直流电机调速控制和测速系统设计

直流电机在制动、启动以及调速等方面有良好的性能,在直流测速发电机、马达、机床转轴等旋转设备的转速控制和转速采集中的应用非常广泛.近年来,以微处理器为核心的数字系统已经发展成为主流的直流电机控制系统.针对传统直流电机在调速时稳定性、精确度和抗干扰能力等方面的缺点,考虑到将直流电机的转速通过霍尔传感器进行变换再送到单片机中...     

直流电机过流检测装置

简述了直流电机主回路过流检测装置的工作原理与实现。本系统具有内保护功能，适用于１０００ＶＤＣ以下的直流电机，体积小，成本低，经现场３５００ＨＰ、７００ＶＤＣ直流电机上运行近三年时间，一直工作良好。     

基于形态滤波器和中心极值差分的直流电机间接测速

提出了一种基于数学形态学和中心极值差分的直流电机间接测速新方法.首先,在介绍数学形态学理论的基础上,构建了多元素结构并行复合形态滤波器;其次,根据直流电机电流的特点,提出了基于中心极值差分(CED,central extreme difference)的电流频率求取方法;最后根据电机电流频率和转速间的关系,获取了电机的转速特性.较之小波阈值消噪,通过形态学滤波获取的电流曲线较光滑,滤波效果更好;与基于小波脊线的测速方法相比,所提方法不仅获取的转速曲线波动程度较小,而且计算速度也有大幅度的提高.     

直流电机转速神经网络控制

直流电机速度跟踪控制问题中，由于未知的负载转矩特性存在常规的常值反馈控制不能满足较高的性能指标要求。采用前馈神经网络在线辨识，设计出一种合适的控制率。仿真结果证明此种方法的可行性。     

基于PLC的直流电机转速控制系统设计

以西门子PLC输出的模拟电压控制PWM发生器的占空比,进而实现直流电机的PWM调速控制。在设计中,西门子S7-200系列CPU224XPCN（AC／DC／RLY）作为整个控制系统的核心部分,配备PWM产生模块、测速电路模块以及相应的调速按钮和系统指示灯,实现对电机转速的测量和调节。最后进行设计的实际测试,结果表明：用PLC来实现直流电机的转速控制效果符合控制要求。     

大型直流电机计算机检测系统

本文介绍在大型直流电机的检测过程中,用计算机取代传统的目测读数、手工记录等落后的检测方法,实现微机对电压、电流、温度、压力、流量及转速等参数的自动检测;随时打印试验报告、经历簿、绘制各种电机数据曲线,并描述该系统的总体结构、硬件软件的设计思想.     

基于CAN总线的数字式直流电机控制系统

本文介绍了一种以P80C592单片机构成的数字式直流电机控制系统，单片机与上位机之间的通信采用了CAN总线，文中给出了具体的硬件电路和软件的设计方法，并给出了部分电路的原理图。     

基于单片机的电机测速系统设计

电机测速系统在当今的科技领域有着非常广泛的应用,目前对该系统的设计也成为了相关领域的热门话题。本文以单片机为核心基础,对电机测速系统的结构进行了了解。并分析系统设计中的硬件设计以及软件设计要点,最终设计出基于单片机的合格电机测速系统,希望能为多样化、准确、稳定的电机测速工作提供参考。     

82C54在电机测速电路中的应用

可编程计数器82C54具备Intel处理器操作接口,有多种工作模式,使用灵活简便。提出一种以82C54为计数单元的电机测速方案,以光电编码器为传感器,同时采集电机的瞬时转速和周期转速数据,作为电机瞬时稳定度和周期稳定度性能分析依据。     

滑差电机频率法测速系统

滑差电机频率法测速系统东南大学李鸿寿，蒋守国ｌ概述微机控制燃烧系统中，给煤机转速是个重一Ｋ的被调量。一般给煤，采用ＪＺＩＹ系列滑差电机‘’‘，它由三相异步电动机，滑差离合器和同步测速发电机组成。与ＪＤＩ；Ａ控制器构成一套具有速度负反馈，可调节ｊ’＃．...     

最小拍无波纹直流电机控制系统仿真研究

在单位阶跃、单位速度和单位加速度3种典型输入下对直流电机的最小拍无波纹控制进行了研究.Matlab仿真结果表明,最小拍无波纹控制,输出能较快的跟随输入,且跟随后能基本消除静差.最小拍无波纹控制应用在直流电机控制系统中,能够使电机的转速在最少个采样周期内跟随期望转速,使系统达到无静差的稳态,表明了最小拍无波纹控制能实现较高的控制品质.     

基于连续可微摩擦模型的直流电机扰动补偿自适应控制

摩擦和未建模动态是电机伺服系统普遍存在的非线性问题,针对这些问题,以直驱直流电机为研究对象,建立了包含连续可微摩擦模型的系统模型,通过对未建模动态的上界进行自适应估计,设计了扰动补偿鲁棒反馈项,进一步采用基于指令的参数回归器,设计了基于指令及指令导数的模型补偿项,降低了系统跟踪性能对测量噪声的敏感度,采用Lyapunov函数证明稳定性,仿真对比结果验证了控制器对于摩擦和扰动具有较好地补偿效果。     

基于连续可微摩擦模型的直流电机扰动补偿自适应控制

探讨了人参与和主导的信息融合技术,阐述了人所承担的重要作用和人在环中的信息融合实现方式,针对应用提出一种目标综合识别模型,建立了人机协作的识别融合处理框架,以及预处理、数据关联、证据综合、融合推理、识别判定等环节的处理模型,充分结合人的认知思维与机器高速精确计算的优势,为解决相关实际问题提供一种可行技术途径。     

RCMAC-based adaptive control design for brushless DC motors

This paper proposes a recurrent cerebellar model articulation controller (RCMAC)-based adaptive control for brushless DC motors. This control system is composed of a RCMAC and a compensation controller. RCMAC is used to mimic an ideal controller, and the compensation controller is designed to compensate for the approximation error between the ideal controller and RCMAC. The Lyapunov stability theory is utilized to derive the parameter tuning algorithm, so that the uniformly ultimately bound stability of the closed-loop system can be achieved. For comparison, a fuzzy control, an adaptive fuzzy control and the developed RCMAC-based adaptive control are implemented on a field programmable gate array chip for controlling a brushless DC motor. Experimental results reveal that the proposed RCMAC-based adaptive control system can achieve the best tracking performance. Moreover, since the developed RCMAC-based adaptive control scheme uses a hyperbolic tangent function to compensate for the approximation error, there is no chattering phenomenon in the control effort. Thus, the proposed control method is more suitable for real-time practical control applications.     

A parallel neuro-controller for DC motors containing nonlinear friction

This paper presents an application of a parallel neuro-controller for compensating the effects induced by the friction in a DC motor system. A back-propagation neural network based on a gradient descent algorithm is employed, and a bound on the tracking error is derived from the analysis of the tracking error dynamics. The parallel neuro-controller is a combination of a linear controller and a neural network controller which compensates for nonlinear friction. The proposed scheme is implemented and tested on an IBM PC-based DC motor control system. The algorithm, simulations, and experimental results are described. The results are relevant for many precision drives, such as those found in industrial robots.     

An improved nonlinear control design for series DC motors

A series DC motor must be represented by a nonlinear model when nonlinearities such as magnetic saturation are considered. To provide effective control, nonlinearities and uncertainties in the model must be taken into account in the control design. In this paper, the recursive design method is applied to generate nonlinear control, nonlinear PI control, and robust control, and these controls are shown to be efficient and robust in the simulation study compared to existing results.     

Agent-based consensus on speed in the network-coupled DC motors

In this paper, a new agent-based method is proposed to address the speed synchronization problem in the network-connected motors. In this study, DC motor is used, driven by a buck chopper circuit. In the proposed method, the consensus protocol of the leader-following multi-agent system is modified, in order to make consensus on the speed among multiple motors in the network, so that they can attain synchronous speed. In order to have a stable system, a common Lyapunov function is developed such that consensus is said to be reached if the ith agent is controllable and observable. MATLAB is used for the purpose of simulation, and results obtained authorize the proposed methodology.

     

关于凸轮轴测量系统的数据分析

凸轮轴的精度直接影响到汽车发动机的噪声、动力性能、经济性等整体性能指标,德国汽车工业提出了测量能力指数Cg.介绍了一种凸轮轴综合测量仪的设计,并对该测量仪的数据进行了Cg分析.经分析认为,所研制的凸轮轴测量仪达到使用要求.     

应用无损耗电阻器的直流电机速度控制方法研究

将无损耗电阻器应用于直流电动机调速系统，采用瞬时值补偿控制方法，通过控制无损耗电阻器的输出，实现了电动机的转速控制，并改善了速度控制系统的动态特性，无损耗电阻器控制方案适用于要求无超调且响应快的系统，如某些机器人速度－位置控制系统和机械加工控制系统。     

Adaptive diagnosis of DC motors using R-WDCNN classifiers based on VMD-SVD

Traditional fault diagnosis methods of DC (direct current) motors require high expertise and human labor. However, the other disadvantages of these methods are low efficiency and poor accuracy. To address these problems, a new adaptive and intelligent mechanical fault diagnosis method for DC motors based on variational mode decomposition (VMD), singular value decomposition (SVD), and residual deep convolutional neural networks with wide first-layer kernels (R-WDCNN) was proposed. First, the vibration signals of a DC motor were collected by a designed acquisition system. Subsequently, VMD was employed to decompose the raw signals adaptively into several intrinsic mode functions (IMFs). Moreover, the transient frequency means method, which can quickly and accurately obtain the optimal value of K, is proposed. SVD was applied to reduce the dimensionality of the IMF matrix for further feature extraction. Finally, the reconstructed matrix containing the main fault feature information was used to train and test the R-WDCNN. Based on residual learning, identification and classification of four types of vibration signals were achieved, while the R-WDCNN was optimized by the adaptive batch normalization algorithm (AdaBN). The recognition rate and the convergence were improved by this classifier. The results show that the method proposed in this paper has better adaptability and intelligence than other methods, and the R-WDCNN can reach a 94% recognition rate on unknown samples. Therefore, the proposed method is more intelligent and accurate than other methods.