scholarly journals Position Estimator Design for a MEMS Top-Drive Electrostatic Rotary Actuator

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7081
Author(s):  
Jemin Woo ◽  
Bongsu Hahn ◽  
Changsun Ahn
Keyword(s):  
Unscented Kalman Filter ◽  
Estimation Error ◽  
Position Estimation ◽  
Capacitance Measurement ◽  
Accurate Estimation ◽  
Nonlinear Capacitance ◽  
Rotary Actuator ◽  
Plate Capacitor ◽  
Highly Nonlinear ◽  
Rotor Angle

The capacitance and rotor angle of a MEMS top-drive electrostatic rotary actuator do not have a linear relationship due to the non-ignorable fringe effect and low aspect ratio of the electrodes. Therefore, the position estimation is not as straightforward as that for a comb-drive linear actuator or a side-drive rotary actuator. The reason is that the capacitance is a nonlinear and periodic function of the rotor angle and is affected by the three-phase input voltages. Therefore, it cannot be approximated as a simple two-plate capacitor. Sensing the capacitance between a rotor and a stator is another challenge. The capacitance can be measured in the electrodes (stators), but the electrodes also have to perform actuation, so a method is needed to combine actuation and sensing. In this study, a nonlinear capacitance model was derived as a data-driven model that effectively represents the nonlinear capacitance with sufficient accuracy. To measure the capacitance accurately, the stator parts for actuation and those for sensing are separated. Using the nonlinear model and the capacitance measurement, an unscented Kalman filter was designed to mitigate the large estimation error due to the periodic nonlinearity. The proposed method shows stable and accurate estimation that cannot be achieved with a simple two-plate capacitor model. The proposed approach can be applied to a similar system with highly nonlinear capacitance.

scholarly journals GNSS Signal Processing with EKF and UKF for Stationary User Position Estimation

2021 ◽  
Vol 17 ◽  
pp. 75-80
Author(s):  
Mert Sever ◽  
Chingiz Hajiyev
Keyword(s):  
Signal Processing ◽  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Measurement Data ◽  
Position Estimation ◽  
Accurate Estimation ◽  
Important Process ◽  
Moving Platforms ◽  
Linearization Methods ◽  
Nonlinear Motion

Precise and accurate estimation of state vectors is an important process during position determination. In this study, Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) of stationary user, state vectors defined in Earth Centered Inertial (ECI) coordinate system, accompanied by GNSS measurement data. It is aimed to make estimations with methods. EKF and UKF methods were compared with each other. In this study, the effects of nonlinear motion analysis and linearization methods on state vector estimations were investigated. Thanks to this study, estimations of the positioning information required during the specific tasks of many moving platforms have been made.

scholarly journals Inter-Vehicle Position Estimation For NLOS Condition In The Persistence Of GPS Outages

2021 ◽  
Author(s):  
Meharoon Shaik
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Estimation Error ◽  
Position Estimation ◽  
Line Of Sight ◽  
Accurate Estimation ◽  
Range Data ◽  
Radio Communications ◽  
Collision Warning ◽  
Range Measurements

The main focus of thesis work addresses one of the functional key points of Cooperative Collision Warning application which is an accurate estimation of the range data of neighboring vehicles during persistent GPS outages under both line-of-sight (LOS) and non-line-of-sight (NLOS) situations. Cooperative Collision Warning, based on vehicle-to-vehicle radio communications and GPS systems, is one promising active safety application that has attracted considerable research interest. One of the severe estimation error is due to NLOS that can be mitigated by applying biased Kalman filter on range measurements. For our algorithm these inter-vehicle distances are measured from using one of the radio-based ranging techniques. Main objective is to establish an accurate map of positions for neighboring vehicles in the persistance of GPS outages. GPS outages can be possible in multipath environments where NLOS component is introduced to the true range measurements. These position estimates mainly depend on two factors: (i) Preprocessed inter-vehicle distances (range data is processed from biased Kalman filter); (ii) Road constraints (the vehicle uncertainty is more in the direction of road than the uncertainty in the direction opposite the road); This thesis suggests smoothing and mitigating the NLOS for radio-based ranging measurements under multipath conditions. In order to find accurate positions of neighboring vehicles an extended Kalman filter is implemented along with road constraints. Unbiased Kalman filter, biased Kalman filter and extended Kalman filter performances are experimentally verified using Matlab simulation tool with random number of vehicles at unknown random distinct positions in some physical region along a section of road for vehicular environment.

scholarly journals UKF-Based Vehicle Pose Estimation under Randomly Occurring Deception Attacks

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xinghua Liu ◽  
Dandan Bai ◽  
Yunling Lv ◽  
Rui Jiang ◽  
Shuzhi Sam Ge
Keyword(s):  
Kalman Filter ◽  
Pose Estimation ◽  
Upper Bound ◽  
Unscented Kalman Filter ◽  
Estimation Error ◽  
Accurate Estimation ◽  
Ground Vehicles ◽  
Ground Vehicle ◽  
Attack Model ◽  
Error Covariance

Considering various cyberattacks aiming at the Internet of Vehicles (IoV), secure pose estimation has become an essential problem for ground vehicles. This paper proposes a pose estimation approach for ground vehicles under randomly occurring deception attacks. By modeling attacks as signals added to measurements with a certain probability, the attack model has been presented and incorporated into the existing process and measurement equations of ground vehicle pose estimation based on multisensor fusion. An unscented Kalman filter-based secure pose estimator is then proposed to generate a stable estimate of the vehicle pose states; i.e., an upper bound for the estimation error covariance is guaranteed. Finally, the simulation and experiments are conducted on a simple but effective single-input-single-output dynamic system and the ground vehicle model to show the effectiveness of UKF-based secure pose estimation. Particularly, the proposed scheme outperforms the conventional Kalman filter, not only by resulting in more accurate estimation but also by providing a theoretically proved upper bound of error covariance matrices that could be used as an indication of the estimator’s status.

scholarly journals Inter-Vehicle Position Estimation For NLOS Condition In The Persistence Of GPS Outages

2021 ◽  
Author(s):  
Meharoon Shaik
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Estimation Error ◽  
Position Estimation ◽  
Line Of Sight ◽  
Accurate Estimation ◽  
Range Data ◽  
Radio Communications ◽  
Collision Warning ◽  
Range Measurements

The main focus of thesis work addresses one of the functional key points of Cooperative Collision Warning application which is an accurate estimation of the range data of neighboring vehicles during persistent GPS outages under both line-of-sight (LOS) and non-line-of-sight (NLOS) situations. Cooperative Collision Warning, based on vehicle-to-vehicle radio communications and GPS systems, is one promising active safety application that has attracted considerable research interest. One of the severe estimation error is due to NLOS that can be mitigated by applying biased Kalman filter on range measurements. For our algorithm these inter-vehicle distances are measured from using one of the radio-based ranging techniques. Main objective is to establish an accurate map of positions for neighboring vehicles in the persistance of GPS outages. GPS outages can be possible in multipath environments where NLOS component is introduced to the true range measurements. These position estimates mainly depend on two factors: (i) Preprocessed inter-vehicle distances (range data is processed from biased Kalman filter); (ii) Road constraints (the vehicle uncertainty is more in the direction of road than the uncertainty in the direction opposite the road); This thesis suggests smoothing and mitigating the NLOS for radio-based ranging measurements under multipath conditions. In order to find accurate positions of neighboring vehicles an extended Kalman filter is implemented along with road constraints. Unbiased Kalman filter, biased Kalman filter and extended Kalman filter performances are experimentally verified using Matlab simulation tool with random number of vehicles at unknown random distinct positions in some physical region along a section of road for vehicular environment.

scholarly journals Sensorless Control for IPMSM Based on Adaptive Super-Twisting Sliding-Mode Observer and Improved Phase-Locked Loop

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1225 ◽  
Author(s):  
Shuo Chen ◽  
Xiao Zhang ◽  
Xiang Wu ◽  
Guojun Tan ◽  
Xianchao Chen
Keyword(s):  
High Speed ◽  
Sliding Mode ◽  
Sensorless Control ◽  
Estimation Error ◽  
Signal Reconstruction ◽  
Phase Locked Loop ◽  
Position Estimation ◽  
Sliding Mode Observer ◽  
Accurate Estimation ◽  
Simple Strategy

In traditional sensorless control of the interior permanent magnet synchronous motors(IPMSMs) for medium and high speed domains, a control strategy based on a sliding-mode observer(SMO) and phase-locked loop (PLL) is widely applied. A new strategy for IPMSM sensorless controlbased on an adaptive super-twisting sliding-mode observer and improved phase-locked loop isproposed in this paper. A super-twisting sliding-mode observer (STO) can eliminate the chatteringproblem without low-pass filters (LPFs), which is an effective method to obtain the estimated backelectromotive forces (EMFs). However, the constant sliding-mode gains in STO may causeinstability in the high speed domain and chattering in the low speed domain. The speed-relatedadaptive gains are proposed to achieve the accurate estimation of the observer in wide speed rangeand the corresponding stability is proved. When the speed of IPMSM is reversed, the traditionalPLL will lose its accuracy, resulting in a position estimation error of 180°. The improved PLL basedon a simple strategy for signal reconstruction of back EMF is proposed to ensure that the motor canrealize the direction switching of speed stably. The proposed strategy is verified by experimentaltesting with a 60-kW IPMSM sensorless drive.

A New Initial Rotor Angle Position Estimation Method for High-Speed Brushless Direct Current Motor Using Current Injection and Mathematical Model

2018 ◽  
Vol 140 (7) ◽  
Author(s):  
Zhenyan Wang ◽  
Yanzhao He
Keyword(s):  
Mathematical Model ◽  
Direct Current ◽  
High Speed ◽  
Estimation Error ◽  
Mathematical Formulation ◽  
Estimation Method ◽  
Position Estimation ◽  
Average Value ◽  
Three Phase ◽  
Rotor Angle

In this paper, a new initial rotor angle position estimation method for the sensorless high-speed brushless direct current (DC) motor (HS-BLDCM) is proposed. Two groups of special three-phase conduction current pulse signals are injected into the three phases of the motor, and the mathematical formulation for the initial angle position estimation is illustrated. The initial rotor position is expressed as a function of the line voltage, the phase current derivative, and the average value of d–q frame stator inductance. Particularly, the independent parameters of the initial rotor angle position are eliminated in the mathematical model. The cooperative simulation results based on Maxwell and Simplorer and the experimental results demonstrate that the proposed method is effective with the estimation error less than 0.2 deg electrical in simulation and 5 deg electrical in experiment.

scholarly journals Sensorless SPMSM Position Estimation Using Position Estimation Error Suppression Control and EKF in Wide Speed Range

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Zhanshan Wang ◽  
Longhu Quan ◽  
Xiuchong Liu
Keyword(s):  
Permanent Magnet ◽  
High Performance ◽  
Sensorless Control ◽  
Estimation Error ◽  
Position Estimation ◽  
Combination Method ◽  
Accurate Estimation ◽  
Rotor Position ◽  
Estimation System ◽  
Error Suppression

The control of a high performance alternative current (AC) motor drive under sensorless operation needs the accurate estimation of rotor position. In this paper, one method of accurately estimating rotor position by using both motor complex number model based position estimation and position estimation error suppression proportion integral (PI) controller is proposed for the sensorless control of the surface permanent magnet synchronous motor (SPMSM). In order to guarantee the accuracy of rotor position estimation in the flux-weakening region, one scheme of identifying the permanent magnet flux of SPMSM by extended Kalman filter (EKF) is also proposed, which formed the effective combination method to realize the sensorless control of SPMSM with high accuracy. The simulation results demonstrated the validity and feasibility of the proposed position/speed estimation system.

scholarly journals Performance Analysis of a Full Order Sensorless Control Adaptive Observer for Doubly-Fed Induction Generator in Grid Connected Operation

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1254
Author(s):  
Gianluca Brando ◽  
Adolfo Dannier ◽  
Ivan Spina
Keyword(s):  
Sensorless Control ◽  
Estimation Error ◽  
Position Estimation ◽  
Adaptive Observer ◽  
Induction Generator ◽  
Doubly Fed Induction Generator ◽  
Rotor Position ◽  
Stator Flux ◽  
Doubly Fed ◽  
Rotor Position Estimation

This paper focuses on the performance analysis of a sensorless control for a Doubly Fed Induction Generator (DFIG) in grid-connected operation for turbine-based wind generation systems. With reference to a conventional stator flux based Field Oriented Control (FOC), a full-order adaptive observer is implemented and a criterion to calculate the observer gain matrix is provided. The observer provides the estimated stator flux and an estimation of the rotor position is also obtained through the measurements of stator and rotor phase currents. Due to parameter inaccuracy, the rotor position estimation is affected by an error. As a novelty of the discussed approach, the rotor position estimation error is considered as an additional machine parameter, and an error tracking procedure is envisioned in order to track the DFIG rotor position with better accuracy. In particular, an adaptive law based on the Lyapunov theory is implemented for the tracking of the rotor position estimation error, and a current injection strategy is developed in order to ensure the necessary tracking sensitivity around zero rotor voltages. The roughly evaluated rotor position can be corrected by means of the tracked rotor position estimation error, so that the corrected rotor position is sent to the FOC for the necessary rotating coordinate transformation. An extensive experimental analysis is carried out on an 11 kW, 4 poles, 400 V/50 Hz induction machine testifying the quality of the sensorless control.

Automatic weaving method for three-dimensional composite preforms using vision system

2021 ◽  
pp. 004051752098238
Author(s):  
Siyuan Li ◽  
Zhongde Shan ◽  
Dong Du ◽  
Li Zhan ◽  
Zhikun Li ◽  
...  
Keyword(s):  
Relative Position ◽  
Vision System ◽  
Estimation Error ◽  
Three Dimensional ◽  
Fiber Reinforced Composites ◽  
Accurate Estimation ◽  
Digital Manufacturing ◽  
Relative Rotation ◽  
Reinforced Composites ◽  
Main Structure

Three-dimensional composite preform is the main structure of fiber-reinforced composites. During the weaving process of large-sized three-dimensional composite preform, relative rotation or translation between the fiber feeder and guided array occurs before feeding. Besides, the weaving needles can be at different heights after moving out from the guided array. These problems are mostly detected and adjusted manually. To make the weaving process more precise and efficient, we propose machine vision-based methods which could realize accurate estimation and adjustment of the relative position-pose between the fiber feeder and guided array, and make the needles pressing process automatic by recognizing the position of the weaving needles. The results show that the estimation error of relative position-pose is within 5%, and the rate of unrecognized weaving needles is 2%. Our proposed methods improve the automation level of weaving, and are conducive to the development of preform forming toward digital manufacturing.

scholarly journals Nondestructive estimation of potato yield using relative variables derived from multi-period LAI and hyperspectral data based on weighted growth stage

Plant Methods ◽  
2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Shanjun Luo ◽  
Yingbin He ◽  
Qian Li ◽  
Weihua Jiao ◽  
Yaqiu Zhu ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Growth Stage ◽  
Estimation Error ◽  
Potato Yield ◽  
Hyperspectral Data ◽  
Single Stage ◽  
Accurate Estimation ◽  
Yield Estimation ◽  
Area Index ◽  
Red Edge

Abstract Background The accurate estimation of potato yield at regional scales is crucial for food security, precision agriculture, and agricultural sustainable development. Methods In this study, we developed a new method using multi-period relative vegetation indices (rVIs) and relative leaf area index (rLAI) data to improve the accuracy of potato yield estimation based on the weighted growth stage. Two experiments of field and greenhouse (water and nitrogen fertilizer experiments) in 2018 were performed to obtain the spectra and LAI data of the whole growth stage of potato. Then the weighted growth stage was determined by three weighting methods (improved analytic hierarchy process method, IAHP; entropy weight method, EW; and optimal combination weighting method, OCW) and the Slogistic model. A comparison of the estimation performance of rVI-based and rLAI-based models with a single and weighted stage was completed. Results The results showed that among the six test rVIs, the relative red edge chlorophyll index (rCIred edge) was the optimal index of the single-stage estimation models with the correlation with potato yield. The most suitable single stage for potato yield estimation was the tuber expansion stage. For weighted growth stage models, the OCW-LAI model was determined as the best one to accurately predict the potato yield with an adjusted R2 value of 0.8333, and the estimation error about 8%. Conclusion This study emphasizes the importance of inconsistent contributions of multi-period or different types of data to the results when they are used together, and the weights need to be considered.

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