An adaptive stable observer for on board auxiliary inverters with online current identification strategy

An adaptive stable observer with output current online identification strategy for the auxiliary inverters applied in advanced electric trains, such as high speed railway, urban rail, subway and maglev trains, is proposed. The designed observer is used to estimate the state variables, i.e. controllable duty ratio and current components in d-q-o rotary reference frame. The convergence of the observer estimation error is analyzed with consideration of uncertain level variation of input voltage at direct current(DC) side and sufficient conditions are given to prove its practical stability. Experimental results are shown to confirm the effectiveness of the proposed observer.

A Technology for Online Parameter Identification of Permanent Magnet Synchronous Motor

Accuracy of the motor parameters is important in realizing high performance control of permanent magnet synchronous motor(PMSM).However,the inductance and resistance of motor winding vary with the change of temperature,rotor position and current frequency.In this paper,a technology based on circuit model is introduced for realizing online identification of the parameter of PMSM.In the proposed method,a set of nonlinear equations containing the parameters to be identified is established.Considering that it is very difficult to obtain the analytical solution of a nonlinear system of equations,Newton iterative method is used for solving the equations.Both the simulation and testing results confirm the effectiveness of the method presented.

A METHOD OF ONLINE DAMAGE IDENTIFICATION FOR STRUCTURES BASED ON AMBIENT VIBRATION

A method of damage identification for engineering structures based on ambient vibration is put forward, in which output data are used only. Firstly, it was identification of the statistic parameters to associate with the exterior excitation for undamaged structures. Then it was detection and location of the structural damages for damaged structures. The ambient identification method includes a theoretical model and numerical method. The numerical experiment results show the method is precise and effective. This method may be used in health monitoring for bridges and architectures.

Identification of time-varying system and energy-based optimization of adaptive control in seismically excited structure

The combination of structural health monitoring and vibration control is of great importance to provide components of smart structures.While synthetic algorithms have been proposed,adaptive control that is compatible with changing conditions still needs to be used,and time-varying systems are required to be simultaneously estimated with the application of adaptive control.In this research,the identification of structural time-varying dynamic characteristics and optimized simple adaptive control are integrated.First,reduced variations of physical parameters are estimated online using the multiple forgetting factor recursive least squares(MFRLS)method.Then,the energy from the structural vibration is simultaneously specified to optimize the control force with the identified parameters to be operational.Optimization is also performed based on the probability density function of the energy under the seismic excitation at any time.Finally,the optimal control force is obtained by the simple adaptive control(SAC)algorithm and energy coefficient.A numerical example and benchmark structure are employed to investigate the efficiency of the proposed approach.The simulation results revealed the effectiveness of the integrated online identification and optimal adaptive control in systems.

Online asymmetry estimation for whole angle mode coriolis vibratory gyroscopes by high frequency injection

The whole angle mode gyroscope(WAMG)is considered to be the next generation architecture,but it is suffered from the asymmetry errors to conduct real products.This paper proposes a novel high frequency injection based approach for the error parameters online identification for the WAMG.The significance is that it can separate physical and error fingerprints to enable online calibration.The nonlinear WAMG dynamics are discretized to meet the requirement of numerical precision and computation efficiency.The optimized estimation methods are then constructed and compared to track asymmetry error parameters continuously.In the validation part,its results firstly prove that the proposed scheme can accurately identify constant asymmetry parameters with an overall tracking error of less than 1 ppm and the extreme numerical convergence can reach 10^(-12)ppm.Under the dynamic asymmetry variation condition,the root mean square errors(RMSE)indicate that the tracking accuracy can reach the level of10^(-3),which shows the robustness of the proposed scheme.In summary,the proposed method can effectively estimate the WAMG asymmetry errors online with satisfied performance and practical values.

A Non-Contact Original-State Online Real-Time Monitoring Method for Complex Liquids in Industrial Processes

Failures are very common during the online real-time monitoring of large quantities of complex liquids in industrial processes, and can result in excessive resource consumption and pollution. In this study, we introduce a monitoring method capable of non-contact original-state online real-time monitoring for strongly coated, high-salinity, and multi-component liquids. The principle of the method is to establish the relationship among the concentration of the target substance in the liquid （C）, the color space coor- dinates of the target substance at different concentrations （L＊, a＊, b＊）, and the maximum absorption wave- length （λmax）; subsequently, the optimum wavelength λT of the liquid is determined by a high-precision scanning-type monitoring system that is used to detect the instantaneous concentration of the target substance in the flowing liquid. Unlike traditional monitoring methods and existing online monitoring methods, the proposed method does not require any pretreatment of the samples （i.e., filtration, dilution, oxidation/reduction, addition of chromogenic agent, constant volume, etc.）, and it is capable of original- state online real-time monitoring. This method is employed at a large electrolytic manganese plant to monitor the Fe3. concentration in the colloidal process of the plant＇s aging liquid （where the concentra- tions of Fe3＋, Mn2＋, and （NH4）2SO4 are 0.5-18 mg.L 1, 35-39 g.L 1, and 90-110 g.L 1, respectively）. The relative error of this monitoring method compared with an off-line laboratory monitoring is less than 2%.

An adaptive control strategy for microgrid secondary frequency based on parameter identification

Complex microgrid structures and time-varying conditions, among other factors, cause problems in the mechanical modeling of microgrids, making model-based controller optimization difficult. Therefore, this study proposed a secondary frequency adaptive control strategy based on parameter identification, which uses an online parameter identification method to identify the parameters in the microgrid in real-time. The identified parameters are then used in the secondary frequency adaptive controller to optimize the real-time controller performance. The proposed method realizes adaptive optimization of the controller in the microgrid operation state and is applied to a microgrid with unknown parameters to adjust the controller parameters. Finally, a simulation experiment was conducted to verify the model accuracy and the frequency regulation effect of the proposed adaptive control strategy.

Modeling and Adaptive Self-Tuning MVC Control of PAM Manipulator Using Online Observer Optimized with Modified Genetic Algorithm

In this paper, the application of modified genetic algorithms (MGA) in the optimization of the ARX Model-based observer of the Pneumatic Artificial Muscle (PAM) manipulator is investigated. The new MGA algorithm is proposed from the genetic algorithm with important additional strategies, and consequently yields a faster convergence and a more accurate search. Firstly, MGA-based identification method is used to identify the parameters of the nonlinear PAM manipulator described by an ARX model in the presence of white noise and this result will be validated by MGA and compared with the simple genetic algorithm (GA) and LMS (Least mean-squares) method. Secondly, the intrinsic features of the hysteresis as well as other nonlinear disturbances existing intuitively in the PAM system are estimated online by a Modified Recursive Least Square (MRLS) method in identification experiment. Finally, a highly efficient self-tuning control algorithm Minimum Variance Control (MVC) is taken for tracking the joint angle position trajectory of this PAM manipulator. Experiment results are included to demonstrate the excellent performance of the MGA algorithm in the NARX model-based MVC control system of the PAM system. These results can be applied to model, identify and control other highly nonlinear systems as well.

Identification of aircraft longitudinal aerodynamic parameters using an online corrective test for wind tunnel virtual flight

The identification of aerodynamic parameters is accomplished through the test data of the dynamic movement of scaled aircraft models flying dynamically in wind tunnel,which can real-ize the accurate acquisition of the aerodynamic model of the aircraft in the preliminary stage for aircraft design,and it is of great significance for improving the efficiency of aircraft design.How-ever,the translational motion of the test model in the wind tunnel virtual flight is subject to con-straints that result in distinct flight dynamics compared to free flight.These constraints have implications for the accuracy of aerodynamic derivatives obtained through the identification of wind tunnel test data.With this issue in mind,the research studies the differences in longitudinal dynamic characteristics between unconstrained free flight and wind tunnel virtual flight,and inno-vatively proposes an online correction test based wind tunnel virtual flight test technique.The lon-gitudinal trajectory and velocity changes of the model are solved online by the aerodynamic forces measured during the test,and then the coupled relationship between aircraft translation and rota-tion is used to correct the model's pitch attitude motion online.For the first time,the problem of solving the data approximation for free flight has been solved,eliminating the difference between the dynamics of wind tunnel virtual flight and free flight,and improving the accuracy of the aero-dynamic derivative identification results.The experiment's findings show that accurate aerodynamic derivatives can be identified based on the online correction test data,and the observed behaviour of the identified motion model has similarities to that of the free flight motion model.

A comparative study of different online model parameters identification methods for lithium-ion battery

Precise states estimation for the lithium-ion battery is one of the fundamental tasks in the battery management system(BMS),where building an accurate battery model is the first step in model-based estimation algorithms.To date,although the comparative studies on different battery models have been performed intensively,little attention is paid to the comparison among different online parameters identification methods regarding model accuracy,robustness ability,adaptability to the different battery operating conditions and computation cost.In this paper,based on the Thevenin model,the three most widely used online parameters identification methods,including extended Kalman filter(EKF),particle swarm optimization(PSO),and recursive least square(RLS),are evaluated comprehensively under static and dynamic tests.It is worth noting that,although the built model’s terminal voltage may well follow a measured curve,these identified model parameters may significantly out of reasonable range,which means that the error between measured and predicted terminal voltage cannot be seen as a gist to determine which model is the most accurate.To evaluate model accuracy more rigorously,battery state-of-charge(SOC)is further estimated based on identified model parameters under static and dynamic tests.The SOC prediction results show that EKF and RLS algorithms are more suitable to be used for online model parameters identification under static and dynamic tests,respectively.Moreover,the random offset is added into originally measured data to verify the robustness ability of different methods,whose results indicate EKF and RLS have more satisfactory ability against imprecisely sampled data under static and dynamic tests,respectively.Considering model accuracy,robustness ability,adaptability to the different battery operating conditions and computation cost simultaneously,EKF is recommended to be adopted to establish battery model in real application among these three most widely used methods.

Online identification of time-varying dynamical systems for industrial robots based on sparse Bayesian learning

Nowadays, industrial robots have been widely used in manufacturing, healthcare, packaging, and more. Choosing robots in these applications mainly attributes to their repeatability and precision. However, prolonged and loaded operations can deteriorate the accuracy and efficiency of industrial robots due to the unavoidable accumulated kinematical and dynamical errors. This paper resolves these aforementioned issues by proposing an online time-varying sparse Bayesian learning(SBL) method to identify dynamical systems of robots in real-time. The identification of dynamical systems for industrial robots is cast as a sparse linear regression problem. By constructing the dictionary matrix, the parameters of the robot dynamics are effectively estimated via a re-weighted1-minimization algorithm. Online recursive methods are integrated into SBL to achieve real-time system identification. By including sparsity and promoting online learning, the proposed method can handle time-varying dynamical systems and therefore improve operational stability and accuracy. Experimental results on both simulated and real selective compliance assembly robot arm(SCARA) robots have demonstrated the effectiveness of the proposed method for industrial robots.

Deep adaptive control with online identification for industrial robots

Derivation of control equations from data is a critical problem in numerous scientific and engineering fields.The inverse dynamic control of robot manipulators in the field of industrial robot research is a key example.Traditionally,researchers needed to obtain the robot dynamic model through physical modeling methods before developing controllers.However,the robot dynamic model and suitable control methods are often elusive and difficult to tune,particularly when dealing with real dynamical systems.In this paper,we combine an enhanced online sparse Bayesian learning(OSBL)algorithm and a model reference adaptive control method to obtain a data-driven modeling and control strategy from data containing noise;this strategy can be applied to dynamical systems.In particular,we use a sparse Bayesian approach,relying only on some prior knowledge of its physics,to extract an accurate mechanistic model from the measured data.Unmodeled parameters are further identified from the modeling error through a deep neural network(DNN).By combining the identification model with a model reference adaptive control approach,a general deep adaptive control(DAC)method is obtained,which can tolerate unmodeled dynamics.The adaptive update law is derived from Lyapunov’s stability criterion,which guarantees the asymptotic stability of the system.Finally,the Enhanced OSBL identification method and DAC scheme are applied on a six-degree-of-freedom industrial robot,and the effectiveness of the proposed method is verified.

Combining flame monitoring techniques and support vector machine for the online identification of coal blends

The combustion behavior of two single coals and three coal blends in a 300 kW coal-fired furnace under variableoperating conditions was monitored by a flame monitoring system based on image processing and spectral analysis. A similaritycoefficient was defined to analyze the similarity of combustion behavior between two different coal types. A total of 20 flamefeatures, extracted by the flame monitoring system, were ranked by weights of their importance estimated using ReliefF, a featureselection algorithm. The mean of the infrared signal was found to have by far the highest importance weight among the flamefeatures. Support vector machine （SVM） was used to identify the coal types. The number of flame features used to build the SVMmodel was reduced from 20 to 12 by combining the methods of ReliefF and SVM, and computational precision was guaranteedsimultaneously. A threshold was found for the relationship between the error rate and similarity coefficient, which were positivelycorrelated. The success rate decreased with increasing similarity coefficient. The results obtained demonstrate that the system canachieve the online＂ identification of coal blends in industry.

基于阻抗在线测量的锂离子电池过放电诱发内短路识别研究

锂离子电池的过放电行为可诱发内短路进而可能导致热失控。由于单体电池一致性差异和过放电导致的内短路初期电、热特征不明显,使得电压和温度等常规物理参数难以可靠实现故障预警,而阻抗可以反映电池内部信息,对故障状态具有较好的指示能力。该文通过分析锂离子电池过放电诱发内短路引起阻抗变化的机理,基于设计的阻抗在线测量装置,确定用于监测电池内短路故障的特征阻抗频率为70 Hz,获取过放电诱发内短路过程中特征频率下动态阻抗及阻抗变化率,提出一种基于动态阻抗特征的内短路在线识别方法并验证了方法的可靠性。实验结果表明,锂离子电池放电过程中动态阻抗半正弦变化特征可提前约144 s实现过放电预警,动态阻抗针状变化特征可提前约152 s实现内短路故障预警,动态阻抗明显回升特征可作为内短路发生的标志。此外,阻抗变化率特征有助于实现锂离子电池故障识别及预警,该方法在锂离子电池故障在线快速诊断中具有重要的应用潜力。

高职“中药鉴定技术”在线精品课程规范化建设探索与实践

“中药鉴定技术”是高职高专中药、中药制药等专业学生的必修核心课程。本课程团队秉承知识传授、能力培养与价值引领三位一体的理念,依托省级“课程思政”示范课程建设项目,重新设定了在线开放课程建设的思路与目标,开发体现课程思政教育特点的教学资源,尝试进行“线上+线下”混合式教学模式。本文从在线精品课程的设计、课程建设、课程应用和课程思政建设及考核评价等方面介绍中药鉴定在线精品课程的规范化建设思路和内容,以期对同类职业院校的在线课程建设和专业课程教学提供一定的帮助与参考,也是对“课程思政”教学模式的有益探索。

融合Mind Map优势助力完善线上线下教学衔接--以园林树木学树种识别教学为例

疫情当前,线上线下混合式教学已经成为课程教学新模式。树种识别是园林树木学教学的重点和难点,也是教学目的之一。由于课程涉及的树种种类繁多,知识点琐碎,专业术语抽象,再加上课时少任务重,使得教师在教学过程中无法将所有树种的特征逐一讲解到,学生在学习过程中也容易混淆,无法有效吸收知识点。因此,在课程的教学改革中,通过引入Mind Map帮助厘清知识框架,优化知识结构,搭建知识关联,不仅使教师授课过程更顺畅,而且能够激发学生在线学习的兴趣,促使学生养成“整理知识点”的良好习惯,使学习效率大幅度提高,从而创建高效的线上课堂,有效巩固混合式教学的教学效果。

基于扇块化阈值判定的机载激光雷达可通行区域在线检测方法研究

激光受扫描区域与摄像点间角度、方向以及间隔距离等因素影响,导致激光可覆盖区域有限,从而影响可通行区域在线检测的准确性。为此,提出一种基于扇块化阈值判定的机载激光雷达可通行区域在线检测方法。扇块化阈值包括最大扫描距离阈值和畸变系数阈值,利用其判定机载激光雷达扇形块区域;采用最大熵值预测模型与调节系数处理扇形块区域数据,结合小波变换实现机载激光雷达可通行区域在线检测。实验结果表明,所提方法不仅检测到在4、6、7检测点处,道路宽度信息发生了变化,而且可以有效检测出区域内的障碍物,从而提高可通行区域的检测准确性,具备较好的检测效果。

面向高比例新能源电网短路计算的机电-电磁融合电源模型

为解决新型电力系统计算与分析中传统电源模型难以继续适用的问题,提出一种面向高比例新能源电网短路计算的机电-电磁融合电源模型。一方面,考虑新能源电源通用控制策略作用下的外部输出特性,采用单一压控电流源等效电源侧海量新能源电源的整体输出;另一方面,根据短路电流故障的时间划分尺度,保留传统发电机工频基波意义下的外部输出特性,并采用3个大小不一且衰减速度不同的电压源等效电源侧所有发电机的整体输出。进而采用3个电压源和1个压控电流源并联的形式作为融合电源模型的结构,并采用内点法对融合电源模型参数进行在线辨识。通过短路仿真数据和电网实测故障录波数据验证了融合电源模型的适用性。

电力系统在线安全稳定计算分析技术综述与展望

在线安全稳定计算分析技术是电力调度部门准确感知电网实时运行状态并做出正确评估决策的重要支撑。随着新型电力系统动态特性愈发复杂与随机,以稳态、非同步量测数据为基础的传统在线安全稳定分析体系逐渐表现出实时性和准确性上的不足,而以动态、同步相量量测数据为基础的新一代在线安全稳定分析技术愈发受到国内外相关科研及工程领域的关注。基于此,该文就实时状态感知、动态参数辨识和在线稳定评估3个核心组成部分对现有在线安全稳定计算分析体系所涉及的关键技术及瓶颈难题进行综述,并进一步提出新一代在线安全稳定计算分析技术的总体框架和未来研究方向,以期为新型电力系统安全稳定运行提供可靠技术保障。

在线开放课“课程思政”建设与实施研究——以高职“建筑装饰材料识别与选购”课程为例

随着新时代职业教育信息化的发展,以及全面落实“立德树人”根本任务的要求,研究如何把“课程思政”的思想引领贯穿线上线下全过程的教学管理中,实现课程思政育人目标,提高学生思想道德素质和职业素养尤为重要。以高职“建筑装饰材料识别与选购”课程为例,调研行业人才素质需求,利用典型工作岗位的任务教学法,收集、分析线上课程平台信息化教学管理数据,实施阶梯式模块项目教学模式,构建一个闭环的课程思政与专业课融合的教学环境。通过实践得出一套适用于高职同类课程的“以德载能三段式协同育人”混合式课程思政教学模式,并在建设与实施中取得了显著的育人成效。