Accurate Position Estimation Research Articles

Online Inductance Identification Using PWM Current Ripple for Position Sensorless Drive of High-Speed Surface-Mounted Permanent Magnet Synchronous Machines

Back–electromotive force estimation-based rotor position estimation methods are usually adopted for high-speed surface-mounted permanent magnet synchronous machines (SPMSM) drive to deal with the limitations of position sensors in high-speed applications. Considering inductance mismatch is the main cause of the position estimation error, this article proposes a novel pulsewidth modulation (PWM) current ripple-based online inductance identification method to improve the position estimation accuracy. The proposed method utilizes the inherent PWM current ripple of the voltage source inverter for inductance identification. In addition, the transient circuit equations on the estimated <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\gamma \delta$</tex-math></inline-formula> frame are adopted as the identification model, and the recursive least squares method is used to obtain the estimated inductance. Compared with the traditional parameter identification methods, the proposed method does not need to inject additional signals or fix parameters. During the sensorless drive of high-speed SPMSM, the proposed method can accurately identify the inductance while not affecting the normal operation of PMSM at all, which is the main contribution of this article. With the proposed method, the estimated inductance can rapidly converge to the accurate value, and then the position estimation error will be eliminated. Finally, the effectiveness of the proposed method is verified by simulations and experiments.

A Time-Efficient Complementary Kalman Gain Filter Derived From Extended Kalman Filter and Used for Magnetic and Inertial Measurement Units

Magnetic and inertial measurement units (MIMUs) are mainly used for determining the attitude of moving bodies. The extended Kalman filter (EKF) and the complementary filters (CFs) are the most commonly applied algorithms for calculating the attitude of the body that an MIMU is attached. Despite its much higher accuracy than CFs, the time-consuming EKF may not be competent for the work of attitude calculation if a microcomputer with limited computation power is used. To any attitude calculating algorithm, its posterior attitude (PA) is computed as: PA = prior attitude + gain matrix (GM) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> innovation. The prior attitude and the innovation are derived from the measurements of the gyroscope, the accelerometer, and the magnetometer. The detailed analysis in this study found that the reason for the performance difference between EKF and CFs is their different structures of the GMs, which promotes the authors to construct a new GM. Based on some reasonable assumptions, the GM of EKF is simplified under the principle that the computation accuracy of PA is decreased as little as possible, but its computation burden is reduced as much as possible. After the replacement of the GM of the EKF by the innovatively designed GM, a new CF, named time-efficient complementary Kalman gain filter (TCF), is proposed. The simulation and experimental results show that TCF performs better than two CFs and is close to EKF in terms of attitude estimation accuracy but uses nearly the same computation time as the two CFs and about half of the computation time required by EKF.

Rotor Position Estimation Strategy Based on Rotating High-Frequency Voltage Injection Using Synchronous Frequency Filter

In order to improve the performance of motor drive systems, this paper introduces a sensorless control method for interior permanent magnet synchronous motors (IPMSM) based on the rotating high-frequency voltage injection method. The demodulation method using a synchronous frequency filter (SFF) instead of a bandpass filter (BPF) and a high pass filter (HPF) is proposed. The structure and transfer function of SFFs are introduced, and their characteristics are simulated and analyzed. Therefore, the center frequency of SFFs can be adaptively adjusted according to the estimated rotational speed, which is convenient for frequency setting. Finally, according to the proposed rotating high-frequency voltage injection method, the simulation model is built. The experimental results verify that the control strategy can maintain good position estimation accuracy, better speed tracking performance and anti-load disturbance performance.

An adaptive gradient descent attitude estimation algorithm based on a fuzzy system for UUVs

An adaptive gradient descent algorithm (AGDA) based on a fuzzy system is proposed to improve the attitude estimation accuracy and adaptability of unmanned underwater vehicles (UUVs) under various ocean environments. First, the algorithm uses current and historical gyroscope data to predict the quaternion of the current moment. Then, the accelerometer data are calibrated according to the predicted quaternion, and the gradient direction is obtained. At the same time, a fuzzy system generated by the genetic algorithm is used to solve the adaptive coefficient. Finally, the algorithm uses the gradient descent method to achieve data fusion, compensating for the accumulated error of the gyroscope. The experimental results show that the prediction of the quaternion and the adaptive coefficient can effectively improve the accuracy and environmental adaptability of the algorithm, and the proposed algorithm has better dynamic and static performances than traditional attitude estimation algorithms.

Adaptive Iterative Learning Control-Based Rotor Position Harmonic Error Suppression Method for Sensorless PMSM Drives

Accurate rotor position estimation is important to ensure the high-performance operation of position sensorless permanent magnet synchronous motor (PMSM) drives. To suppress the estimated rotor position harmonic error (ERPHE) caused by inverter nonlinearities, flux spatial harmonics, and the current measurement error in high-frequency signal injection-based schemes, an adaptive iterative learning control-based online suppression method is proposed in this article. This method constructs a P-type iterative learning rotor position observer with the forgetting factor to obtain the tracking error and generate the compensation value in real time. The stability, the convergence, and the parameter sensitivity of this observer are analyzed in detail. To improve the suppression effect of the ERPHE for different loads and speeds, an iterative learning gain self-tuning strategy is investigated. The proposed method does not require the offline detection and the discrimination of harmonic frequencies, which has strong suppression ability for different harmonic error components. The effectiveness is verified by experiments on a 2.2-kW interior PMSM drive platform.

Improved Position Sensorless Drive Method for IPMSM Based on Fully Discretized Model and Inductance Identification Utilizing Current Ripple

This article proposes an improved position sensorless drive method for interior permanent magnet synchronous machines (IPMSM) in medium- and high-speed regions, which relies on the fully discretized extended back-electromotive force (EEMF) model and online inductance identification. First, the fully discretized EEMF model is developed for constructing position observers, which overcomes the accuracy deterioration of the conventional Euler-discretized model with the speed increasing. And then, an online inductance identification method for IPMSM, which utilizes the inherent pulsewidth modulation (PWM) current ripple of the voltage source inverter (VSI), is proposed to improve the position estimation accuracy. Compared with the traditional parameter identification method, the proposed method does not require the additional signal injection, therefore, the undesirable torque fluctuation and additional voltage occupancy due to signal injection are avoided. Benefiting from the fully discretized EEMF model and the estimated inductances, the improved rotor position estimation of IPMSM can be achieved. Finally, the proposed method is verified by simulations and experiments.

Comparative Analysis and Optimization of Novel Pulse Injection Sensorless Drive Methods for Fault-Tolerant DC Vernier Reluctance Machine

Multiphase dc-excited Vernier reluctance machine (DC-VRM) exhibits the merits of robust structure, small torque ripple, and good fault-tolerant ability. Developing advanced sensorless drive methods can further promote its application in the safety-critical system. In this article, pulse injection sensorless drive methods are optimized in a six-phase DC-VRM parallel H-bridge drive system to strengthen their acceleration performance and fault-tolerant ability. The acceleration performance studied in this article corresponds to the acceleration speed during the startup stage. By the full-phase alternative pulse injection method (APIM), each phase can be excited independently to avoid mutual-inductance influence on position estimation, but this method suffers from a long communication delay and relatively poor acceleration performance. A reduced-phase APIM can reduce detection time, but the lack of detected phase may influence position estimation accuracy and fault-tolerant ability. To solve these problems, a novel vertical-axis synchronous pulse injection method is proposed and compared with previous methods in this article. The key is to inject detection pulses into vertical-axis phases simultaneously, thus reducing the detection time and improving the torque generation. It is proved that the influence of mutual inductance on position estimation can be ignored, and the detection accuracy and acceleration performance can be improved without deterioration of fault-tolerant ability.

Attitude estimation of connected drones based on extended Kalman filter under real outdoor environments

In this study, we design connected multiple drones through electromagnets, and it requires an attitude estimation method that does not use magnetic sensors. In order to solve this problem, we propose an extended Kalman filter-based attitude estimation method based on the multiple global positioning system (GPS) to substitute the magnetic sensor. Additionally, we demonstrate that combining velocity vectors allows for highly accurate attitude estimation. We compare the estimation accuracy and stability of attitude estimation in simulation and real environments using two types of experiments. It is confirmed that the accuracy of yaw angle estimation of the proposed method is improved 36.1% in simulation and 29.7% in the real environments compared to conventional extended Kalman filter.

Development of an Online Adaptive Parameter Tuning vSLAM Algorithm for UAVs in GPS-Denied Environments.

In recent years, unmanned aerial vehicles (UAVs) have been applied in many fields owing to their mature flight control technology and easy-to-operate characteristics. No doubt, these UAV-related applications rely heavily on location information provided by the positioning system. Most UAVs nowadays use a global navigation satellite system (GNSS) to obtain location information. However, this outside-in 3rd party positioning system is particularly susceptible to environmental interference and cannot be used in indoor environments, which limits the application diversity of UAVs. To deal with this problem, in this paper, a stereo-based visual simultaneous localization and mapping technology (vSLAM) is applied. The presented vSLAM algorithm fuses onboard inertial measurement unit (IMU) information to further solve the navigation problem in an unknown environment without the use of a GNSS signal and provides reliable localization information. The overall visual positioning system is based on the stereo parallel tracking and mapping architecture (S-PTAM). However, experiments found that the feature-matching threshold has a significant impact on positioning accuracy. Selection of the threshold is based on the Hamming distance without any physical meaning, which makes the threshold quite difficult to set manually. Therefore, this work develops an online adaptive matching threshold according to the keyframe poses. Experiments show that the developed adaptive matching threshold improves positioning accuracy. Since the attitude calculation of the IMU is carried out based on the Mahony complementary filter, the difference between the measured acceleration and the gravity is used as the metric to online tune the gain value dynamically, which can improve the accuracy of attitude estimation under aggressive motions. Moreover, a static state detection algorithm based on the moving window method and measured acceleration is proposed as well to accurately calculate the conversion mechanism between the vSLAM system and the IMU information; this initialization mechanism can help IMU provide a better initial guess for the bundle adjustment algorithm (BA) in the tracking thread. Finally, a performance evaluation of the proposed algorithm is conducted by the popular EuRoC dataset. All the experimental results show that the developed online adaptive parameter tuning algorithm can effectively improve the vSLAM accuracy and robustness.

Simplex Back Propagation Estimation Method for Out-of-Sequence Attitude Sensor Measurements.

For a small satellite, the processor onboard the attitude determination and control system (ADCS) is required to monitor, communicate, and control all the sensors and actuators. In addition, the processor is required to consistently communicate with the satellite bus. Consequently, the processor is unable to ensure all the sensors and actuators will immediately respond to the data acquisition request, which leads to asynchronous data problems. The extended Kalman filter (EKF) is commonly used in the attitude determination process, but it assumes fully synchronous data. The asynchronous data problem would greatly degrade the attitude determination accuracy by EKF. To minimize the attitude estimation accuracy loss due to asynchronous data while ensuring a reasonable computational complexity for small satellite applications, this paper proposes the simplex-back-propagation Kalman filter (SBPKF). The proposed SBPKF incorporates the time delay, gyro instability, and navigation error into both the measurement and covariance estimation during the Kalman update process. The performance of SBPKF has been compared with EKF, modified adaptive EKF (MAEKF), and moving–covariance Kalman filter (MC-KF). Simulation results show that the attitude estimation error of SBPKF is at least 30% better than EKF and MC-KF. In addition, the SBPKF’s computational complexity is 17% lower than MAEKF and 29% lower than MC-KF.

REVIO: Range- and Event-Based Visual-Inertial Odometry for Bio-Inspired Sensors.

Visual-inertial odometry is critical for Unmanned Aerial Vehicles (UAVs) and robotics. However, there are problems of motion drift and motion blur in sharp brightness changes and fast-motion scenes. It may cause the degradation of image quality, which leads to poor location. Event cameras are bio-inspired vision sensors that offer significant advantages in high-dynamic scenes. Leveraging this property, this paper presents a new range and event-based visual-inertial odometry (REVIO). Firstly, we propose an event-based visual-inertial odometry (EVIO) using sliding window nonlinear optimization. Secondly, REVIO is developed on the basis of EVIO, which fuses events and distances to obtain clear event images and improves the accuracy of position estimation by constructing additional range constraints. Finally, the EVIO and REVIO are tested in three experiments-dataset, handheld and flight-to evaluate the localization performance. The error of REVIO can be reduced by nearly 29% compared with EVIO in the handheld experiment and almost 28% compared with VINS-Mono in the flight experiment, which demonstrates the higher accuracy of REVIO in some fast-motion and high-dynamic scenes.

Human-to-Human Position Estimation System Using RSSI in Outdoor Environment.

Methods to prevent collisions between people to avoid traffic accidents are receiving significant attention. To measure the position in the non-line-of-sight (NLOS) area, which cannot be directly visually recognized, position-measuring methods use wireless-communication-type GPS and propagation characteristics of radio signals, such as received signal strength indication (RSSI). However, conventional position estimation methods using RSSI require multiple receivers, which decreases the position estimation accuracy, owing to the presence of surrounding buildings. This study proposes a system to solve this challenge using a receiver and position estimation method based on RSSI MAP simulation and particle filter. Moreover, this study utilizes BLE peripheral/central functions capable of advertising as the transmitter/receiver. By using the advertising radio waves, our method provides a framework for estimating the position of unspecified transmitters. The effectiveness of the proposed system is evaluated in this study through simulations and experiments in actual environments. We obtained an error average of the distance to be 1.6 m from the simulations, which shows the precision of the proposed method. In the actual environment, the proposed method showed an error average of the distance to be 3.3 m. Furthermore, we evaluated the accuracy of the proposed method when both the transmitter and receiver are in motion, which can be considered as a moving person in the outdoor NLOS area. The result shows an error of 4.5 m. Consequently, we concluded that the accuracy was comparable when the transmitter is stationary and when it is moving. Compared with conventional path loss, the model can measure distances of 3 m to 10 m, whereas the proposed method can estimate the “position” with the same accuracy in an outdoor environment. In addition, it can be expected to be used as a collision avoidance system that confirms the presence of strangers in the NLOS area.

Visual SLAM for Dynamic Environments Based on Object Detection and Optical Flow for Dynamic Object Removal

In dynamic indoor environments and for a Visual Simultaneous Localization and Mapping (vSLAM) system to operate, moving objects should be considered because they could affect the system’s visual odometer stability and its position estimation accuracy. vSLAM can use feature points or a sequence of images, as it is the only source of input that can perform localization while simultaneously creating a map of the environment. A vSLAM system based on ORB-SLAM3 and on YOLOR was proposed in this paper. The newly proposed system in combination with an object detection model (YOLOX) applied on extracted feature points is capable of achieving 2–4% better accuracy compared to VPS-SLAM and DS-SLAM. Static feature points such as signs and benches were used to calculate the camera position, and dynamic moving objects were eliminated by using the tracking thread. A specific custom personal dataset that includes indoor and outdoor RGB-D pictures of train stations, including dynamic objects and high density of people, ground truth data, sequence data, and video recordings of the train stations and X, Y, Z data was used to validate and evaluate the proposed method. The results show that ORB-SLAM3 with YOLOR as object detection achieves 89.54% of accuracy in dynamic indoor environments compared to previous systems such as VPS-SLAM.

Robust and Efficient Velocity Estimation for MAVs With an RGB-D Camera

Micro Aerial Vehicles (MAVs) have become increasingly popular due to their wide applications in many areas. For autonomous navigation and safe control of MAVs, it is essential to have accurate and reliable velocity and position estimation. However, due to limited computational power and payload, it is still challenging for autonomous operation of MAVs in complex environments. In this paper, we propose a robust and efficient velocity estimation framework for MAVs with a single downward-facing RGB-D camera, which is able to provide metric velocity estimation in three dimensions as well as yaw rate in real-time without the fusion of additional sensors. Unlike traditional algorithms, our method uses kernel cross-correlators (KCCs) to efficiently determine optical flow for motion estimation, which does not rely on costly feature extraction and matching process. Moreover, we utilize depth images to estimate the vertical velocity of MAVs without the assumption of a flat ground. Autonomous flight tests on a quadrotor in complex environments demonstrate the robustness and efficiency of our method.

Generalized LOAM: LiDAR Odometry Estimation With Trainable Local Geometric Features

This letter presents a LiDAR odometry estimation framework called Generalized LOAM. Our proposed method is generalized in that it can seamlessly fuse various local geometric shapes around points to improve the position estimation accuracy compared to the conventional LiDAR odometry and mapping (LOAM) method. To utilize continuous geometric features for LiDAR odometry estimation, we incorporate tiny neural networks into a generalized iterative closest point (GICP) algorithm. These neural networks improve the data association metric and the matching cost function using local geometric features. Experiments with the KITTI benchmark demonstrate that our proposed method reduces relative trajectory errors compared to the other LiDAR odometry estimation methods.

Interacting Multiple Model Adaptive Unscented Kalman Filter for Accurate Localization of a High-Dynamic Motion Mobile Robot Using Wireless Sensor Network

Accurate and precise positioning of a mobile robot is a critical problem in the navigation system, and it has become difficult for localization indoors and in GPS-denied areas. Odometry, which is based on dead reckoning, cannot usually provide long-term accurate position estimates. As a result, integrating encoder-based odometry with Wireless Sensor Network (WSN) measurements is recommended as a good solution for minimizing divergences due to uncertainties. The Kalman filtering technique is used to improve state estimation and measurement fusion. Therefore, this paper describes the design and implementation of an Unscented Kalman Filter (UKF) with an adaptive tuning technique to adjust the process noise covariance matrix, resulting in better position estimation, particularly in high-dynamic motions of the mobile robot. Furthermore, for a mobile robot, this work proposes an Interacting Multiple Model (IMM) method with two dynamic motion models, i.e. Constant Velocity (CV) and Coordinated Turn (CT), to deliver high performance with minimal computational overhead. Several experiments on two types of mobile robots were conducted to evaluate and compare the effectiveness of the proposed localization algorithm. The proposed localization technique outperformed the other three methods and demonstrated its effectiveness in high dynamic motions due to the adaptive tuning system.

Attitude Control Method of Unmanned Helicopter Based on Asymmetric Tracking Differentiator and Fal-Extended State Observer

In order to meet the constraints of velocity and acceleration of displacement and attitude motion of an unmanned helicopter during an automatic carrier landing mission, an asymmetric tracking differentiator, which could set the speed and acceleration limits in two directions of tracked signal motion respectively, was derived based on the tracking differentiator in the active disturbance rejection control method. Based on the proposed asymmetric tracking differentiator, a fal-extended state observer based on the fal function was added to construct the attitude and angular velocity controller which is programmable during the transition process of unmanned helicopters. The mathematical simulation and result analysis show that the newly proposed attitude estimation algorithm effectively compensates for the deficiencies of the existing methods, improves the anti-jamming capability and the accuracy of attitude estimation in the maneuvering process, achieving the expected design purposes.

Chattering suppression of the sliding mode observer for marine electric propulsion motor based on piecewise power function

The sliding mode observer (SMO)–based sensorless control is essentially a discontinuous switching control algorithm. Therefore, there is large speed or torque chattering in the permanent magnet synchronous motor (PMSM) with existing sliding mode observer–based control methods. In order to solve this problem well, an algorithm based on an improved sliding mode observer is proposed. In detail, the piecewise power function approach is used to replace the traditional ones, that is, the sgn function or the sigmoid function approach. Simulation verifications are conducted to validate the effectiveness of the proposed observer. It shows that the piecewise power function–based sliding mode observer algorithm has obvious advantages in the stability of the three-phase current, namely, it improves the estimation accuracy of the rotor position, rotor speed, and dynamic response to various loads. For further verification, the proposed sliding mode observer algorithm is implemented and verified in a marine electric propulsion motor with a DSP chip of TMS320F28377D. Experimental results validate that the piecewise power function–based sliding mode observer algorithm has high control accuracy and fewer fluctuations, which are all vital in marine engineering.

A Novel SPMSM Sensorless Drive Using Discrete-Time Synchronous-Frequency Adaptive Observer Under Low Frequency Ratio

To improve the stability and accuracy of the estimated position using the conventional sliding mode observer (CSMO) under low frequency ratio (LFR), this article proposes a novel discrete-time synchronous-frequency adaptive observer (SFAO) for surface-mounted permanent magnet synchronous motor (SPMSM) sensorless drives. The proposed SFAO has merits of high amplitude and zero-phase lag of resonant controller at synchronous frequency. The sign function and the low-pass filter (LPF) of CSMO in the proposed observer can be removed and conducive to dispel the inherent chattering of the CSMO as well as phase shift produced by the LPF. Then, taking into account the digital control delay and discretization truncation error, an exact complex-vector discrete-time model in the stationary coordinate is developed. Based on that, the direct discrete-time domain design approach is utilized in the proposed observer, which can be devised by pole placement directly on complex <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">z</i> -domain to assure the stability and position estimation accuracy under LFR. Finally, the availability of the proposed discrete-time SFAO scheme is verified via experiments on a 2.1-kW SPMSM drive.

A Fast Robust In-Motion Alignment Method for Laser Doppler Velocimeter-Aided Strapdown Inertial Navigation System

With ultra-high velocity measurement accuracy, laser Doppler velocimeter (LDV) is promising to replace the odometer to provide vehicle velocity information in the process of land integrated navigation. This paper investigates the fast in-motion initial alignment for LDV-aided strapdown inertial navigation system (SINS). Aiming at the problem that the installation misalignment angles and scale factor error of LDV will reduce the alignment accuracy and the alignment speed, a Robust Double Gain Square-Root Unscented Quaternion Estimator (RDGSR-USQUE) method with good effect at large misalignment angle is proposed. In RDGSR-USQUE, the influence of unknown parameter error is considered in the construction of process model and measurement model and a double gain structure is proposed to improve the convergence speed based on the usually neglected posterior measurement residual information. RDGSR-USQUE method improves the defects of the traditional Unscented Quaternion Estimator (USQUE) method, such as poor noise resistance, slow convergence speed under large misalignment angle and easy to lead to non-positive definite covariance matrix. This will help to estimate and compensate unknown parameter errors while estimating attitude, so as to improve the accuracy of process model and measurement model, and finally improve the accuracy of attitude estimation. The performance of the proposed scheme is verified by vehicle field test. The results show that the proposed method has higher alignment accuracy, faster convergence speed and stronger robustness than three other compared methods, and the estimated installation misalignment angle and scale factor of LDV are satisfactory in general cases.