基于Matlab的制芯机器人夹持器稳定性研究与分析

由于铸造厂制芯机器人的工作环境恶劣,机器人抓取砂芯的稳定性较差,时常出现掉砂芯、夹伤砂芯的问题。针对上述问题建立以夹持器、气压、机械振动的系统数学模型,采用高阶系统极点在[s]平面的分布法来判定该系统的稳定性。在确保高阶系统极点值不变的情况下,通过改变系统的零点值来观察零点值对系统稳定性、振荡衰减时间的影响。用Matlab的pzmap和impulse函数分别对系统的零点、极点分布和零点值的不同对系统响应进行仿真,运用仿真结果与现场试验进行优化参数,结果表明采用一种刚度与柔度各为1/2的夹持器对制芯机器人的稳定性具有较好优化效果。     

含有蓄能器的液压伺服系统动态特性仿真设计

该文以一类常见的电动液压能源为对象,建立了含有蓄能器的电动液压能源的仿真模型,并对能源的启动特性以及负载工作所要求的多种信号的跟踪能力进行了仿真分析。     

Improved synergetic excitation control for transient stability enhancement and voltage regulation of power systems

This paper proposes decentralized improved synergetic excitation controllers (ISEC) for synchronous generators to enhance transient stability and obtain satisfactory voltage regulation performance of power systems. Each generator is considered as a subsystem, for which an ISEC is designed. According to the control objectives, a manifold, which is a linear combination of the deviation of generator terminal voltage, rotor speed and active power, is chosen for the design of ISEC. Compared with the conventional synergetic excitation controller (CSEC), a parameter adaptation scheme is proposed for updating the controller parameter online in order to improve the transient stability and voltage regulation performance simultaneously under various operating conditions. Case studies are undertaken on a single-machine infinite-bus power system and a two-area four-machine power system, respectively. Simulation results show the ISEC can provide better damping and voltage regulation performance, compared with the CSEC without parameter adaptation scheme and the conventional power system stabilizer.     

Design of disturbance observer based on adaptive-neural control for large-scale time-delay systems in the presence of actuator fault and unknown dead zone

This article presents an adaptive neural compensation scheme for a class of large-scale time delay nonlinear systems in the presence of unknown dead zone, external disturbances, and actuator faults. In this article, the quadratic Lyapunov–Krasovskii functionals are introduced to tackle the system delays. The unknown functions of the system are estimated by using radial basis function neural networks. Furthermore, a disturbance observer is developed to approximate the external disturbances. The proposed adaptive neural compensation control method is constructed by utilizing a backstepping technique. The boundedness of all the closed-loop signals is guaranteed via Lyapunov analysis and the tracking errors are proved to converge to a small neighborhood of the origin. Simulation results are provided to illustrate the effectiveness of the proposed control approach.     

基于“天空地”一体化技术的安全监测系统建设与应用

西藏江达县白格村金沙江右岸于2018年10月11日和2018年11月3日先后发生2次大规模滑坡—堰塞湖堵江事件,溃堰洪水对下游拉哇库区不良地质体的稳定性造成不同程度的影响。为保障下游水电站建设安全,对拉哇库区主要不良地质体建立了基于星载InSAR技术、无人机技术和地面传感器实时监测的“天空地”一体化监测预警体系,以多维空间采集技术获取变形信息,通过智能监控平台对信息及时进行处理、分析和可视化呈现,利用平台、短信等方式向相关人员进行分级告警,取得了较好的应用效果。     

起重机械防碰装置试验系统的设计

防碰装置是起重机械重要的安全防护装置,其性能质量直接关系到人员和设备安全。常规的防碰装置性能试验方法难以保证试验数据的精度和可靠性。针对防碰装置的工作特点和试验要求,从试验原理、系统结构、运行逻辑等方面入手,设计一套能模拟防碰装置实际运行工况,自动进行性能试验和数据采集处理,精度高、重复性好的试验系统,可以有效提升防碰装置检测能力。     

Operational time-series data modeling via LSTM network integrating principal component analysis based on human experience

Today’s information technologies involve increasingly intelligent systems, which come at the cost of increasingly complex equipment. Modern monitoring systems collect multi-measuring-point and long-term data which make equipment health prediction a “big data” problem. It is difficult to extract information from such condition monitoring data to accurately estimate or predict health statuses. Deep learning is a powerful tool for big data processing that is widely utilized in image and speech recognition applications, and can also provide effective predictions in industrial processes. This paper proposes the Long Short-term Memory Integrating Principal Component Analysis based on Human Experience (HEPCA-LSTM), which uses operational time-series data for equipment health prognostics. Principal component analysis based on human experience is first conducted to extract condition parameters from the condition monitoring system. The long short-term memory (LSTM) framework is then constructed to predict the target status. Finally, a dynamic update of the prediction model with incoming data is performed at a certain interval to prevent any model misalignment caused by the drifting of relevant variables. The proposed model is validated on a practical case and found to outperform other prediction methods. It utilizes a powerful deep learning analysis method, the LSTM, to fully process big condition monitoring series data; it effectively extracts the features involved with human experience and takes dynamic updates into consideration.     

以用户为中心的可见光通信协作传输中训练资源分配算法

以用户为中心的可见光通信协作传输是近年来出现的新架构,这导致虚拟小区之间出现重叠。为避免导频污染问题,每个虚拟小区中的光接入点(AP)或者虚拟小区中选择相同AP的用户发送的训练序列应该是正交的。针对可见光通信中以用户为中心的协作网络,研究训练资源的正交分配问题,提出了一种新的导频分配算法,联合导频分配和用户选择问题,以期最大限度地增加虚拟小区内可被接入的用户数。分析和仿真结果表明,该导频分配方案可以有效改善导频污染问题,提高训练资源利用率,并且相比已有的导频分配方案,性能有所改进。     

釉面分相对无铅CuO-MnO2系金属光泽釉的影响

以钙系生料釉为基础,引入结晶剂CuO、MnO₂制备无铅CuO-MnO₂系金属光泽釉;结合XRD、SEM-EDS进行物相组成定性分析和显微结构表征,系统探究外加TiO₂、V₂O₅以及玻璃粉对金属光泽釉釉面分相的影响。研究表明:一定量的TiO₂、V₂O₅引入能有效促进釉面的分相,将玻璃粉部分替换基础釉中的钾长石能使得釉熔体的高温粘度降低,并进一步加剧釉面分相,促进CuMn₂O₄铜锰尖晶石在釉层表面的析出和富集;当TiO₂引入量为2%,V₂O₅引入量为1%,玻璃粉引入量为25%(同为质量分数)时,金属光泽釉釉面效果最佳。     

智能化救援监控系统在煤矿安全生产中的应用

针对煤矿发生事故后传统救援监控系统无法实时对井下人员进行动态定位，导致矿井救援盲目性大、救援效率差、救援难度大等技术难题，为了进一步提高煤矿救援效率，通过技术研究，设计了一套以通信基站为核心的智能化救援监控系统，分析了该系统结构组成、工作原理，通过在担水沟煤矿井下实际应用效果来看，智能化救援监控系统对人员定位精准度达95%，实现人员动态位置三维成像，救援效率提高至80%以上，有效缩短了煤矿事故救援时间，取得了显著应用成效。