Position Estimation Technique Research Articles

Wireless AC Motor Speed Control System

In this paper, we demonstrate the feasibility of controlling the speed of an induction motor using a wireless position feedback over an RF link and compare its performance under dynamic- and steady-state conditions with those obtained by using a wire-based position feedback control. An electronic circuit is coupled to the motor to provide the control-speed signal serially from distant microcontroller. A control panel is designed as an interface for the user to select the appropriate speed. This paper presents a high performance electric assisted bicycle system, where the rotor position is obtained by a position estimation technique using terminal quantities of the motor, which has a capability of starting from standstill. The components of the drive system include a wireless torque feedback, an application specific reference torque generation and torque control. The paper explains the implementation details of the position estimation technique that utilize a motor control DSP and provides real time test results to demonstrate its capabilities under real riding conditions including regenerative braking

A Study on the UWB-Based Position Estimation Method Using Dead Reckoning Information for Active Driving in a Mapless Environment of Intelligent Wheelchairs

As the world enters an aging and super-aged society, the application of advanced technology in assistive devices to support the daily life of the elderly is becoming a hot issue. Among them, electric wheelchairs are representative assistive devices for the walking support of the elderly, and their structural form is similar to AGV and AMR. For this reason, research is being introduced and underway to guarantee the right to voluntarily move or improve the convenience of movement for the elderly and severely disabled people who have difficulties in operating a joystick for operating an electric wheelchair. Autonomous driving of mobile robots is a technology that configures prior information on the driving environment as a map DB and operates based on it. However, active driving assistance technology is needed because wheelchairs do not move in a limited space, but can move to a place without a prior map DB or vehicle boarding depending on the passenger’s intention to move. Therefore, a system for estimating the moving position and direction of the wheelchair is needed to develop a driving assistance technology in the relevant driving environment. In order to solve the above problem, this study proposes a position and direction estimation algorithm suitable for active driving of a wheelchair based on a UWB sensor. This proposal is an algorithm for estimating the position and direction of the wheelchair through the fusion of UWB, IMU, and encoder sensors. In this proposal, it is difficult to design an active driving assistance system for wheelchairs due to low accuracy, obstacles, and errors due to signal strength in the position and direction estimation with UWB sensors alone. Therefore, this study proposes a wheelchair driving position and direction estimation system that fuses the dead recording information of a wheelchair and the UWB-based position estimation technique based on sensors applied in IMU and encoders. Applying quantitative verification to the proposed technique, the direction estimation accuracy of the wheelchair of about 15.3° and the position estimation error average of ±15 cm were confirmed, and it was verified that a driving guide for active driving was possible when the sensor system proposed in a mapless environment of the wheelchair was installed at a specific destination.

A Leader-follower formation control of mobile robots by position-based visual servo method using fisheye camera

This paper presents a leader-follower formation control of multiple mobile robots by position-based method using a fisheye camera. A fisheye camera has a wide field of view and recognizes a wide range of objects. In this paper, the fisheye camera is first modeled on spherical coordinates and then a position estimation technique is proposed by using an AR marker based on the spherical model. This paper furthermore presents a method for estimating the velocity of a leader robot based on a disturbance observer using the obtained position information. The proposed techniques are combined with a formation control based on the virtual structure. In this paper, the formation controller and velocity estimator can be designed independently, and the stability analysis of the total system is performed by using Lyapunov theorem. The effectiveness of the proposed method is demonstrated by simulation and experiments using two real mobile robots.

Utilizing Probabilistic Maps and Unscented-Kalman-Filtering-Based Sensor Fusion for Real-Time Monte Carlo Localization

An autonomous car must know where it is with high precision in order to maneuver safely and reliably in both urban and highway environments. Thus, in this paper, a reliable and relatively precise position estimation (localization) technique for autonomous vehicles is proposed and implemented. In dealing with the obtained sensory data or given knowledge about the vehicle’s surroundings, the proposed method takes a probabilistic approach. In this approach, the involved probability densities are expressed by keeping a collection of samples selected at random from them (Monte Carlo simulation). Consequently, this Monte Carlo sampling allows the resultant position estimates to be represented with any arbitrary distribution, not only a Gaussian one. The selected technique to implement this Monte-Carlo-based localization is Bayesian filtering with particle-based density representations (i.e., particle filters). The employed particle filter receives the surrounding object ranges from a carefully tuned Unscented Kalman Filter (UKF) that is used to fuse radar and lidar sensory readings. The sensory readings are used to detect pole-like static objects in the egocar’s surroundings and compare them to the ones that exist in a supplied detailed reference map that contains pole-like landmarks that are produced offline and extracted from a 3D lidar scan. Comprehensive simulation tests were conducted to evaluate the outcome of the proposed technique in both lateral and longitudinal localization. The results show that the proposed technique outperforms the other techniques in terms of smaller lateral and longitudinal mean position errors.

Super Twisting Sliding Mode Control with Compensated Current Controller Dynamics on Active Magnetic Bearings with Large Air Gap

Due to their unique properties, like no mechanical contact and therefore no wear and no lubrication needed, Active Magnetic Bearings (AMBs) have been a dynamic field of research in the past decades. The high non-linearities of AMBs generate many challenges for the control of the otherwise unstable system, thus they need to be addressed to deliver the performance that modern applications require. Integrating the current controller dynamics into the model of a position controller in a cascading control loop helps to improve the performance of the control loop compared to a plain current controlled schema. Further, this nested control loop guarantees the predefined current dynamics of the current controller, tuned according to an industrial criterion. The systems dynamics are modelled and the proposed controller is validated experimentally on a physical test bench. The experimental results show a performant position control with a nested and explicit current controller on an AMB, even with a large air gap and star-connected coils. The trajectory range of the rotor was reduced by 87% to ±20 μm, compared to a plain current-controlled model. The proposed control strategy lays the foundation for further research, especially concerning sensorless position estimation techniques since these usually have limited bandwidth and benefit from a predefined current dynamic.

Position estimation using neural networks in semi-monolithic PET detectors

Objective. The goal of this work is to experimentally compare the 3D spatial and energy resolution of a semi-monolithic detector suitable for total-body positron emission tomography (TB-PET) scanners using different surface crystal treatments and silicon photomultiplier (SiPM) models. Approach. An array of 1 × 8 lutetium yttrium oxyorthosilicate (LYSO) slabs of 25.8 × 3.1 × 20 mm3 separated with Enhanced Specular Reflector (ESR) was coupled to an array of 8 × 8 SiPMs. Three different treatments for the crystal were evaluated: ESR + RR + B, with lateral faces black (B) painted and a retroreflector (RR) layer added to the top face; ESR + RR, with lateral faces covered with ESR and a RR layer on the top face and; All ESR, with lateral and top sides with ESR. Additionally, two SiPM array models from Hamamatsu Photonics belonging to the series S13361-3050AE-08 (S13) and S14161-3050AS-08 (S14) have been compared. Coincidence data was experimentally acquired using a 22Na point source, a pinhole collimator, a reference detector and moving the detector under study in 1 mm steps in the x- and DOI- directions. The spatial performance was evaluated by implementing a neural network (NN) technique for the impact position estimation in the x- (monolithic) and DOI directions. Results. Energy resolution values of 16 ± 1%, 11 ± 1%, 16 ± 1%, 15 ± 1%, and 13 ± 1% were obtained for the S1 3-ESR + B + RR, S1 3-All ESR, S14-ESR + B + RR, S14-ESR + RR, and S14-All ESR, respectively. Regarding positioning accuracy, mean average error of 1.1 ± 0.5, 1.3 ± 0.5 and 1.3 ± 0.5 were estimated for the x- direction and 1.7 ± 0.8, 2.0 ± 0.9 and 2.2 ± 1.0 for the DOI- direction, for the ESR + B + RR, ESR + RR and All ESR cases, respectively, regardless of the SiPM model. Significance. Overall, the obtained results show that the proposed semi-monolithic detectors are good candidates for building TB-PET scanners.

Self-Sensing Control of Open-End Winding PMSMs Fed by an Asymmetrical Hybrid Multilevel Inverter

The paper presents a self-sensing control technique for a special type of multilevel motor drive featuring an Open-end Winding Permanent-Magnet Synchronous Motor fed on one side by a main multilevel inverter (MLI) and on the other side by an auxiliary two-level inverter (TLI). In order to minimize the power losses, the MLI manages the machine active power operating at a low-switching frequency. The TLI instead acts as an active power filter and operates at a higher switching frequency and a lower DC-Bus voltage than the MLI. The current control task is shared between the two inverters, as a predictive action is exerted by the MLI, while a feedback action is accomplished by the TLI. Common sensorless rotor position estimation techniques cannot be straightforwardly applied on such a system, due to the particular drive structure. Therefore, a specific technique has been carried out, able to ensure satisfactory efficiency and control performance in all the operating speed ranges by optimally exploiting the different features of the two inverters. Simulation and experimental results confirm the effectiveness of the proposed approach.

Performance Comparison of Several Range-based Techniques for Indoor Localization Based on RSSI

The classical rang-based technique for position estimation is still reliably used for indoor localization. Trilateration and multilateration, which include three or more references to locate the indoor object, are two common examples. These techniques use at least three intersection-locations of the references' distance and conclude that the intersection is the object's position. However, some challenges have appeared when using a simple power-to-distance parameter, i.e., received signal strength indicator (RSSI). RSSI is known for its fluctuated values when used as the localization parameter. The improvement of classical range-based has been proposed, namely min-max and iRingLA algorithms. These algorithms or methods use the approximation in a bounding-box and rings for min-max and iRingLA, respectively. This paper discusses the comparison performance of min-max and iRingLA with multilateration as the classical method. We found that min-max gives the best performance, and in some positions, iRingLA gives the best accuracy error. Hence, the approximation method can be promising for indoor localization, especially when using a simple and straightforward RSSI parameter.

Experimental Study on Tele-Manipulation Assistance Technique Using a Touch Screen for Underwater Cable Maintenance Tasks

In underwater environments restricted from human access, many intervention tasks are performed by using robotic systems like underwater manipulators. Commonly, the robotic systems are tele-operated from operating ships; the operation is apt to be inefficient because of restricted underwater information and complex operation methods. In this paper, an assistance technique for tele-manipulation is investigated and evaluated experimentally. The key idea behind the assistance technique is to operate the manipulator by touching several points on the camera images. To implement the idea, the position estimation technique utilizing the touch inputs is investigated. The assistance technique is simple but significantly helpful to increase temporal efficiency of tele-manipulation for underwater tasks. Using URI-T, a cable burying ROV (Remotely Operated Vehicle) developed in Korea, the performance of the proposed assistance technique is verified. The underwater cable gripping task, one of the cable maintenance tasks carried out by the cable burying ROV, is employed for the performance evaluation, and the experimental results are analyzed statistically. The results show that the assistance technique can improve the efficiency of the tele-manipulation considerably in comparison with the conventional tele-operation method.

UKF-Based Sensor Fusion Method for Position Estimation of a 2-DOF Rope Driven Robot

In this study, the unscented Kalman filter-based method was introduced as a new technique for position estimation of the two-degree-of-freedom facade cleaning robot known as the Dual Ascender Robot (DAR). While other facade cleaning robots use a winch, the DAR uses an ascender, resulting in rope slip inside the ascender. Rope slip does easily cause errors in length data, so DARs cannot be easily controlled based on length data as in the case of most facade cleaning robots. Therefore, the DARs estimate the length data and use it through position estimation to overcome the rope slip for control. DARs use a rope length-based sensor fusion method for position estimation. This method employs position data based on both length data and angle data to estimate the position; however, it is difficult to use for long periods of time owing to the increased error that accumulates with time. Therefore, the use of position data based on angle data is proposed herein via application of the unscented Kalman filter. This unscented Kalman filter-based method is tested to confirm that the positional estimation performance is improved relative to that achieved via the previously used method. The performance improvements are compared in terms of accuracy and repeatability using the double ball bar method, and the errors in accuracy and repeatability are found to be reduced by approximately 2–3 times.

A Novel Robust Sensorless Technique for Field-Oriented Control Drive of Permanent Magnet Synchronous Motor

This paper proposes a novel rotor position estimation technique based on stator flux linkage to implement robust sensorless field-oriented control for permanent magnet synchronous motor drives. The proposed method can accurately estimate rotor position by mitigating the dependence on quality of the magnitude and phase angle of the estimated stator flux linkage. A high-accuracy load angle calculation based on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> - <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> -axis current estimation method is first developed for further rotor position determination. In the rotor position calculation, the reference flux linkage magnitude is used instead of the estimated one to avoid the influence of potential high noise levels during low speed operation. As a result, the rotor angle estimation depends only on the phase angle of stator flux linkage, without the effect of its magnitude. The estimation accuracy can thus be improved. Also, by employing a simple stator flux linkage phase lag compensator, the performance of the proposed sensorless method can be dramatically improved at low speed. Moreover, the problem of motor parameter variation (e.g., changes in winding resistance, stator inductances, and permanent magnetic flux linkage) has been analyzed and overcome. Finally, comparisons between conventional and proposed methods are presented with simulations and experiments to demonstrate the effectiveness and reliability of the proposed method.

Analysis of Position Estimation Techniques in a Surgical EM Tracking System

Electromagnetic tracking systems (EMTSs) are widely used in surgical navigation, allowing to improve the outcome of diagnosis and surgical interventions in terms of shorter hospitalization times, reduction of wound pain and increased accuracy, by providing the surgeon with real-time position of surgical instruments during medical procedures. Particular effort must be made on the development of an efficient and robust algorithm to obtain an accurate estimation of the instrument position, for distances from the magnetic field generator beyond 500 mm, which represents the maximum measurement range of current commercial systems, which strongly affects the freedom of movement of the medical team. In this paper we describe two position estimation techniques, based on a model of the magnetic field, then we analyze the robustness to errors and distortions in a set of simulations for different conditions, discussing their pros and cons in a large measurement volume, and we propose a solution by combining the two techniques. The performance of the new technique is tested on experimental data from a novel EMTS prototype, developed to increase the measurement range of current EMTSs, at a mean distance of 600 mm, and a calibration procedure is performed to reduce systematic errors. A mean position and orientation accuracy of 2.2 mm and 0.5° is achieved, along with mean repeatability errors of 1.5 mm and 0.25°, within the specifications required in many surgical applications.

Two-Dimensional Position and Orientation Estimation Using a 5.8 GHz RFID System

Precise estimation of object position and orientation using RF tags is an emerging application of RFID technology. In this work, we present a novel wireless two-dimensional position and orientation estimation and tracking technique that illustrates the concept of sensor fusion-using multiple, disparate sensor measurements to synthesize a single, superior estimate of position and orientation. The technique is implemented using a 5.8 GHz RFID system that consists of three readers and a customized tag equipped with a 3-axis accelerometer, 3-axis gyroscope and 3-axis magnetometer. Received signal strengths and packet-to-packet phase differences at the reader are used to measure radial tag positions and radial tag velocities, and backscattered data packets provide measurements of the tag's linear accelerations, angular velocities and orientations. A hybrid inertial microwave reflectometry (HIMR) algorithm is used to produce motion-capture grade estimates of tag position and orientation; in experiments, a two-dimensional position accuracy of less than 13 mm was achieved. The HIMR algorithm is designed using physics-based modeling equations, modifications are proposed to accommodate temporary link loss, and observability analyses clarify the foundations of the estimator design method.

Speed and Position Estimation for Sensorless Control of PMSM: A Critical Review

The Vector control scheme widely used for control purposes of PMSM drives requires rotor position information. Generally, sensors are used for this purpose, which increases the cost, size, and complexity of the system and also reduces the efficiency of drives. Therefore, it has become essential to avoid the use of these sensors and go for sensorless drives. In this paper, different types of speed and position estimation techniques are reviewed for sensorless control with their recent developments and their inherent advantages and disadvantages.

Magnetizing current based improved rotor position and speed estimation of doubly-fed induction generator using model reference adaptive scheme

An improved rotor position and speed estimation technique for doubly-fed induction generator (DFIG) has been described in this paper. DFIG is widely used as electrical generator in wind energy conversion system. For this wind generator, rotor position and speed information is extremely necessary for implementation of position/speed sensor-less control logics for attaining high dynamic performance. In this respect, magnetization current based rotor position and speed estimation technique is proposed. The estimation scheme adopts model reference adaptive system (MRAS). Various highly desirable features such as integrator- and differentiator-less operation, flux computation-less logic, least machine’s parameter sensitivity, start on fly and capability of operation on both sub- and super-synchronous modes have been achieved in proposed scheme. The improvement in proposed estimation model can be stated that it is free from magnetizing parameter mismatch irrespective of power level. On the other hand, existing excitation current based scheme does not provide accurate estimation due to deviation of magnetizing inductance in low output power condition i.e. limited operating range. Also, another traditional excitation current based technique includes direct flux estimation (subjected to flux dynamics directly) in addition to magnetizing parameter dependency. Both simulation and real-time validation of proposed estimation concept have been done to present superiority over traditional schemes.

Algorithm of local features fusion and modified covariance-matrix technique for hand motion position estimation and hand gesture trajectory tracking approach

Nowadays, visual recognition based dynamic hand gesture tracking has gained very considerable attention. Hand gestures can play an important role as a non-touchable communication tool between machines and humans based on using the affordable built-in webcam. The need for replacing touch-based computing devices interaction has been increasing in many fields like healthcare, security, and generally as interface-based devices controlling. Especially with COVID19 outbreak spreading around the world, where people take a risk and avoid dealing with electronic consumer machines that required hand touching. However, in any dynamic hand gesture recognition system, dynamic hand gesture tracking is a very hard task, where position estimation over video frames for freely moving hand in the air is quite challenging. This shortcoming is due to hand great scale changes, posture variations, and translation problems. Hence, to tackle these difficulties and extract gesture features for gesture recognition phase accurately, this paper proposed an algorithm of dynamic hand trajectory tracking for gesture recognition. The presented algorithm proposes local features fusion based on Gabor-Canny- Hog features embedded an updated compact covariance matrix technique as sophisticated feature-based tracking, utilizing video sequences of IBGHT dataset. As a result, the proposed approach has shown adorable optimization achieving an accuracy rate of 96.97%, overcoming the problems of hand appearance variation in the complicated environment.

An Embedded Platform for Positioning and Obstacle Detection for Small Unmanned Aerial Vehicles

Unmanned Aerial Vehicles (UAV) with on-board augmentation systems (UAS, Unmanned Aircraft System) have penetrated into civil and general-purpose applications, due to advances in battery technology, control components, avionics and rapidly falling prices. This paper describes the conceptual design and the validation campaigns performed for an embedded precision Positioning, field mapping, Obstacle Detection and Avoiding (PODA) platform, which uses commercial-off-the-shelf sensors, i.e., a 10-Degrees-of-Freedom Inertial Measurement Unit (10-DoF IMU) and a Light Detection and Ranging (LiDAR), managed by an Arduino Mega 2560 microcontroller with Wi-Fi capabilities. The PODA system, designed and tested for a commercial small quadcopter (Parrot Drones SAS Ar.Drone 2.0, Paris, France), estimates position, attitude and distance of the rotorcraft from an obstacle or a landing area, sending data to a PC-based ground station. The main design issues are presented, such as the necessary corrections of the IMU data (i.e., biases and measurement noise), and Kalman filtering techniques for attitude estimation, data fusion and position estimation from accelerometer data. The real-time multiple-sensor optimal state estimation algorithm, developed for the PODA platform and implemented on the Arduino, has been tested in typical aerospace application scenarios, such as General Visual Inspection (GVI), automatic landing and obstacle detection. Experimental results and simulations of various missions show the effectiveness of the approach.

A Solution to Ambiguities in Position Estimation for Solenoid Actuators by Exploiting Eddy Current Variations.

Position estimation techniques for solenoid actuators are successfully used in a wide field of applications requiring monitoring functionality without the need for additional sensors. Most techniques, which also include standstill condition, are based on the identification of the differential inductance, a parameter that exhibits high sensitivity towards position variations. The differential inductance of some actuators shows a non-monotonic dependency over the position. This leads to ambiguities in position estimation. Nevertheless, a unique position estimation in standstill condition without prior knowledge of the actuator state is highly desired. In this work, the eddy current losses inside the actuator are identified in terms of a parallel resistor and are exploited in order to solve the ambiguities in position estimation. Compared to other state-of-the-art techniques, the differential inductance and the parallel resistance are estimated online by approaches requiring low implementation and computation effort. Furthermore, a data fusion algorithm for position estimation based on a neural network is proposed. Experimental results involving a use case scenario of an end-position detection for a switching solenoid actuator prove the uniqueness, the precision and the high signal-to-noise ratio of the obtained position estimate. The proposed approach therefore allows the unique estimation of the actuator position including standstill condition suitable for low-cost applications demanding low implementation effort.

Sensorless Predictive Current Control of PMSM EV Drive Using DSOGI-FLL Based Sliding Mode Observer

Conventional back electromotive force (EMF) estimation based active flux concept for sensorless control has various limitations due to pure integrator problems and performance deterioration at low speeds. The fundamental back EMF component extraction with a conventional low pass filter, is difficult due to the presence of lower order harmonics, dc offset, saturation, etc., and causes significant phase delay in the estimated position and speed. This article deals with a sensorless speed and position estimation technique for a surface mounted permanent magnet synchronous motor (SPMSM) based electric vehicle (EV) drive using a sliding mode observer (SMO) with a dual second order generalized integrator frequency locked loop (DSOGI-FLL). A novel method of utilizing the DSOGI-FLL, along with feedforward term is shown to achieve better harmonic elimination of estimated back EMF components and faster dynamic speed estimation. A delay compensated predictive current controller-based PMSM EV drive with the presented estimation technique is used for improved dynamic response. This system is simulated using Simulink and its efficacy is experimentally validated using dSPACE 1202. Test results show that the DSOGI-FLL-based SMO has better performance compared to the conventional technique.

Design of a Compact, Multifrequency, Multiconstellation GNSS Precise Point Positioning Correction Format

High-accuracy, high-precision global navigation satellite systems (GNSSs) have been utilized by specialized user markets for over two decades. Traditional high-accuracy GNSS systems are not scalable as they require local infrastructure. Precise point positioning (PPP) is a GNSS position estimation technique that allows positioning and navigation with centimetre-level accuracy without local infrastructure. The caveat of PPP is the timely transmission of corrections to errors in the GNSS signals. Next-generation GNSS satellites such as Galileo, BeiDou, QZSS, and other local systems have plans to broadcast corrections for PPP services broadcast corrections for PPP services. However, since these services are expected to have limited channel capacity, the development of efficient formats for PPP corrections is needed. In this article, a messaging strategy for PPP corrections and testing its performance, in terms of data rate and obtained positioning accuracy, is proposed. Message formats in the literature require 522 bps or more to transmit corrections for 70 satellites. By contrast, the message format proposed in this article can transmit information corrections for more than 70 satellites using 52% of the standard satellite-based augmentation system channel's 212 bps capacity, while incurring less than 1 cm of positioning accuracy degradation.