Encoder Resolution Research Articles

Improved implementation of serial pseudorandom/natural code converters used in absolute position encoders

Abstract Pseudorandom encoders represent a significant type of absolute position encoders that can be implemented using only a single code track on a code disk and a single reading head, making them especially suitable for high-resolution position measurements. Since pseudorandom code words used for encoding positions are not compatible with other electronics, pseudorandom encoders include a converter that translates the pseudorandom code into the natural binary code. This paper proposes an improved implementation of the serial Fibonacci pseudorandom/natural code converter used in pseudorandom position encoders. The improvement is based on modifying the way bits are written into the converter’s shift register, which reduces the propagation delay inside the code converter, thus enabling an increase in the clock frequency. Simulations of the proposed solution, performed in Multisim software, provide a realistic picture of its behaviour and performance. Based on the simulation results, the proposed solution enables a significant increase in the clock frequency of the code converter by 180.54% or 57.56%, depending on the encoder resolution. This significantly boosts the speed of code conversion and enhances the overall performance of the position encoder. The proposed solution is highly valuable for numerous position measurement applications, especially in the increasingly relevant high-resolution applications.

Design and implementation of an encoder calibration system for improved resolution and accuracy in IPMSM drive with embedded software

ABSTRACT This paper presents an encoder calibration system to enhance resolution and accuracy in an interior permanent magnet synchronous motor (IPMSM) drive, along with custom embedded software. The system implements closed-loop control with the IPMSM drive, adjusting the encoder resolution through gear ratio adjustments. Communication between the human-machine interface (HMI) and the DSP’s internal 28× CPU is achieved via digital communication RS-422 interface. The embedded software on the 28× CPU processes real-time data and displays calibration information on the HMI. Firmware integration includes external analog/digital converters, comparators, digital communication interfaces, high-accuracy encoders, original encoders, and additional gear ratio adjustments, with computed compensation values stored in EEPROM. The TMS-320F-28335 digital signal processor serves as the control core, enabling encoder signal processing, embedded software execution, HMI operation, and IPMSM drive control. The experimental results validate the practicality of the proposed digitization method, demonstrating its effective applicability in industrial control systems.

Data Acquisition and Performance Analysis of Image-Based Photonic Encoder Using Field-Programmable Gate Array (FPGA)

With the continuous advancement of numerical control technology, the requirements for the position detection resolution, precision, and size of photoelectric encoders in computer numerical control machine tools are increasingly stringent. In the pursuit of high resolution and precision, this work investigates the principles of electronic subdivision and embedded hardware. It designs a high-precision image-based photonic encoder using a Field-Programmable Gate Array (FPGA). This photonic encoder captures the pattern of a rotating code disk using a complementary metal-oxide-semiconductor (CMOS) image sensor. The encoder’s core is the XC6SLX25T chip from the Spartan-6 series, with peripheral circuits including only A/D sampling and low-pass signal processing circuits. The FPGA module handles the digital signal reception, waveform conversion, quadrature frequency coarse count calculation, fine count subdivision calculation, and final position calculation of the encoder. In experiments, the output signal of the photonic encoder contains many impurities. After processing by the signal processing module, the A and B phase signals are not affected by previous interference, with a phase difference of 90°, meeting the requirements for subsequent signal processing modules. After fine count subdivision processing, the waveform graph significantly increases within one cycle, and after quadrupling the frequency, 30 subdivisions are performed within each cycle. Noise is introduced into graphic positioning or graphics are positioned under different noise conditions. Experimental results show that utilizing an improved centroid algorithm helps further suppress noise and enhance measurement accuracy in the design of image-based photonic encoders.

A Novel Angle Segmentation Method for Magnetic Encoders Based on Filtering Window Adaptive Adjustment Using Improved Particle Swarm Optimization.

In this paper we outline newly-developed control algorithms, designed to achieve high-precision feedback for a motor control system using a magnetic encoder. The magnetic encoder, combing single-pole and multi-pole magnetic steels, was adopted to extend the resolution of the magnetic encoder. First, with a view to settling the issue of the jump points of the multi-pole angle value at the convergence of two adjacent magnetic poles, the angle segmentation method, which uses the window filter discrimination method, is employed to determine the actual angle value. The appropriate filter window width is selected via the improved particle swarm optimization (IPSO) algorithm, and an expanded resolution is achieved. Second, a compensation table is completed via a linear compensation algorithm based on virtual cutting to enhance the accuracy of the combined magnetic encoder. On this basis, a linear difference algorithm is used to achieve deviation correction of the angle. Finally, the jump points can be restrained effectively via the angle segmentation method. The resolution reaches 0.05°, and the accuracy is 0.045°.

Low-Speed Model Predictive Control Based on Modified Extended State Observer of Arc Motor

The article proposes a low speed model predictive control (LSMPC) based on modified extended state observer with low resolution encoder for the field modulated stator permanent magnet arc motor (FMSPMAM). The resolution of the encoder and the large torque ripples caused by the motor structure limit the expansion of the drive system to lower speeds. In order to reduce the contradiction between high bandwidth and high noise, and improve the performance of the drive system in low speed, a modified linear extended state observer (LESO) with a feedforward compensation is designed to estimate the speed and disturbance. Moreover, a dead time compensation is equipped to reduce the voltage distortion in light load and low speed. The experimental results verify the effectiveness of the proposed method in low speed with low resolution encoder.

Instantaneous-Angular-Speed-Based Synchronous Averaging Tool for Bearing Outer Race Fault Diagnosis

Synchronous averaging (SA) is a powerful signal processing tool to enhance the feature of interesting periodic events by suppressing non-synchronous components. However, the features related to the rolling element bearing (REB) fault may not be effectively enhanced by SA under random slip conditions. To address this issue, two instantaneous angular speed (IAS)-based synchronous averaging frameworks are proposed in this study. Then, an improved negentropy indicator is proposed to characterize the richness of the REB fault information. Additionally, based on the estimation feature of the IAS signal, the effects of the encoder resolution and structure damping factor on the waveform related to the faulty REB are studied, respectively. Simulation and experiment results show that the proposed schemes can be used to enhance the feature of the REB fault under random slip conditions.

A Novel Analog Interpolation Method for Heterodyne Laser Interferometer

Laser interferometer technology is used in the precision positioning stage as an encoder. For better resolution, laser interferometers usually work with interpolation devices. According to the interpolation factor, these devices can convert an orthogonal sinusoidal signal into several square-wave signals via digital processing. The bandwidth of the processing will be the limitation of the moving speed of the positioning stage. Therefore, the user needs to make a trade-off between the interpolation factor and the moving speed. In this investigation, a novel analog interpolation method for a heterodyne laser interferometer has been proposed. This method is based on the principle of the lock-in amplifier (LIA). By using the proposed interpolation method, the bandwidth of the laser encoder system can be independent of the interpolation factor. This will be a significant benefit for the ultra-high resolution encoder system and the laser interferometers. The concept, design, and experiment are revealed in this manuscript. The experimental results show that the proposed interpolation method can reach nanometer resolution with a heterodyne laser interferometer, and the bandwidth of the signal is independent of the resolution.

Design of intelligent robots for tourism management service based on green computing.

The modular intelligent robot platform has important application prospects in the field of tourism management services. Based on the intelligent robot in the scenic area, this paper constructs a partial differential analysis system for tourism management services, and adopts the modular design method to complete the hardware design of the intelligent robot system. Through system analysis, the whole system is divided into 5 major modules, including core control module, power supply module, motor control module, sensor measurement module, wireless sensor network module, to solve the problem of quantification of tourism management services. In the simulation process, the hardware development of wireless sensor network node is carried out based on MSP430F169 microcontroller and CC2420 radio frequency wireless communication chip, and the corresponding physical layer and MAC (Media Access Control) layer data definition and data definition of IEEE802.15.4 protocol are completed for software implementation, and data transmission and networking verification. The experimental results show that the encoder resolution is 1024P/R, the power supply voltage is DC5V5%, and the maximum response frequency is 100 kHz. The algorithm designed by MATLAB software can avoid the existing shortcomings and meet the real-time requirements of the system, which significantly improves the sensitivity and robustness of the intelligent robot.

Hybrid Frequency and Period Based for Angular Speed Measurement of DC Motor Using Kalman Filter


 The Incremental Rotary Encoder have been widely used to measure the angular speed of electrical drive such as Permanent Magnet Direct Current Motor (PMDCM). Nevertheless, speed measurement of PMDCM from the encoder signals can be subject to errors in some special condition such as in low resolution encoder. There are two main methods to measure the angular speed of PMDCM through encoder signal such as frequency-based and period-based wich has its own properties. Hence in this reseach aimed to improve the angular speed measurement with hybridization of frequency and period-based measurement. The Hybrid method is defined as paralleling the period and frequency then estimated the angular speed using sensor fusion with Kalman Filter. The experiment is doing by comparing of all method to get the best way in measuring. From the experimental showed that the Kalman filter parameter was fine tuned that resulting the sensor fusion or the mixed measurement between the frequency-based and the period based measure the angular speed accurately.

Nonuniform Dual-Rate Extended Kalman-Filter-Based Sensor Fusion for Path-Following Control of a Holonomic Mobile Robot with Four Mecanum Wheels

This paper presents an extended Kalman-filter-based sensor fusion approach, which enables path-following control of a holonomic mobile robot with four mecanum wheels. Output measurements of the mobile platform may be sensed at different rates: odometry and orientation data can be obtained at a fast rate, whereas position information may be generated at a slower rate. In addition, as a consequence of possible sensor failures or the use of lossy wireless sensor networks, the presence of the measurements may be nonuniform. These issues may degrade the path-following control performance. The consideration of a nonuniform dual-rate extended Kalman filter (NUDREKF) enables us to estimate fast-rate robot states from nonuniform, slow-rate measurements. Providing these estimations to the motion controller, a fast-rate control signal can be generated, reaching a satisfactory path-following behavior. The proposed NUDREKF is stated to represent any possible sampling pattern by means of a diagonal matrix, which is updated at a fast rate from the current, existing measurements. This fact results in a flexible formulation and a straightforward algorithmic implementation. A modified Pure Pursuit path-tracking algorithm is used, where the reference linear velocity is decomposed into Cartesian components, which are parameterized by a variable gain that depends on the distance to the target point. The proposed solution was evaluated using a realistic simulation model, developed with Simscape Multibody (Matlab/Simulink), of the four-mecanum-wheeled mobile platform. This model includes some of the nonlinearities present in a real vehicle, such as dead-zone, saturation, encoder resolution, and wheel sliding, and was validated by comparing real and simulated behavior. Comparison results reveal the superiority of the sensor fusion proposal under the presence of nonuniform, slow-rate measurements.

매니퓰레이터 로봇의 기구학 파라미터 보정에 사용되는 구조광의 정밀성 향상을 위한 영상처리 알고리즘

The machining tolerance and assembly error in the robot mechanism cause deterioration of the control precision of the articulated manipulator robot. To handle this problem, a correction algorithm should be used. The algorithm can be classified into a contact-based method and a non-contact-based method. This paper deals with a non-contact correction method using a structured light and subsequent image processing algorithm to estimate a laser point projected onto a screen to improve the measurement accuracy of non-contact method. In the conventional method, the laser image on the screen observed by the camera was simply assumed to be a single point. However, the actual observed image has an area of a size that is difficult to ignore. In addition, there is a significant time delay between the actual point and the captured point from the frame grabber of the computer vision. Consequently, these reduce the estimation accuracy. To improve the estimation performance, mathematical model-based image processing algorithm is proposed to handle these two major prerequisites. Testbed system was developed with an embedded computer vision system to implement the algorithm. The performance was verified by comparing the actual point from the high resolution encoder signal and the estimated point from the proposed algorithm.

Deep embedded hybrid CNN–LSTM network for lane detection on NVIDIA Jetson Xavier NX

In recent years, lane detection has become one of the most important factors in the progress of intelligent vehicles. To deal with the challenging problem of low detection precision and real-time performance of most traditional systems, we proposed a real-time deep lane detection system based on CNN Encoder–Decoder and Long Short-Term Memory (LSTM) networks for dynamic environments and complex road conditions. The CNN Encoder network is used to extract deep features from a dataset and to reduce their dimensionality. A corresponding decoder network is used to map the low resolution encoder feature maps to dense feature maps that correspond to road lane. The LSTM network processes historical data to improve the detection rate through the removal of the influence of false alarm patches on detection results. We propose three network architectures to predict the road lane: CNN Encoder–Decoder network, CNN Encoder–Decoder network with the application of Dropout layers and CNN Encoder–LSTM-Decoder network that are trained and tested on a public dataset comprising 12764 road images under different conditions. Experimental results show that the proposed hybrid CNN Encoder–LSTM-Decoder network that we have integrated into a Lane-Departure-Warning-System (LDWS) achieves high prediction performance namely an average accuracy of 96.36%, a Recall of 97.54%, and a F1-score of 97.42%. A NVIDIA Jetson Xavier NX supercomputer has been used, for its performance and efficiency, to realize an Embedded Deep LDWS.

Super-Resolution Velocity Reconstruction and Control of Ultrasonic Motors.

Accurate and fast velocity feedback signal is essential for the velocity control of ultrasonic motors (USMs). However, the low operating velocity of USMs results in a long velocity detection dead time (VDDT) of incremental encoders, which seriously restricts the dynamic control performance of USMs. Therefore, this article presents a super-resolution velocity control (SRVC) scheme based on the velocity reconstruction for the USM. First, the mathematical model of the USM is derived from the mechanical characteristics and the electromechanical coupling characteristics. Then, the velocity reconstruction method is proposed by combining the model estimated velocity and the encoder measured velocity. The closed-loop control scheme using the reconstructed velocity is implemented by a self-designed driving circuit. Experimental results show the velocity reconstruction method not only can break through the limitation of the encoder resolution to reduce the VDDT but also has a high-velocity accuracy. Furthermore, compared with the existing encoder-based control scheme, the proposed SRVC scheme has a faster velocity response under different loads.

Research on Angle Subdivision Method of Multi-Pole Magnetic Encoder Based on Pole Number Quotient Remainder

To solve the problem of difficulty in the accurate judgment of the zero-crossing position of multi-pole angle value in multi-pole magnetic encoder and then the angle value jumps, this article proposes an angle subdivision method for multi-pole magnetic encoder based on pole number quotient remainder. Firstly, the multi-loop angle data of the magnetic encoder are collected and sorted, and 16 fuzzy multi-pole angle zero-crossing boundaries are obtained. According to the boundary, 32 boundary range values are obtained. The remainder and quotient are calculated according to the boundary range value and are used as the judgment basis of pole pair of the current multi-pole angle value. When the remainder number is 0, the current quotient value can be directly used as the pole number. If the remainder number is not 0, it is necessary to judge the pole number according to the specific range of multi-pole angle values. This method effectively solves the problem of multi-pole number judgment error caused by angle noise of magnetic encoder, and effectively improves the resolution of the magnetic encoder. Experiments show that the multi-pole magnetic encoder with the proposed method can effectively suppress the angle jump caused by the calculation error. Finally, the accuracy of the multi-pole magnetic encoder proposed in this article can reach 11 bits (±0.165°).

Deep Learning With Anaphora Resolution for the Detection of Tweeters With Depression: Algorithm Development and Validation Study.

BackgroundMental health problems are widely recognized as a major public health challenge worldwide. This concern highlights the need to develop effective tools for detecting mental health disorders in the population. Social networks are a promising source of data wherein patients publish rich personal information that can be mined to extract valuable psychological cues; however, these data come with their own set of challenges, such as the need to disambiguate between statements about oneself and third parties. Traditionally, natural language processing techniques for social media have looked at text classifiers and user classification models separately, hence presenting a challenge for researchers who want to combine text sentiment and user sentiment analysis.ObjectiveThe objective of this study is to develop a predictive model that can detect users with depression from Twitter posts and instantly identify textual content associated with mental health topics. The model can also address the problem of anaphoric resolution and highlight anaphoric interpretations.MethodsWe retrieved the data set from Twitter by using a regular expression or stream of real-time tweets comprising 3682 users, of which 1983 self-declared their depression and 1699 declared no depression. Two multiple instance learning models were developed—one with and one without an anaphoric resolution encoder—to identify users with depression and highlight posts related to the mental health of the author. Several previously published models were applied to our data set, and their performance was compared with that of our models.ResultsThe maximum accuracy, F1 score, and area under the curve of our anaphoric resolution model were 92%, 92%, and 90%, respectively. The model outperformed alternative predictive models, which ranged from classical machine learning models to deep learning models.ConclusionsOur model with anaphoric resolution shows promising results when compared with other predictive models and provides valuable insights into textual content that is relevant to the mental health of the tweeter.

Spectral precision of frequency demodulation method: Influence of additive noise on instantaneous angular speed spectral estimation

This paper treats the spectral precision of frequency demodulation method based on Gabor’s analytic signal applied to Instantaneous Angular Speed (IAS) estimation from speed sensor under stationary speed conditions. The quadrature decomposition combined with geometric complex representation are used to approach the spectral modeling of the influence of wide-sense stationary additive noise on IAS spectral estimate using the periodogram. A method is proposed for IAS noise spectral estimation from signal’s envelope. Moreover, the effect of the carrier power and encoder resolution is demonstrated through real life signals. Finally, a statistical approach is proposed in order to set thresholds on IAS spectral observation allowing a good separation of additive random perturbations from real information under a certain probability. The proposed tools are applied on both synthetic and experimental signals and are shown to be effective in estimating IAS noise level from the input signal properties.

A Novel Network With Parallel Resolution Encoders for the Diagnosis of Corneal Diseases.

To propose a deep-learning network for the diagnosis of two corneal diseases: Fuchs' endothlelial dystrophy and keratoconus, based on optical coherence tomography (OCT) images of the cornea. In this paper, we propose a novel network with parallel resolution-specific encoders and composite classification features to directly diagnose Fuchs' endothelial dystrophy and keratoconus using OCT images. Our proposed network consists of a multi-resolution input, multiple parallel encoders, and a composite of convolutional and dense features for classification. The purpose of using parallel resolution-specific encoders is to perform multi-resolution feature fusion. Also, using composite classification features enhances the dense feature learning. We implemented other related networks for comparison with our network and performed k-fold cross-validation on a dataset of 16,721 OCT images. We used saliency maps and sensitivity analysis to visualize our proposed network. The proposed network outperformed other networks with an image classification accuracy of 0.91 and a scan classification accuracy of 0.94. The visualizations show that our network learned better features than other networks. The proposed methods can potentially be a step towards the early diagnosis of corneal diseases, which is necessary to prevent their progression, hence, prevent loss of vision.

Double Dynamic Multi-Window Interval Prediction Angle Segmentation Method for Multi-Pole Magnetic Encoder

In this article, a magnetic encoder consisting of single-pole magnetic steel and multi-pole magnetic steel has been proposed. In order to solve the problem of multi-pole angle jump point caused by signal noise, a dynamic multi-window angle pole number prediction algorithms has been put forward, which eliminates the error of angle pole number judgment and improves the resolution effectively. In order to improve the precision of angle value after subdivision, over-sampling linear interpolation tabulation method has been proposed. The method which is put forward in this article is able to enhance the precision of multi-pole angle value and eliminate the jump points in subdivision process. The resolution of the magnetic encoder obtained by this method could be improved, and it is well applied in motor control. At last, the accuracy of the magnetic encoder is ±40 LSB (0.014°) and the resolution increased from 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">16</sup> to 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">20</sup> .

Development of a Data Clustering System for 2DOF Robotic Ball Balancer Using Laser Scanning RangeFinder

In this study, a new perspective for developing laser scanner rangefinder based data clustering system for a 2DOF robotic ball balancer was proposed. The study focused on detecting an object (i.e., ball) on the tilt-table robotic platform using the sensor fusion and data clustering systems proposed. Clustering system was modeled by following the principles of hierarchical clustering method. The developed system involving the clustering and sensor fusion algorithms was embedded in Matlab-Simulink environment to be able to run in real-time applications. The system was tested using an experimental platform including a 2DOF robotic ball balancer equipped with high resolution encoders and a laser scanner rangefinder. In the experiments, the goal was to detect the ball and its position not only on the flat but also on the tilted platform. A camera was also attached to the top of the experimental setup and used to monitor the location of the ball on the platform. By this way the results obtained using the proposed system could be verified for accuracy, performance and repeatability issues.

Electronic Interpolation Interface Based on Linear Subdivision Method for Sinusoidal Optical Encoders

Sinusoidal optical encoders are widely utilized in precision positioning systems. With the development of precision positioning systems, higher resolution is required. Electronic interpolation is a promising technique to further improve the resolution of sinusoidal optical encoders. In this paper, we propose an electronic interpolation interface based on linear subdivision method with better practical accuracy. Firstly, a pseudo-linearized signal based on the difference between the absolute values of sine and cosine signals is generated. Then, a compensation signal with a ratiometric form is constructed, which has a better robustness to the non-ideal input signals. Finally, a nearly perfect linear output signal is obtained by combining these two signals. Thus, the displacement can be linearly determined without LUTs. It is shown that the theoretical nonlinear error of the proposed method is below 0.08° over a signal period of 360°, which corresponds to a theoretical interpolation error of $0.018~\mu \text{m}$ for sinusoidal optical encoders with a pitch of $80~\mu \text{m}$ . Moreover, theoretical analysis and simulation results indicate that the proposed method offers a better practical accuracy. In the experiment, the proposed electronic interpolation interface is developed in a field-programmable gate array (FPGA), and experiments are carried out to evaluate its performance. Both the theoretical and experiment results verify its effectiveness.