基于P-模糊自适应PID控制的无刷直流电动机调速系统

分析了无刷直流电动机的数学模型,提出了一种新型的P-模糊自适应PID控制方法,并在Matlab/Simulink环境中建立了基于P-模糊自适应PID控制的无刷直流电动机调速系统仿真模型。在该调速系统中,电流控制采用电流滞环,转速控制采用P控制和模糊自适应PID控制相结合的方式,实现了电流滞环和转速模糊控制的双闭环调速控制功能。仿真结果表明,该系统与基于常规PID控制的调速系统相比,系统响应时间缩短一半,且超调减小,具有较强的鲁棒性和自适应能力。     

Stability analysis and implementation of chopper fed DC series motor with hybrid PID-ANN controller

The attempt is made to enhance the performance of a closed loop control of DC series motor fed by DC chopper (DC-DC buck converter) by hybridization of PID controller with an intelligent control using ANN (Artificial Neural Network) controller. This system consists of inner current controller loop and outer PID-ANN based speed controller loop. The current controller allows the PWM (Pulse Width Modulation) signal when the motor current is less than set value. The PID-ANN speed controller controls the motor voltage by controlling the duty cycle of the chopper thereby the motor speed is regulated. The PID-ANN controller performances are analyzed in both steady state and dynamic operating condition with various set speed and various load torque. The rise time, maximum over shoot, settling time, steady state error and speed drops are taken for comparison with conventional PID controller and existing work. The steady state stability analysis of the system also is made by using the transfer function model with MATLAB. The training data for PID-ANN controller is taken from conventional PID controller. The Hybrid PID-ANN controller with DC chopper has better control over the conventional PID controller and the reported existing work. This system is simulated using MATLAB/Simulink and also it is implemented with a NXP 80C51 family Microcontroller (P89V51RD2 BN) based Embedded System.     

基于MATLAB的无刷直流电机调速系统的建模与仿真

从无刷直流电机的数学模型出发,提出了一种无刷直流电机控制系统仿真建模的方法.该方法在Matlab/Simulink中,把独立的功能模块和s函数相结合,构建了无刷直流电机系统的仿真模型.系统采用双闭环控制:速度环采用PID控制,根据滞环电流跟踪型PWM逆变器原理实现电压控制.利用该模型分析了电机的动静态性能,得到了电机运行时的转速、转矩、相电流和反电动势曲线.仿真结果与理论分析一致,验证了该方法的合理性和有效性,为实际电机控制系统的设计和调试提供了新的思路.     

基于BP神经网络PID控制的BLDCM调速系统

在分析无刷直流电机(BLDCM)数学模型的基础上,采用改进型BP神经网络与传统PID相结合作为速度控制器,应用于无刷直流电机调速系统中。在电机初始运行阶段采用传统PID控制,网络学习一段时间后,切换到经过改进的BP神经网络在线自整定PID控制。仿真研究表明,应用这种新型控制方式的无刷直流电机调速系统具有良好的动态性能和稳态精度。     

Improved torque performance in BLDC-motor-drive through Jaya optimization implemented on Xilinx platform

Robust dynamic speed response with less maintenance and efficient operation makes the Brushless Direct Current (BLDC) motors as an oblivious choice for many motoring applications which needs instantaneous speed control while developing high torque. This paper proposes Jaya optimization Algorithm on Xilinx platform for BLDC Motor and Fractional Order Proportional Integral Derivative controller to improve its efficiency through minimizing the ripples present in the Torque of the motor. The control strategy of torque controller works with the combinations of Xilinx and optimization algorithms. In this proposed controller, the strategy of control is achieved after deriving the BLDC motor's dynamic mode there after the electronic interaction with BLDC motor is enrooted with the Math lab/Simulink model while tactically utilizing Xilinx tools for all kind of co-simulation in the same model. Initially the dynamic model of a BLDC motor drive is derived and the control topology is designed with the help of the proposed controller. The controlling mechanism established in this paper is effective and efficient in minimizing the ripples present in the torque of the motor. The proposed controller is utilized for possible low-cost and high-performance industrial applications. The results are obtained and analyzed with existing methodologies and it shows that the proposed technique outperforms the existing models.     

Compensation of torque ripple in high performance BLDC motor drives

Brushless DC motor drives (BLDC) are finding expanded use in high performance applications where torque smoothness is essential. The nature of the square-wave current excitation waveforms in BLDC motor drives permits some important system simplifications compared to sinusoidal permanent magnet AC (PMAC) machines. However, it is the simplicity of the BLDC motor drive that is responsible for causing an additional source of ripple torque commonly known as commutation torque to develop. In this paper, a compensation technique for reducing the commutation torque ripple is proposed. With the experimental results, the proposed method demonstrates the effectiveness for a control system using the BLDC motors that requires high speed and accuracy.     

ANFIS based multi-sector space vector PWM scheme for sensorless BLDC motor drive

This paper presents a design and development of Multi Sector Space Vector Pulse Width Modulation scheme (MS-SVPWM) for the speed control of brushless direct current (BLDC) motor drive. This control scheme is developed to enhance the performance of BLDC under wide range of speed and load variation. The hardware prototype is developed for 400 W, 30 V, 3000 rpm BLDC motor. The drive consists of uncontrolled rectifier unit for providing DC source to inverterunit. The proposed drive control has been done by implementing MS-SVPWM scheme using ANFIS control. The main function of ANFIS controller is to select the suitable sector for the drive and also predict the mismatching pulses by comparing conventional SVPWM and MS-SVPWM switching patterns. This new switching control technique helps to reduce switching losses of inverter and also improves an efficiency of BLDC system. This MS-SVPWM reduces the DC voltage ripple; Total Harmonic Distortion (THD) and torque ripple to the standard level. To verify and validate the practicality of the proposed system, the simulation is first performed using MATLAB Simulink tool. The hardware system is developed for the MS-SVPWM using DSPIC30F4011controller, the simulation and experimental results are presented.     

飞艇隔振云台电机控制器设计与实现

飞艇隔振云台的主要执行机构是三相无刷直流电机,电机控制的精度决定了隔振云台系统的隔振效果。传统的云台电机控制器设计方法是根据控制规律通过反复试凑,得到PID控制器参数,这种方法费时费力,需要一定的调参经验。针对云台调参问题,对无刷直流电机进行了理论分析与模型建立,并提供一种与传统方法不同的利用特定工具和技术手段获取模型参数的方法,通过Matlab中rltool根轨迹零极点配置工具设计出合理的控制器,使得电机具有良好的动态响应和稳态响应。并且在此基础之上将控制器实际运用到云台平台上,实验取得良好的控制效果,为隔振云台中三相无刷直流电机控制器设计与调参方案提供参考。     

A new approach for commutation torque ripple reduction of FPGA based brushless DC motor with outgoing phase current control

Brushless Direct Current Motor (BLDC) has been deployed across several kinds of applications. However, attaining a smooth torque ripple with fast response is relatively tough, as usually this is based on the varied slew rate line current in the commutation period. Hysteresis Current control has been widely used in earlier works for the maintaining incoming and outgoing phase current at the same rate throughout the commutation period. Additionally, this also helps in reducing the commutation torque ripple and delivers successful commutation. The proposed work here uses a relatively simple control technique that is primarily derived on the basis Outgoing-phase Current Discharge Hysteresis Control (OCDHC). This has been characterized to produce low response time and reduced torque ripple which has been suggested to execute in both conduction and commutation intervals. For the implementation of digital controller, we chose Xilinx Spartan 6 FPGA board. The digital controller algorithm is written using VHDL and is dumped on the FPGA. For this purpose we use Xilinx ISE and iMPACT tools. FPGA receives hall sensor output and current from BLDC motor with reduced torque ripple and generates the gate pulses which drive the IGBT switches using OCDHC control. This condition shows that the performance of the system is not primarily based on the motor parameters, excluding the stator resistance. Lastly, results obtained from the simulation and experimental results validate the significance of the proposed control technique based on response time at load conditions that vary at different junctures.     

Novel bacterial foraging-based ANFIS for speed control of matrix converter-fed industrial BLDC motors operated under low speed and high torque

In this paper, a novel adaptive neuro-fuzzy inference system (ANFIS)-based control technique optimized by Bacterial Foraging Optimization Algorithm for speed control of matrix converter (MC)-fed brushless direct current (BLDC) motor is presented. ANFIS is considered to be one of the most promising technologies for control of electrical drives fed by MC. Optimizing the training parameters of ANFIS, to improve its performance, is still being considered by several researchers recently. Parameters of the online ANFIS controller such as learning rate (η), forgetting factor (λ) and steepest descent momentum constant (α) are optimized by using the proposed algorithm. For the purpose of comparison, proportional integral derivative controller, fuzzy logic controller, PSO-ANFIS and BAT-ANFIS are considered. Set point tracking performances of the proposed system are carried out at various operating points for an industrial BLDC motor operating at a maximum rated speed of 380 rpm and torque of 6.4 N m. Time domain specifications such as rise time, settling time, peak time, steady-state error and peak overshoot in the presence and absence of load torque disturbances are presented. Time integral performance measures such as integral square error, integral absolute error, and integral time multiplied absolute error are analyzed for various operating conditions. Speed fluctuation in the output of BLDC motor is dependent on the source current harmonics of the inverter/converter. To illustrate this, total harmonic distortion (THD) analysis is carried out for the existing PWM inverter and the proposed MC, and it is proved that MC results in reduced THD, as compared to PWM inverter. Simulation results confirm that the proposed controller outperforms the other existing control techniques under various set speed and torque conditions. Statistical analysis is effectively carried out to prove the effectiveness of the proposed controller. Experimental analysis is performed to validate the performance of the proposed control scheme.

     

傅里叶变换光谱仪动镜运动控制仿真

在傅里叶变换光谱仪中,动镜按一定规律进行往复运动。一般控制方案采用直线电机直接推动,但在体积小而高分辨率的傅里叶变换光谱仪中,直线电机要做得很长,不是很合适。采用无刷直流电机进行动镜控制;分析了无刷直流电机(BLDC)数学模型和动镜速度均匀性对光谱仪的影响,采用自适应在线遗传算法整定的PID控制和传统PID控制方法分别进行仿真控制,着重分析了带负载无刷直流电机的控制效果,并人工加入负载扰动。仿真结果验证了采用合适的控制策略控制无刷直流电机可达到傅里叶变换光谱仪对速度均匀性的要求。     

基于DSP/BIOS的无刷直流电机控制系统的研制

无刷直流电机继承了直流电机运行效率高、调速性能好等优点,同时,克服了直流电机电刷带来的噪声、火花等缺点,在诸多领域得到广泛应用.为改善无刷直流电机控制系统的性能,本文研究模糊自适应PI控制器在无刷直流电机控制系统的应用.系统以TMS320F2812数字信号处理器为控制器设计了控制系统硬件,基于DSP/B10S嵌入式操作...     

模糊PID矢量控制在异步电机调速中的应用

为了提高三相异步电机矢量控制的性能,在传统的转速PID控制器的基础上,建立模糊PID控制器,利用Matlab/Simulink搭建基于三相异步电动机转速控制的模糊PID系统,分别使用常规PID控制器与模糊PID控制器进行控制,并进行比较。仿真结果表明模糊控制能使系统取得较好的控制性能并具有较强的鲁棒性。     

双电机同轴驱动系统功率追踪算法研究

研究同轴双电机负载功率追踪问题,在该系统中,两台电机速度被强制同步,由于两台相同电机参数略有差异,将导致功率分配不平衡。基于功率平衡要求,根据矢量控制理论,将交流异步电机的电机转矩和电机磁通的解耦,得到双电机功率(电流)追踪的数学模型,通过设计模糊控制器与迭代算法进行控制,进行了同轴双电机功率追踪问题仿真。仿真结果表明,模糊PID控制与迭代算法控制的系统相比,可以提高功率跟踪的响应速度,更快达到功率平衡。     

直流电机神经元PID调速系统设计与仿真

针对传统PI双闭环直流电机调速系统存在响应速度慢、超调量大、抗干扰能力及自适应能力差等问题,提出了一种双闭环直流电机调速系统的神经元PID转速调节器设计方法。该转速调节器采用神经元控制器和比例控制相结合进行设计,从而构成了一种具有自学习、自适应能力的神经元PID控制器,然后与传统单神经元PID设计的转速调节器控制效果进行了对比。结果表明,基于神经元PID转速调节器的双闭环直流电机调速系统具有较快的响应速度、良好的动态和静态稳定性、较强的自适应能力和抗干扰能力。     

某履带式全方位平台的电机控制器设计

针对某履带全方位平台实际工况要求,介绍了平台总体方案,选取一款1000W无刷直流电机（BLDC）作为平台驱动电机,阐述了BLDC控制原理及其控制方案,基于DSP28335设计了电机控制器,搭建了硬件电路,编写并调试了软件程序,采用速度、电流双闭环控制方案,设定电机转速为3000rpm,测定了电机相电压、电流等数据,进行了实物实验,结果表明该控制器性能稳定,响应迅速,具有较高的实际应用价值,且为后续控制策略研究奠定了基础。     

基于DSP2812的电惯量模拟控制系统设计

电惯量技术即是按照一定的控制算法用电动机的输出力矩和转速来模拟机械惯量的方法。为了保证电惯量控制系统的控制性能,该文在阐述电惯量控制系统工作原理的基础上,建立了电惯量控制的数学模型,采用TI公司的DSP2812控制芯片作为核心控制单元,构成了电惯量控制系统的框架集成硬件平台,并对之进行软件分析与设计。该系统内部的神经网络PID控制模块,实现了电动机对机械惯量制动器的模拟。试验结果表明,该系统稳定可靠,响应速度快。     

机器人关节用高精度交流伺服系统研究与实现

机器人关节驱动要求满足响应速度快﹑运行稳定及精度高等性能指标,其关键是通过转矩控制及位置调节器设计来满足要求;首先针对非理想反电势的永磁交流伺服机,对转矩构成进行了推导,分析了转矩和反电动势、占空比的关系,提出了一种基于离线反电势测量实现了抑制转矩脉动的控制方法;其次,位置伺服采用了模糊自适应PID控制器实现位置的快速跟踪控制,采用此法能够克服单纯使用PID控制存在的超调和振荡,使系统输出超调明显减小,定位精度得到提高。在实验系统中,采用以英飞凌公司的XC167CI控制系统和功率MOSFET逆变器组成的伺服系统,实现了速度阶跃响应时间<20ms,速度稳定精度±2%,转矩精度±1%。整个过程速度响应迅速又运动平稳,位置能也迅速跟踪给定信号,且位置响应无超调;证明所提出的方法对于提高系统的性能是有效的,最后将其成功应用于自行研制的高压输电线巡检机器人控制系统中,获得了满意的效果。     

无轴承同步磁阻电机逆系统解耦控制仿真研究

研究无轴承同步磁阻电机稳定性控制问题,由于无轴承同步磁阻电机是一个强耦合的非线性系统,为实现负载条件下的稳定悬浮运行,需解除电机转矩和径向悬浮力等多变量之间的耦合关系。针对前馈补偿解耦的缺陷,给出了无轴承同步磁阻电机包含转矩控制和悬浮力控制的统一数学模型,证明电机逆系统存在,设计了一种通过非线性状态反馈的逆系统解耦控制方案,将复杂的无轴承同步磁阻电机系统解耦成两个转子径向位置二阶积分子系统和一个转速一阶积分子系统,并用PI和PID调节器分别对转子位置与转速进行综合设计。运用MATLAB软件对电机控制系统进行仿真。仿真结果证明,解耦控制方案的有效性,为电机系统优化设计提供了保证。     

基于PID神经网络的多轮轮毂电机协调控制研究

多轮轮毂电机运行参数具有不确定性,因此难以精准计算电机协调控制量,所以设计基于PID神经网络的多轮轮毂电机协调控制方法。搭建多轮轮毂电机数学模型,在该模型下实时采集多轮轮毂电机转速、位置等运行参数,并计算电机协调控制量。装设PID神经网络协调控制器,利用PID神经网络算法生成协调控制指令,从转速、转矩等方面实现多轮轮毂电机的协调控制。测试结果表明,在所提方法的协调控制下,多轮轮毂电机在空载和负载工况下的转矩控制误差降低了21.25r/min和25.7r/min,转矩控制误差降低了6.55N·m和17.45N·m,电机运行平衡度有所提升,多轮轮毂电机协调控制效果更好。