control of a shape memory alloy–actuated rotary manipulator using an artificial neural network–based self-sensing technique

Shape memory alloys are a class of smart materials that can recover their original shape by heating above a temperature called austenite transformation temperature when subjected to deformation at low temperature. This property enables the shape memory alloy to be used as a unique actuator. Also, the material’s resistance changes during transformation. Thus, the change in resistance and other electrical properties can be used to sense the deformation, which eliminates the requirement of additional sensors. An artificial neural network, described in our earlier study, was able to accurately model the relationship between the electrical properties and manipulator position. This study aims to develop a control methodology of a shape memory alloy–actuated rotary manipulator using the feedback signal from the previously developed artificial neural network model, thus eliminating the need of any external position sensor. The control methodology using variable structure control technique is experimentally tested under different conditions. Also, the effect of environmental temperature on the ability of artificial neural network to predict manipulator position is analyzed using phenomenological model simulations. It is concluded that this system gives a robust performance with a small tolerance (less than 5°) and can operate well even when the ambient temperature changes considerably.

Linear and Angular Position Sensing for Two- Degrees-of-Freedom Motor

Abstract A linear and rotational two-degrees-of-freedom switched reluctance motor (2DOF-SRM) has been developed to independently control linear and rotational actuation on one actuator. The 2DOF-SRM drive system requires information on the linear and angular positions of the rotor for its control. The approximate angular position of the prototype 2DOF-SRM rotor was detected by the built-in photo-switches that utilize the salient pole of the rotor as the sensor target. The linear position of the rotor was detected by an external linear position sensor mechanically connected to the output shaft. To simplify this sensing system and improve sensing resolution, this paper presents a noncontact optical sensing method to simultaneously detect linear and angular positions of an actuating 2DOF-SRM rotor. The sensing principle for detecting the combined linear and rotational motion of the 2DOF-SRM and the performance of the linear and angular position sensing system are reported.

Optimization strategies for ultrasound volume registration

This paper considers registration of 3D ultrasound volumes acquired in multiple views for display in a single image volume. One way to acquire 3D data is to use a mechanically swept 3D probe. However, the usefulness of these probes is restricted by their limited field of view. This problem can be overcome by attaching a six-degree-of-freedom (DOF) position sensor to the probe, and displaying the information from multiple sweeps in their proper positions. However, an external six-DOF position sensor can be an inconvenience in a clinical setting. The objective of this paper is to propose a hybrid strategy that replaces the sensor with a combination of three-DOF image registration and an unobtrusive inertial sensor for measuring orientation. We examine a range of optimization algorithms and similarity measures for registration and compare them in in vitro and in vivo experiments. We register based on multiple reslice images rather than a whole voxel array. In this paper, we use a large number of reslices for improved reliability at the expense of computational speed. We have found that the Levenberg–Marquardt method is very fast but is not guaranteed to give the correct solution all the time. We conclude that normalized mutual information used in the Nelder–Mead simplex algorithm is potentially suitable for the registration task with an average execution time of around 5 min, in the majority of cases, with two restarts in a C++ implementation on a 3.0 GHz Intel Core 2 Duo CPU machine.

Selective image similarity measure for bronchoscope tracking based on image registration

We propose a selective method of measurement for computing image similarities based on characteristic structure extraction and demonstrate its application to flexible endoscope navigation, in particular to a bronchoscope navigation system. Camera motion tracking is a fundamental function required for image-guided treatment or therapy systems. In recent years, an ultra-tiny electromagnetic sensor commercially became available, and many image-guided treatment or therapy systems use this sensor for tracking the camera position and orientation. However, due to space limitations, it is difficult to equip the tip of a bronchoscope with such a position sensor, especially in the case of ultra-thin bronchoscopes. Therefore, continuous image registration between real and virtual bronchoscopic images becomes an efficient tool for tracking the bronchoscope. Usually, image registration is done by calculating the image similarity between real and virtual bronchoscopic images. Since global schemes to measure image similarity, such as mutual information, squared gray-level difference, or cross correlation, average differences in intensity values over an entire region, they fail at tracking of scenes where less characteristic structures can be observed. The proposed method divides an entire image into a set of small subblocks and only selects those in which characteristic shapes are observed. Then image similarity is calculated within the selected subblocks. Selection is done by calculating feature values within each subblock. We applied our proposed method to eight pairs of chest X-ray CT images and bronchoscopic video images. The experimental results revealed that bronchoscope tracking using the proposed method could track up to 1600 consecutive bronchoscopic images (about 50s) without external position sensors. Tracking performance was greatly improved in comparison with a standard method utilizing squared gray-level differences of the entire images.

Accuracy of Biodex system 3 pro computerized dynamometer in passive mode

A specific experimental design has been developed to determine the accuracy of the Biodex system 3 pro dynamometer in passive mode. Five cyclic stretching repetitions were imposed to an elastic rubber band at different velocities using the dynamometer, and the torque produced was measured using both the dynamometer and external force and position sensors. Velocity patterns performed by the dynamometer were also characterized and our results show that these patterns were reliable (ICC = 1.00). The torque measured with the dynamometer and the sensors were reliable (ICC = 1.00), although significant differences were observed between both methods. However, the measured torque standard error was velocity independent and was lower than 0.33 N m. Moreover, regressions between the two torque measurements were close to the axes-bisector ( r = 1.00, slope: 1.01 ± 0.01, y-intercept: −0.36 ± 0.22 N m). Finally, our results showed decreases in torque during the five cycles, but these decreases were not due to the dynamometer. It can be concluded that the dynamometer performed valid torque measurements in passive mode, and was an accurate tool to determine passive mechanical properties of the musculo-articular system. However, some discrepancies between the programmed and the measured speed profiles have been observed when approaching the speed limit of the system.

Speckle classification for sensorless freehand 3-D ultrasound

Despite being a valuable tool for volume measurement and the analysis of complex geometry, the need for an external position sensor is holding up the clinical exploitation of freehand 3-D ultrasound. Some sensorless systems have been developed, using speckle decorrelation for out-of-plane distance estimation, but their accuracy is still not as good as that of sensor-based systems. Here, we examine the widely held belief that accuracy can be improved by limiting the distance measurements to patches of ultrasound data containing fully developed speckle. Without speckle detection, we observe that scan separation is systematically underestimated by 33.1% in biological tissue. We describe a number of speckle detectors and show that they reduce the underestimate to about 25%. We conclude that speckle classification can improve the quality of distance estimation, but not sufficiently to achieve accurate, metric reconstruction of the insonified volume. (E-mail: ph305@eng.cam.ac.uk)

環境との接触を考慮したPSDカメラによる冗長ロボットの位置&amp;middot;姿勢制御

The paper describes a position and posture controller design based on the absolute position by external PSD vision sensor for redundant robot manipulator. The redundancy enables a potential capability to avoid obstacle while continuing given end-effector jobs under contact with middle link of manipulator. Under contact motion, the deformation due to joint torsion obtained by comparing internal and external position sensor, is actively suppressed by internal/external position hybrid controller. The selection matrix of hybrid loop is given by the function of the deformation. And the detected deformation is also utilized in the compliant motion controller for passive obstacle avoidance. The validity of the proposed method is verified by several experimental results of 3link planar redundant manipulator.

Four-Quadrant Position Sensorless Control in SRM Drives Over the Entire Speed Range

Proper synchronization of the excitation with respect to the rotor position is essential in optimal control of switched reluctance motor (SRM) drives. To avoid additional cost, size, and unreliability caused by the external position sensors, magnetic status of the SRM can be directly monitored to detect commutation instants. A one-to-one correspondence between magnetic status of the SRM and rotor position removes the need for an explicit access to the rotor position. In order to obtain a good precision over the entire speed range, educated modifications on the structure of the sensorless strategy is necessary. This is due to the impact of the operational regions on dynamic behavior of the SRM. In addition, introduction of SRM technology to industrial and domestic applications has raised the need for four-quadrant operation of the SRM drives. The present paper introduces a range of strategies that are necessary to accommodate this requirement in a sensorless format. Our experimental findings indicate that high-grade sensorless operation in a four-quadrant SRM drive is possible and can be integrated in a variety of applications.

Robust Direct Visual Servo Using Network-Synchronized Cameras

A direct visual servoing system is described which employs a network of cameras providing high-speed vision feedback that is robust to occlusions. This system does not rely on any external position or velocity sensors, but directly sets motor current using visual feedback alone. The limitation of 60 Hz video is overcome with multiple RS-170 cameras, synchronized over a network in round-robin fashion, to capture video fields at different instants in time. Each camera has its own computer that processes video at field rates to determine the position of a planar robot joint using eigenspace methods. The eigenspace computations produce position and Euclidean distance measurements sent from each camera node over a network to a master servo computer. It is shown that the Euclidean distance from the manifold in eigenspace in the presence of random occlusions is statistically related to the position measurement error. Occlusions are thus considered as "noise," and the measurement error variance is estimated directly from Euclidean distance. The measurement error variance is applied directly to a Kalman filter, which weights feedback from each camera to provide improved position estimates. The Kalman filter also models the vision transport delays to provide timely position estimates to ensure the stable direct visual servoing of a planar robot. Simulation results illustrate improvement in dynamic performance as the number of cameras are increased. Experimental measurements were obtained for a network of four cameras performing direct visual servoing of a simple planar robot. The results demonstrate the step response, as well as stable servo-hold operation in the presence of full occlusions in a subset of cameras or with partial occlusions in all cameras.

Correction of probe pressure artifacts in freehand 3D ultrasound

We present an algorithm which combines non-rigid image-based registration and conventional position sensing to correct probe-pressure-induced registration errors in freehand three-dimensional (3D) ultrasound volumes. The local accuracy of image-based registration enables the accurate freehand acquisition of high resolution (>15 MHz) 3D ultrasound data, opening the way for 3D musculoskeletal examinations. External position sensor readings guarantee the large-scale positional accuracy of the data. Pressure correction is shown to dramatically increase the perceived quality of extended-field-of-view data sets and reslices through volumetric data sets, while quantitative comparisons of multiple in vivo volumes demonstrate the superior precision of the corrected data.

Elimination of position sensors in switched reluctance motor drives: state of the art and future trends

This paper covers the range of topics related to sensorless control of switched reluctance motor (SRM) drives from their fundamentals to their limitations and state of the art and future trends. This should help the reader to develop a systematic understanding of the sensorless techniques that have been developed over the past two decades. The inherent vulnerability to mechanical failures, extra cost, and size associated with external position sensors such as optical encoders, resolvers, and custom-designed Hall-effect sensors has motivated many researchers to develop sensorless control techniques for SRM drives. Ideally, it is desirable to have a sensorless scheme, which uses only terminal measurements and does not require additional hardware or memory while maintaining a reliable operation over the entire speed and torque range with high resolution and accuracy. Advances in the development of low-cost digital-signal-processor-based microcontrollers have paved the way for the fulfillment of this objective. It is, furthermore, our view that the existing trends in the development of more powerful processors will ultimately replace the concept of sensorless controls with the concept of eliminating the need for position sensing, a concept that will further revolutionize the motor drive technology.