Position Estimation Performance Research Articles

Research on Operation Characteristics of Permanent Magnet Synchronous Motor at Zero and Low Speeds Based on Pulse Vibration High-Frequency Injection Method

In order to reduce costs, compress space, and improve system stability under harsh operating conditions, the current vehicle motor drive systems often use position sensorless control methods. However, due to the introduction of filters and the hysteresis of position observers, the position sensorless control has the problem of deteriorating dynamic performance when vehicles start from zero and low speeds or their loads change. Therefore, this article focuses on the problem of position sensorless control applied by permanent magnet synchronous motors when vehicles start and operate at zero and low speed. Combined with high-frequency pulse vibration injection method, the relationship between the types of position observers, parameter selection, and position tracking performance is analyzed and compared. The short-pulse injection method is proposed to locate the initial position of the motor, overcoming the inherent 180° position deviation of pulse vibration high-frequency injections. Subsequently, the impact of the amplitude and frequency of the injected high-frequency signal on the performance of position estimation is focused on. Considering the adverse effects caused by the phase delay of the filter, a design method for filter parameters is proposed to achieve a smooth start and operation of the permanent magnet synchronous motor under position sensorless control. Finally, the rationality of the theoretical analysis and the effectiveness of the adopted methods are fully verified through simulation and experiments.

Extended Kalman Filter-Based Vehicle Tracking Using Uniform Planar Array for Vehicle Platoon Systems.

We develop an extended Kalman filter-based vehicle tracking algorithm, specifically designed for uniform planar array layouts and vehicle platoon scenarios. We first propose an antenna placement strategy to design the optimal antenna array configuration for precise vehicle tracking in vehicle-to-infrastructure networks. Furthermore, a vehicle tracking algorithm is proposed to improve the position estimation performance by specifically considering the characteristics of the state evolution model for vehicles in the platoon. The proposed algorithm enables the sharing of corrected error transition vectors among platoon vehicles, for the purpose of enhancing the tracking performance for vehicles in unfavorable positions. Lastly, we propose an array partitioning algorithm that effectively divides the entire antenna array into sub-arrays for vehicles in the platoon, aiming to maximize the average tracking performance. Numerical studies verify that the proposed tracking and array partitioning algorithms improve the position estimation performance.

3D Position Estimation of Objects for Inventory Management Automation Using Drones

With the recent development of drone technology, drones are being used in various fields. Drones have the advantage of being equipped with various devices to move freely and perform various tasks. In the field of inventory management, many studies have been conducted into management automation based on the drone. Drones scan a marker, such as a quick response code (QR code), attached to the shelves to obtain location information of the shelves on which the inventory is loaded. At the same time, drones perform inventory management by scanning the marker attached to the inventory to obtain inventory information. However, unlike indoor warehouses, where grids or shelves are well-defined, a storage yard is not fixed in the location where the inventory is stored. It is difficult to recognize the loading position from the marker for a storage yard without shelves and grids. Furthermore, the loading position of the inventory is not fixed. For the automation of inventory management of warehouses where shelves and grids are undefined, this paper proposes a framework that estimates the inventory 3D position in the video frame based on a deep learning model. The proposed framework uses the image segmentation model to detect and decode the marker in the video frame to estimate the 3D position of a drone and inventory. In addition, the estimated inventory 3D position is corrected using the continuity of the video frame. Experiment results on the video dataset verified that the proposed framework improved the 3D position estimation performance of the inventory. Consequently, efficient inventory management based on drones can be performed through the proposed framework for the 3D position estimation of inventory in all types of warehouses.

Large-Scale Indoor Localization Solution for Pervasive Smartphones Using Corrected Acoustic Signals and Data-Driven PDR

With continuous and accelerated urbanization, a large number of location-based services (LBS) have shifted from outdoor to indoor. The pervasive smartphone-based localization has been the subject of extensive work, including signals, algorithms, technologies, solutions, and applications. However, no single ubiquitous technology or solution exists for performing indoor positioning similar to the global navigation satellite system (GNSS) in the outdoor environment. The aim of this work is to develop a practical, precise, and economic smartphone-based localization solution. In order to address the challenges of utilizing the limited audible-band acoustic signal in pervasive smartphone localization, i.e., signal detection, correction, and evaluation, we present a low-cost anchor hardware, two-step signal detection method, data-driven pedestrian dead reckoning (PDR), and robust positioning algorithm. Moreover, we further propose acoustic measurement compensation approaches and measurement quality evaluation and control strategy (MQECS) to improve the performance of position estimation. Six phones, including Huawei Mate9, P9 Plus, OnePlus 6, Honor 8, Mi 10, and Google Pixel 3 are used to evaluate the localization performance in three typical wide area indoor scenarios (i.e., convention center, parking lot, and dining-hall). The total testbed area is accumulated to more than 8,800 square meters. The experimental results demonstrate that the proposed method achieves average positioning accuracies of 0.34 m (static) and 0.67 m (dynamic). In addition, the results show that the overall performance, repeatability, and stability are superior for different scenarios and devices.

Improved Position Observer Using Adaptive Training Control-Based Filter for Interior Permanent Magnet Synchronous Motor Drives

Rotor position is the key information to achieve superior performance for sensorless control of interior permanent magnet synchronous motor (IPMSM). Nevertheless, the back electromotive force (EMF) model-based position estimation suffers from severe contamination from the fifth and seventh harmonics resulting from inverter nonlinearity and flux spatial harmonics. Therefore, an adaptive training control-based adaptive filter combined with a sliding-mode observer (SMO) is presented for harmonics rejection in the estimated back-EMF, and thus improving the rotor position estimation performance. This method, based on the steepest descent algorithm, is capable of self-adjusting harmonic coefficients to obtain the fundamental component online under various frequency conditions adaptively. Additionally, the proposed method has simpler structure and less calculation burden since its reference signal is self-generated without external injection compared to the conventional method. The effectiveness is verified by experiments at a 1.5-kW IPMSM drive platform.

Speed Sensorless Control Method of Synchronous Reluctance Motor Based on Resonant Kalman Filter

When estimating the active back electromotive force (AEMF) of synchronous reluctance motor (SynRM) at medium and high speed, the traditional Kalman Filter (KF) will produce large phase delay due to the low-pass filter characteristics. A novel speed sensorless control scheme of SynRM based on resonant Kalman filter (RKF) is proposed in this paper. Firstly, the AEMF observer based on KF is designed and the bandwidth performance is analyzed. To solve the phase delay problem caused by KF, a generalized integral resonance perturbation estimator (GIRPE) is introduced to the framework of KF and the proposed RKF is formed. The estimation error of AEMF is compensated through GIRPE in the current error feedback loop of KF observer. In this way, the high-precision position estimation at high speed range is obtained. Finally, an improved dynamic position compensator (IDPC) is proposed to enhance the position estimation performance under dynamic case. The effectiveness of proposed scheme is validated at a 1.5kW SynRM drive, and the computational burden of the proposed method is assessed quantitatively.

Localization System for Vehicle Navigation Based on GNSS/IMU Using Time-Series Optimization with Road Gradient Constrain

In this paper, we propose a GNSS/IMU localization system for mobile robots when wheel speed sensors cannot be attached. Highly accurate location information is required for autonomous navigation of mobile robots. A typical method of acquiring location information is to use a Kalman filter for position estimation. The Kalman filter is a maximum-likelihood estimation method that assumes normally distributed noise. However, non-normally distributed GNSS multipath noise that frequently occurs in urban environments causes the Kalman filter to break down, and degrades the estimation performance. Other GNSS/IMU localization methods capable of lane-level estimation in urban environments use wheel speed sensors, which are unsuitable for the present situation. In this study, we aim to improve the performance of lane-level localization by adding a vehicle speed estimation function to adapt the method to those requiring wheel speed sensors. The proposed method optimizes time-series data to accurately compensate for accelerometer bias errors and reduce GNSS multipath noise. The evaluation confirmed the effectiveness of the proposed method, with improved velocity and position estimation performance compared with the Kalman filter method.

Multistatic Localization Algorithm for Moving Object with Constant Acceleration Eliminating Extra Variables

This correspondence aims to locate the unknown object moving with constant acceleration in multistatic systems by using bistatic range (BR), bistatic range rate (BRR), and derivative of bistatic range rate (DBRR) measurements obtained at a single time instant. Albeit previous studies can attain optimal performance under mild noise conditions, the localization model used to solely estimate the target position and velocity cannot be applied to some generalized situations with unknown constant acceleration. To address this problem, we start with the fundamental equations of traditional methods and propose an algebraic solution consisting of two stages. In the first stage, we obtain initial estimates of target position, velocity, and acceleration without nuisance variables by using the projection matrix. In the second stage, the estimation accuracy is improved by refining initial estimates. The proposed estimator is proven to achieve the Cramer-Rao lower bound (CRLB) accuracy analytically when the measurement noise is low. Simulation results validate the theoretical analysis and reveal that the proposed method can determine the target acceleration while outperforming the previous studies for position and velocity estimation performance.

Research on Position Sensorless Control of RDT Motor Based on Improved SMO with Continuous Hyperbolic Tangent Function and Improved Feedforward PLL

With the increasing use of electric propulsion ships, the emergence of the shaftless rim-driven thruster (RDT) as a revolutionary integrated motor thruster is gradually becoming an important development direction for green ships. The shaftless structure of RDTs leads to their dependence on position sensorless control techniques. In this study, a novel control algorithm using a composite sliding mode observer (SMO) with a modified feed-forward phase-locked loop (PLL) is presented for achieving high accuracy position and speed control of shaftless RDT motors. The deviation between the observed and actual currents is exploited to develop a current SMO to extract back electromotive force (back-EMF) errors. On this basis, a back-EMF observer is established to achieve accurate estimation of the back-EMF. The basic structure of the PLL was modified and incorporates a speed feedforward mechanism, which enhances the performance of rotor position estimation and facilitates bidirectional rotation. The stability of the algorithm has been verified in Matlab/Simulink for a range of steady-state, dynamic, and ship propeller loading conditions. Remarkably, the control algorithm boasts an impressive adjustment time of approximately 0.006 s and its position estimation error may be as low as 0.03 rad. Simulation results highlight the performance of the algorithm to achieve bidirectional rotation, while exhibiting fast convergence, minimal vibration, exceptional control accuracy, and robustness.

Cramér–Rao Lower Bounds on 3D Position and Orientation Estimation in Distributed Ranging Systems

As radio frequency (RF) hardware continues to improve, many technologies that were traditionally impractical have suddenly become viable alternatives to legacy systems. Two-way ranging (TWR) is often considered a poor positioning solution for airborne and other vehicular navigation systems due to its low precision, poor angular resolution, and precise timing requirements. With the advent of modern RF hardware and advanced processing techniques, however, modern studies have experimentally demonstrated TWR systems with an unprecedented, sub-centimeter ranging precision with low size, weight, power, and cost (SWaP-C) consumer-grade hardware. This technique enables a new class of positioning, navigation, and timing (PNT) capabilities for urban and commercial aircraft but also instigates new system design challenges such as antenna placement, installation of new electronics, and design of supporting infrastructure. To inform these aircraft design decisions, we derive 2D and 3D Cramér–Rao lower bounds (CRLBs) on position and orientation estimation in a multi-antenna TWR system. We specifically formulate these bounds as a function of the number of antennas, platform geometry, and geometric dilution of precision (GDoP) to inform aircraft design decisions under different mission requirements. We simulate the performance of several classic position and orientation estimators in this context to validate these bounds and to graphically depict the expected performance with respect to these design considerations. To improve the accessibility of these highly theoretical results, we also present a simplified discussion of how these bounds may be applied to common airborne applications and suggest best practices for using them to inform aircraft design decisions.

Research on Permanent Magnet Synchronous Motor Sensorless Control System Based on Integral Backstepping Controller and Enhanced Linear Extended State Observer

The traditional sensorless control system of permanent magnet synchronous motor (PMSM) has the problems of low estimation accuracy and poor anti-interference ability. Moreover, the position estimation performance is subjected to position harmonic ripples caused by inverter nonlinearities and flux spatial harmonics. To optimize the dynamic performance of the PMSM sensorless control system, this paper proposes a sensorless control scheme that combines integral backstepping control with enhanced linear extended state observer (ELESO). The ELESO consists of two linear extended state observers (LESOs), which estimate the internal and external disturbances of the system, to improve the estimation accuracy of rotor position. Then, the integral backstepping controller processes the estimated rotor position and speed information to obtain d and q-axis voltages. The sensorless control scheme is implemented in the Matlab/Simulink and verified by experiments. The simulation and experiment show that the scheme can effectively suppress load interference and improve control accuracy.

Research on a Variable-Leakage-Flux Permanent Magnet Motor Control System Based on an Adaptive Tracking Estimator

Due to the characteristics of inductance parameter mismatch and back electromotive force harmonics caused by novel leakage flux branches and other non-ideal factors for the variable-leakage-flux permanent magnet (VLF-PM) motor, its control system suffers from a deteriorated performance of the rotor position estimation. To overcome the problems mentioned above, an adaptive tracking estimator of the rotor position is proposed in this paper for the VLF-PM motor control system. First, the proposed method simplifies the VLF-PM motor mathematical model and reduces the effect of inductance parameter variations according to the active flux concept. Then, robust and gradient descent algorithms are utilized to maintain the robustness of inductance parameter variations and eliminate the specific order harmonics owing to the novel leakage flux branches. Meanwhile, the accuracy and stability are enhanced. Furthermore, the position compensation based on the current adaptive tracking strategy is proposed to compensate the rotor position error caused by other non-ideal factors. Finally, the feasibility of the proposed estimated system is verified.

Position-and-Torque-Sensorless Admittance Control with State Estimation Considering dq-axis Cross-Coupling Factors

This paper presents a position estimation compensation method considering dq-axis cross-coupling factors of Interior Permanent Magnet Synchronous Motors (IPMSMs) inductance for position-and-torque-sensorless admittance control. The cross-coupling factors are from dq-axis mutual inductance and deteriorate control and position estimation performance in control methods without the model. Since dq-axis cross-coupling factors of inductance vary with the rotor position and the current, measurement and estimation of the values are difficult. The proposed method estimates the cross-coupling factors and position estimation errors using dq-axis current variation from the injected voltage. In addition, the proposed method compensates the estimated position based on a voltage equation, including the dq-axis cross-coupling factors, by adding the estimated error. As a result, the vibration of current, velocity, and estimated reaction torque were reduced, and the performance of a position-and-torque-sensorless admittance control system was improved. The experimental results show the proposed method's validity and response by the sensorless admittance control.

A Lag Compensation-Enhanced Adaptive Quasi-Fading Kalman Filter for Sensorless Control of Synchronous Reluctance Motor

A novel position estimation strategy based on lag compensation-assisted adaptive quasi-fading Kalman filter (LC-AQFKF) is proposed for synchronous reluctance motor (SynRM) sensorless drive in this article. In LC-QFKF, the quasi-fading factor is derived to avoid the harsh assumptions of conventional adaptive fading Kalman filter, and accessibility of the method is improved while ensuring the estimation accuracy. The computational efficiency of LC-AQFKF is promoted by introducing the quasi-fading factor into the prediction error covariance matrix. Moreover, the frequency characteristic of AQFKF-based active back electromotive force observer is analyzed, and the phase lag problem in high-speed situations caused by the low-pass filtering property of AQFKF is overcome. In this way, the estimation accuracy of the rotor position is significantly enhanced. Besides, the double dynamic position compensation method is put forward to strengthen the position estimation performance of sensorless SynRM drive under dynamic conditions. The effectiveness of the proposed scheme is validated at a 1.5 kW SynRM drive.

A Sensorless Control Strategy for Permanent Magnet Synchronous Motor at Low Switching Frequency

The high-frequency (HF) square-wave voltage injection method can be used in permanent magnet synchronous motor (PMSM) drive systems. However, when the switching frequency is too low, the injection frequency will also decrease, which will reduce the update frequency of the HF response current, making it difficult to extract the position quadrature signal and affecting the accuracy of position estimation and control performance. This paper proposes a method for extracting position quadrature signals based on sampling rate transformation, and a signal processing strategy based on Cascade Integrator Comb (CIC) interpolation filtering, which can solve the problem of waveform distortion caused by the low sampling rate of the extracted position quadrature signal. This strategy can increase the sampling rate of the position quadrature signal to the pulse width modulation (PWM) update frequency by interpolating in the sampling current, thereby reducing the harmonic content of the position quadrature signal and improving the position estimation. precision. In addition, the PWM update frequency and estimated rotational speed information are used to compensate for the delay caused by position estimation and inverter update, which effectively improves the accuracy of position estimation. Finally, the effectiveness of the proposed control strategy is verified by simulation.

Square-Wave Voltage Injection Based PMSM Sensorless Control Considering Time Delay at Low Switching Frequency

This article proposes a global delay compensation method to improve the sensorless control performance of square-wave voltage injection (SWVI) over low switching frequency applications. The cost function with delay information is preliminarily constructed by simple calculation of high-frequency (HF) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">αβ</i> -axes current and estimated position; second, the cost function is minimized by the gradient descent method, so as to estimate the delay time and corresponding compensation angle. The compensation angle that estimated delay time multiplies rotor speed is used for reducing the lag angle in the estimated position, thus improves the current control performance. At the same time, the inverse of the cosine signal is proposed to substitute the conventional differential current demodulation of SWVI. In addition, the compensation angle is used to offset the phase shift in the cosine signal, thus suppressing the amplitude error of HF response current caused by time delay. Hence, the SWVI method with the proposed delay compensation has good position estimation performance. Finally, a 1.5 kW interior permanent magnet synchronous motor test bench is adopted to demonstrate the effectiveness of the proposed method

Static-Errorless Rotor Position Estimation Method Based on Linear Extended State Observer for IPMSM Sensorless Drives

This article presents a static-errorless rotor position estimation method based on the linear extended state observer (LESO) for interior permanent magnet synchronous motor (IPMSM) drives. Two second-order LESOs are utilized to estimate the α-β axis back-EMFs. A third-order LESO is incorporated into the quadrature phase-locked loop (QPLL) to achieve a high robustness of position tracking against external disturbance. In addition, considering that the nonideal back-EMF will bring DC and harmonic fluctuation errors to the estimated position, an enhanced LESO-based QPLL with static-errorless rotor position estimation is proposed. On the one hand, the DC position esti mation error caused by the phase lag of the back-EMF estimator is analyzed and compensated. On the other hand, to suppress the position harmonic fluctuations induced from the nonsinusoidal back-EMFs, a second-order generalize integrator (SOGI) is embedded in the feedforward path of the LESO-based QPLL. The experimental results on the 1.0 kW IPMSM drive platform show that, compared to the conventional method, the proposed method can achieve better position estimation performance both in steady-state operation and in transient-state operation.

Analysis of Rotor Position Detection Performance According to the Frequency of Square Waveform Voltage in the Harmonic Injection Sensorless Method through HILS

In this paper, the rotor position estimation performance of the sensorless scheme for permanent magnet synchronous motors (PMSMs) implemented through the injection of high-frequency square-wave voltage according to the frequency of the square-wave voltage is presented through HILS (Hardware In the Loop Simulation) experiments. An inverter using an IGBT device usually has a switching frequency of around 15 kHz. On the other hand, GaN devices that can be switched on and off at frequencies higher than 100 kHz have been recently developed, and research is being actively conducted to apply GaNs to a variable speed system. The purpose of this study is to conduct HILS experiments to analysis the rotor position estimation ability of the sensorless technique in cases where a high switching frequency was applied, such as GaN devices, with that of a system having a usual switching frequency, such as IGBT. In the HILS system used in this study, an inverter and motor model implemented with Simulink are located in a real-time simulator. A sensorless motor control method was implemented with an FPGA control board, which includes a PWM interrupt service routine of 100 kHz frequency and a harmonic injection and position detection algorithm. The HILS experiments show rotor position detection errors according to the various frequency of the harmonic voltage injected for estimating the rotor position with a PWM frequency of 100 kHz cases. According to the experimental results, good position estimation was possible not only when the harmonic of 10 kHz corresponding to 1/10 of the PWM frequency was injected, but also when the harmonic of 1 kHz corresponding to 1/100 of the PWM frequency was injected. The experiments suggest that position estimation errors decrease as the frequency of the harmonic voltage increases, and, based on the foregoing, it is thought that the application of a GaN device capable of realizing a high switching frequency in a variable speed drive system can be another advantage.

스테레오 비전과 YOLOv3 를 이용한 드론의 3 차원 실내 위치 추정 알고리즘 개발

In this paper, we propose a three-dimensional indoor position estimation algorithm for drones using stereo vision and YOLOv3. First, we find the bounding box of a drone in the image using a deep-learning-based object detection algorithm called YOLOv3. To this end, we collect the training dataset consisting of drone images. In addition, the object detection performance of the YOLOv3 algorithm is improved by dividing object class labels of the same drone based on the angle of the drone seen from the camera. Then, the three-dimensional relative position of the drone is estimated based on the camera internal parameters, the bounding box information, and the depth map taken by the stereo vision. In addition, the Kalman filter is employed to estimate the position of the drone continuously. Finally, the position estimation performance of the proposed algorithm is evaluated through the experiments.

Improved adaptive parameters estimation considering transient‐state estimation error in sensorless control system of IPMSM

AbstractPosition sensorless vector control of interior permanent magnet synchronous motors (IPMSMs) has been used for downsizing, cost reduction, and high reliability. In the position sensorless vector control, the q‐axis inductance decreases as the q‐axis current increases due to the load variation, which results in deterioration of the position estimation performance without considering the q‐axis inductance. Here, the authors propose an innovative adaptive estimation based on the dynamic certainty equivalence principle for high‐performance position sensorless control for IPMSMs. The effectiveness of the proposed adaptive scheme is verified through experiments.