Dynamic Compensation Model Research Articles

Sensor dynamic compensation method based on GAN and its application in shockwave measurement

Limited by the dynamic characteristics of the sensor, the high-frequency signal will be distorted by the dynamic error after passing through the sensor, which will affect the accuracy of the real value. To reduce the dynamic error, it is necessary to obtain a high-precision dynamic compensation model. This paper provides a solution of compensation model based on the deep learning method. First, the problem of limited sensor dynamic data is solved by data augmentation through Deep Convolutional Generative Adversarial Network. After that, the sensor compensation model is obtained by Speech Enhancement Generative Adversarial Network and applied to step signals and shockwave signals. This compensation method can compensate a variety of sensors used in the dynamic measurement. It is verified by the pressure sensor as an example in this paper, the results are better than that of traditional ones, the overshoot can be reduced from 119.2% to 2.5%, and the rising time is 5.5μs. The innovation of this paper is that we find a way to use deep learning methods to compensate sensor dynamic error based on a small dataset. At the same time, it is proved that this method has strong versatility, which is not available in traditional sensor compensation methods. It also provides a feasible scheme for the application of deep learning in dynamic compensation model calculation.

Precision Enhancement by Compensation of Hemispherical Resonator Gyroscope Dynamic Output Errors

In order to compensate for the hemispherical resonator gyroscope (HRG) dynamic output drift errors, a novel compensation method is presented. Through deriving the resonator motion equations and analyzing the mechanism of HRG dynamic output drift errors, it is found that the resonator defect nonuniformity is the main cause of the HRG dynamic output errors. By analyzing the mechanisms of these errors, the influence of these error sources is interacted and coupled, and it is difficult to analyze HRG output error mechanism and establish an error model comprehensively. We proposed a compensation prediction model for the HRG dynamic output to suppress the influence of these errors comprehensively. To establish the high precision HRG dynamic output compensation model, we proposed an improved variational mode decomposition (VMD) method to achieve an accurate HRG output. First, we used VMD to decompose the HRG output, and the optimal VMD decomposition parameters are achieved by genetic algorithm. In order to better achieve the effective information, the decomposed components of HRG drift are classified using the sample entropy. Then the mixed components are further processed using time frequency peak filtering. Finally, the accurate HRG output is achieved by reconstructing all the effective components. The accurate HRG output is used to construct the training set, and the high-precision HRG dynamic output prediction model is established by long short-term memory. The software compensation of the HRG dynamic output drift is realized by the prediction model. The experiment shows that the HRG dynamic output drift is mainly produced by the resonator nonuniformity, and it is strongly correlated with the standing wave azimuth, which verifies the correctness of the mechanism analysis. The HRG dynamic drift peak-peak value decreases to 5.715×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> °/s, which verifies the effectiveness of the compensation method.

Development of an Intuitive Interface with Haptic Enhancement for Robot-Assisted Endovascular Intervention.

Robot-assisted endovascular intervention has the potential to reduce radiation exposure to surgeons and enhance outcomes of interventions. However, the success and safety of endovascular interventions depend on surgeons' ability to accurately manipulate endovascular tools such as guidewire and catheter and perceive their safety when cannulating patient's vessels. Currently, the existing interventional robots lack a haptic system for accurate force feedback that surgeons can rely on. In this paper, a haptic-enabled endovascular interventional robot was developed. We proposed a dynamic hysteresis compensation model to address the challenges of hysteresis and nonlinearity in magnetic powder brake-based haptic interface, which were used for providing high-precision and higher dynamic range haptic perception. Also, for the first time, a human perceptual-based haptic enhancement model and safety strategy were integrated with the custom-built haptic interface for enhancing sensation discrimination ability during robot-assisted endovascular interventions. This can effectively amplify even subtle changes in low-intensity operational forces such that surgeons can better discern any vessel-tools interaction force. Several experimental studies were performed to show that the haptic interface and the kinesthetic perception enhancement model can enhance the transparency of robot-assisted endovascular interventions, as well as promote the safety awareness of surgeon.

Dynamic Frequency-Temperature Characteristic Modeling for Quartz Crystal Resonator Based on Improved Echo State Network.

Quartz crystal resonators are the key component of various kinds of electronic systems because they provide the reference frequency source of the system running clocks. However, the frequency stability is often affected by the temperature. Therefore, the frequency-temperature ( f-T ) characteristic modeling has been an important research topic in the frequency control field. The classic f-T modeling method omits the system dynamics and may lead to a large frequency compensation error in the case of rapid temperature changing. To deal with this issue, this article proposes a dynamic f-T modeling method based on improved echo state network (ESN), called residual scaled ESN (RSESN). In the proposed method, the residual modeling framework is designed for purposes of good physical understandability and high prediction precision. This framework uses the static polynomial f-T model to depict the approximated data relationship and applies the complicated network model to compensate the detailed dynamic error. To estimate the dynamic errors, one effective dynamic modeling tool, ESN, is introduced to build the dynamic compensation model for f-T characteristic of quartz crystal resonators. For a better fitting performance, the ESN activation limitations are analyzed and the scaled echo states are constructed in the improved ESN model. The modeling and testing results on the real experiment data show that the proposed method can capture the dynamic information effectively and provide better frequency deviation predictions.

Dynamic Compensation Model of BF Slag Homogenization Thermal Based on Advanced Deep Learning Algorithm

In recent years, deep learning has been widely used in the field of image visualization, and has attracted the attention of researchers. The main component of iron tailings is SiO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , In the process of quenching and tempering slag from iron tailings, melted SiO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> particles will randomly walk in the crucible, therefore, how to intelligently identify and track targets (SiO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> particles) in sequence image to promote the low consumption and efficient production of high value-added slag cotton has become an urgent problem. Aims at the temperature requirements of slag cotton preparation process, using intelligent algorithm with deep learning and hierarchical clustering, in-depth analysis of the visual information of the high temperature melting process of SiO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> particles. Through centroid tracking and positioning technology, intelligent edge feature extraction technology and fitting method of experimental data through high temperature melting process of SiO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> particles quantitative characterization functions of visual characteristics of SiO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> particles melting process under high temperature environment were obtained in this article. Approximation of the melting process of iron tailings in high temperature environments and embed this function into the static mathematical model of process thermal compensation an accurate thermal dynamic intelligent compensation model for slag cotton preparation process is realized.

Dynamic temperature compensation model based on nonuniform temperature field change

As the development of infrared temperature measurement technology, the research of the surface temperature monitoring system of rotary kiln based on infrared scanning has attracted the attention of experts in related fields. However, the non-contact characteristics of infrared bring many irresistible factors to the temperature measurement accuracy of rotary kiln, one of which cannot be ignored is the ambient temperature. In order to ensure the accuracy and stability of the system in temperature monitoring, the current research focuses on analyzing the influence of ambient temperature and establish a reasonable and effective temperature compensation model. In this paper, we analyze the influence of ambient temperature on temperature measurement in rotary kiln is analyzed based on the components of radiation received by infrared detector. The radiation received by infrared detector includes target radiation, reflection of ambient radiation and atmospheric radiation. It is found that the environmental temperature mainly affects the reflective radiation and atmospheric radiation of the environment, which cause the accuracy of temperature measurement drop. Therefore, this paper analysis the influence of ambient temperature on these two parts, and we put forward a dynamic temperature compensation model Based on nonuniform temperature field change, which improves the accuracy of temperature measurement by 5 percentage points.

Dynamic characterization method of accelerometers based on the Hopkinson bar calibration system

The dynamic characterization of accelerometers is the prerequisite and insurance for highly accurate measurement of dynamic acceleration signals. The main problem of dynamic characterization is the lack of effective methods to deal with model coefficients and the order coupling effect, which could severely influence the identification accuracy. This paper presents a novel approach to solve the identification coupling problem through synchronous iteration and compensation. In order to guarantee the precision of the dynamic model, the Hopkinson bar dynamic calibration system with a traceable excitation acceleration signal is proposed. The feasibility of dynamic modelling is proved by the data comparison of experiment result and model regression result in the time domain. Based on the dynamic model, a dynamic compensation model is established, which improves the dynamic performance by frequency coverage extension. Finally, the results of the dynamic modelling and dynamic compensation are demonstrated by means of the calibration experiment in the paper.

Research on the Robot Wrist Sensor Dynamic Characteristics Based on Improved Genetic Wavelet Neural Networks

The multi-dimensional wrist force sensor applied to Motomam V3X robot is introduced. The characteristics of genetic algorithm (GA) and artificial neural networks (ANN) are compared. A novel improved genetic algorithm (IGA) is proposed to train wavelet neural networks (WNN). A kind of new dynamic modeling and dynamic compensation methods are presented based on improved genetic algorithm and wavelet neural networks (IGAWNN) and the principle of algorithm is introduced for the multi-dimensional wrist force sensor. The dynamic model and dynamic compensation model of the robot wrist force sensor are set up according to data of the dynamic calibration, where the structure and parameters of wavelet neural networks of the dynamic model and dynamic compensation are optimized by the improved genetic algorithm. The results show that the proposed methods can overcome the shortcomings of easy convergence to the local minimum points of BP algorithm, and the network complexity, the convergence and the generalization ability are well compromised and the training speed and precision of the new dynamic modeling and dynamic compensation are increased.

Time-Varying Compensation for Peak Current-Controlled PFC Boost Converter

In this paper, an optimal time-varying compensation method with zero eigenvalue is first put forward for peak current-controlled power factor correction (PFC) boost converter, which can eliminate the fast-scale instability without zero current dead zone and achieve unity power factor. First, a time-varying mathematic model of a peak current-controlled PFC boost converter under continuous conduction mode is established. Then, based on the theoretical and experimental analyses of the traditional ramp compensation, a time-varying dynamic compensation model and method are presented to obtain zero eigenvalue during the whole line cycle. Therefore, the PFC boost converter occupies the strongest stability control during each switching cycle and can run into stable operation in one switching cycle under any external interference. Finally, the proposed compensation method is verified with experiments. Results show that a unity power factor and the stability in the whole line cycle can be obtained simultaneously.

Multi-variable Fuzzy Controller for Water Tank Temperature Control System

Due to the contradiction between high precision control with complexity of fuzzy controller design, an method for multi-variable fuzzy controller design is presented. A dynamic compensation model for the fuzzy controller is introduced, which simplify the analysis and design of multi-variable fuzzy controller, and improve control performance. The method is applied to high precision water tank temperature control system, and experience show that the multi-variable fuzzy controller with dynamic compensation model has excellent control effects.

Dynamic Design of Compensation System Based on Diversified Project Features–Taking the Project Manager as an Example

With the development and extensive application of project team work under the matrix organizational structure, the traditional compensation system based on the job-evaluation method has been greatly challenged. The job-evaluation method is designed to compliment functional or divisional structure in a static or at least stationary context, which is often ineffective and even counterproductive in the quick changing environments. This paper is an attempt to design a dynamic compensation model by setting a “project adjusting index” based on the key diversified project features. The objective is to primarily rationalize the firm’s pay structure, and to make project employee could be paid according to their contributions to the organization on the norm of equity.

動的摩擦補償モデルを用いたマニピュレータの制御(ロボティクス・メカトロニクスI)

動的摩擦補償モデルを用いたマニピュレータの制御(ロボティクス・メカトロニクスI)