扩展卡尔曼滤波结合前馈补偿永磁同步电机位置估计

转速和转子位置的精确估计对建立永磁同步电机(permanent magnet synchronous moter,PMSM)转速、电流双闭环矢量控制系统非常重要.本文主要研究扩展卡尔曼滤波算法(extended Kalman filter,EKF)估计转速、转子位置问题.与传统EKF估计转子位置方法不同的是,本文采用遗传算法(GA)优化EKF的协方差矩阵,并给出P,Q,R矩阵选取过程.另外将负载转矩观测器观测的负载转矩同速度调节器的输出一起作为电流调节器的控制变量.仿真及实验结果表明:文中提出的新方法有效缩短系统协方差参数选取时间,提高转速的辨识精度和抗负载扰动能力.     

基于参数优化的永磁同步电动机直接转矩控制系统自抗扰控制器研究

针对基于PI控制器的永磁同步电动机直接转矩控制系统存在转矩波动大、易受负载变化影响的问题,设计了一种基于转速外环的自抗扰控制器,代替PI控制器以改善永磁同步电动机直接转矩控制系统的性能;采用粒子群优化算法对自抗扰控制器的相关参数进行了优化计算,改进了控制器的调节性能。仿真和实验结果表明,基于参数优化自抗扰控制器的永磁同步电动机直接转矩控制系统具有较高的抗负载扰动能力,更快的响应速度和良好的动、静态性能。     

基于预测控制的PMSM优化仿真研究

针对在永磁同步电机的运行过程中,容易受到负载转矩扰动和参数失配的影响进而造成转速跟踪性能差和电流脉动大等问题,采用模型预测控制(MPC)的方法设计预测控制器取代传统的比例-积分(PI)调节器,对速度和电流分别做了最优控制;加入Kalman滤波器对负载转矩进行观测作为前馈补偿,并提出参数校正方法,有效地抑制了干扰负载和电感参数的不确定变化对电机性能的影响.通过Simulink对比仿真表明,上述设计方法具有更快的动态响应且无超调,同时抗干扰能力和参数鲁棒性得到显著提升.     

直流脉宽调速系统滑模变结构控制

在直流电机调速系统优化控制的研究中,针对常规滑模变结构控制的双闭环直流调速系统在添加负载扰动后转速响应存在静差的问题.为解决上述问题,提出了考虑扰动在内的转速环滑模变结构控制方案.由于扰动补偿作用的加入加大了电流给定的抖动,使回路电流脉动较大,在上述设计的基础上,在控制器输出添加了滤波器,有效的解决了上述问题.通过MATLAB仿真验证后,在dSPACE DS1103单板系统的支持下,将所设计的控制器与实际电机相连,通过在线调节参数,获取理想曲线.实验结果表明所设计的滑模控制器(smc)具有较好的鲁棒性,系统转速无超调,电流较平滑,抗扰能力较强,为直流电机调速系统优化提供了参考.     

不确定性直接转矩控制系统

在异步电动机直接转矩控制系统中，由于定子电阻变化及负载扰动的不确定性，导致定子磁链、转子转速和电磁转矩估计不准确，从而影响系统的调速性能.本文基于扩展Kalman 滤波器，引进虚拟噪声补偿技术，然后采用sage 和Husa 噪声统计估值器，构成鲁棒扩展Kalman 滤波器.并将定子电流，定子磁链，转子转速，定子电阻及负载作为状态变量，基于鲁棒扩展Kalman 滤波器进行了大量实验研究.实验结果证实：状态变量能够准确估计，且转矩脉动优于常规的直接转矩控制方案，实现了高性能无速度传感器的直接转矩控制系统.     

基于负载观测的抗扰动伪微分反馈策略电机控制

针对电机伪微分反馈(PDF)策略控制系统中的负载突变问题,为了进一步提高其抗负载突变能力,将负载观测器融合到电机PDF策略控制器系统中.采用能够直接测量的转角为已知观测量,设计出降阶负载转矩观测器,推导出观测器与PDF策略融合的控制律,得到抗负载扰动的电机PDF策略控制系统,从而获得更优良的抗负载扰动能力.结合实际应用对象,建立相应控制系统的Simulink仿真模型,通过仿真验证比较本文设计的抗负载扰动的电机PDF策略控制系统与普通的电机PDF策略控制系统的控制效果.其结果表明,采用本文方法设计的电机PDF策略控制系统具有更优良的抗负载扰动能力.     

永磁同步电动机自适应超螺旋控制仿真

为优化永磁同步电动机的转速控制精度,使其达到需求标准,提出一种自适应Super-Twisting控制方法.基于构建的三相永磁同步电动机数学模型,架构超螺旋控制算法与超螺旋滑模观测器增益条件,利用电动机电磁转矩与定子磁链,分别设计自适应超螺旋滑模磁链控制器与自适应超螺旋滑模电磁转矩控制器,将符号函数替换成准滑动模态的激励函数,完成超螺旋磁链控制器与超螺旋转矩控制器优化,经添加坐标变换模块,令自适应超螺旋控制器的输出电压与空间矢量调制模块的输入电压相一致,采用二阶滤波环节平滑转速指令,转换速度追踪问题为速度调节问题,实现自适应控制.仿真结果表明,所提方法在不同扰动作用下均具有快速、稳定的参考转速跟踪效果、精确的扰动估计以及较好的抗扰动性与自适应性.     

基于ADRC的起动机负载测试系统

负载扰动、摩擦力扰动、纹波推力扰动和其他不确定扰动严重影响汽车起动机负载测试系统的测试精度和测试速度,电流和转速的协调控制也对系统提出了更高的要求.该文采用自抗扰控制(ADRC)方法,通过扩张状态观测器(ESO)估计出所有未知扰动作用量并给予实时动态补偿,以抑制未知扰动实现“自抗扰”控制,实现了对电流和转速的解耦控制.利用工控机和数据采集卡构建控制系统实验平台,实验结果表明,采用ADRC的起动机负载测试系统测试精度和速度都有较大提高.     

基于非线性自抗扰控制器的PMSM直接转矩控制

针对传统的永磁同步电机(PMSM)直接转矩控制中转矩脉动和磁链脉动较大及转速超调等问题,研究一种基于非线性自抗扰控制的PMSM直接转矩控制策略.将传统的PI控制器替换成非线性的自抗扰控制器,设计转速环自抗扰控制器.自抗扰控制器中的扩张状态观测器将外部扰动和未知系统的参数的变化进行估计,并通过补偿手段加以控制,提高系统的抗干扰性能.微分跟踪器将给定转速平滑化,使得系统快速跟踪给定的转速信号,提高系统的响应能力.仿真实验验证了该策略的可靠性和有效性.     

一种BUCK变换器的抗扰动控制方法

为解决降压变换器中存在多种扰动(如输入电压变化和负载变化等)严重影响输出电压的问题,提出了一种BUCK变换器的抗扰动控制方法。首先,采用变参数PI(VAPI)控制器代替传统PI控制器,作为改进的PI控制方法;然后,设计扰动观测器(DOB)观测参数摄动与负载变化带来的系统扰动,作为补偿量补偿到前馈通道,提高系统的收敛速度与抗扰动能力;最后,通过仿真验证了该算法的有效性。     

永磁同步电机的PPI-RES电流控制策略

采用常规比例-积分-谐振(PI-RES)电流控制器可抑制永磁同步电机(PMSM)相电流谐波.然而, 电机运行于高输出频率/采样频 率工况时, 系统受数字控制器一个采样周期延时的影响, 将出现电流震荡现象.为了解决上述问题, 提出一种预测比例-积分-谐振(PPI-RES)电流 控制策略.该方法分别利用电流误差微分模型和积分模型预测扰动电压和误差电流, 实现对输入延时的有效补偿.仿真结果验证了所提出电流控制策略的有效性.     

感应电机转子电阻辨识及速度控制策略

感应电机转子电阻的在线辨识和补偿是提高矢量控制系统性能的重要手段。针对感应电机模型的不确定性和非线性,基于扩展卡尔曼滤波(EKF)技术,设计感应电机转子电阻在线估计器,采用模糊控制理论,设计模糊PI速度控制器,根据系统状态的变化,以模糊控制器的输出对PI控制器参数进行修正,从而改善系统的静、动态性能。Matlab仿真结果证明了所设计的EKF转子电阻辨识器和模糊PI控制器的有效性。     

永磁同步电机多采样率EKF转速辨识

永磁同步电机（Permanent Magnet Synchronous Motor,PMSM）具有响应快、高精度、高转矩比等诸多优点,同时无传感器控制策略研究能有效提高PMSM系统的简易性和鲁棒性。在分析EKF和多采样率数字控制系统的基础上,建立永磁同步电机输入多采样率EKF算法,将其用于转速估计。通过仿真和实时实验验证其算法在辨识精度及收敛稳定性方面均优于单采样率EKF算法,并和高频单采样率EKF有着一致的辨识效果,而多采样率EKF算法的数据量及运算量均小于高频单采样率EKF。     

On adaptive trajectory tracking of a robot manipulator usinginversion of its neural emulator

This paper is concerned with the design of a neuro-adaptive trajectory tracking controller. The paper presents a new control scheme based on inversion of a feedforward neural model of a robot arm. The proposed control scheme requires two modules. The first module consists of an appropriate feedforward neural model of forward dynamics of the robot arm that continuously accounts for the changes in the robot dynamics. The second module implements an efficient network inversion algorithm that computes the control action by inverting the neural model. In this paper, a new extended Kalman filter (EKF) based network inversion scheme is proposed. The scheme is evaluated through comparison with two other schemes of network inversion: gradient search in input space and Lyapunov function approach. Using these three inversion schemes the proposed controller was implemented for trajectory tracking control of a two-link manipulator. Simulation results in all cases confirm the efficacy of control input prediction using network inversion. Comparison of the inversion algorithms in terms of tracking accuracy showed the superior performance of the EKF based inversion scheme over others.     

Credit assigned CMAC and its application to online learning robust controllers

In this paper, a novel learning scheme is proposed to speed up the learning process in cerebellar model articulation controllers (CMAC). In the conventional CMAC learning scheme, the correct numbers of errors are equally distributed into all addressed hypercubes, regardless of the credibility of the hypercubes. The proposed learning approach uses the inverse of learned times of the addressed hypercubes as the credibility (confidence) of the learned values, resulting in learning speed becoming very fast. To further demonstrate online learning capability of the proposed credit assigned CMAC learning scheme, this paper also presents a learning robust controller that can actually learn online. Based on robust controllers presented in the literature, the proposed online learning robust controller uses previous control input, current output acceleration, and current desired output as the state to define the nominal effective moment of the system from the CMAC table. An initial trial mechanism for the early learning stage is also proposed. With our proposed credit-assigned CMAC, the robust learning controller can accurately trace various trajectories online.     

Real-time torque control using discrete-time recurrent high-order neural networks

This paper presents a discrete-time direct current (DC) motor torque tracking controller, based on a recurrent high-order neural network to identify the plant model. In order to train the neural identifier, the extended Kalman filter (EKF) based training algorithm is used. The neural identifier is in series-parallel configuration that constitutes a well approximation method of the real plant by the neural identifier. Using the neural identifier structure that is in the nonlinear controllable form, the block control (BC) combined with sliding modes (SM) control techniques in discrete-time are applied. The BC technique is used to design a nonlinear sliding manifold such that the resulting sliding mode dynamics are described by a desired linear system. For the SM control technique, the equivalent control law is used in order to the plant output tracks a reference signal. For reducing the effect of unknown terms, it is proposed a specific desired dynamics for the sliding variables. The control problem is solved by the indirect approach, where an appropriate neural network (NN) identification model is selected; the NN parameters (synaptic weights) are adjusted according to a specific adaptive law (EKF), such that the response of the NN identifier approximates the response of the real plant for the same input. Then, based on the designed NN identifier a stabilizing or reference tracking controller is proposed (BC combined with SM). The proposed neural identifier and control applicability are illustrated by torque trajectory tracking for a DC motor with separate winding excitation via real-time implementation.     

Observer‐based controller for constrained uncertain stochastic nonlinear discrete‐time systems

In this paper, an observer‐based control approach is proposed for uncertain stochastic nonlinear discrete‐time systems with input constraints. The widely used extended Kalman filter (EKF) is well known to be inadequate for estimating the states of uncertain nonlinear dynamical systems with strong nonlinearities especially if the time horizon of the estimation process is relatively long. Instead, a modified version of the EKF with improved stability and robustness is proposed for estimating the states of such systems. A constrained observer‐based controller is then developed using the state‐dependent Riccati equation approach. Rigorous analysis of the stability of the developed stochastically controlled system is presented. The developed approach is applied to control the performance of a synchronous generator connected to an infinite bus and chaos in permanent magnet synchronous motor. Simulation results of the synchronous generator show that the estimated states resulting from the proposed estimator are stable, whereas those resulting from the EKF diverge. Moreover, satisfactory performance is achieved by applying the developed observer‐based control strategy on the two practical problems. Copyright © 2015 John Wiley & Sons, Ltd.     

基于抗差扩展卡尔曼滤波器的永磁同步电机转速估计策略

通过分析粗差对扩展卡尔曼滤波器(extended Kalman filte,EKF)状态估计的影响,对无速度传感器矢量控制的永磁同步电机的转速,提出了一种基于抗差扩展卡尔曼滤波器(robust extended Kalman filter,REKF)的估计方法.建立了永磁同步电机的REKF模型,探讨了永磁同步电机在粗差干扰下引入REKF能否获得优于EKF的估计性能这一问题,比较了REKF与EKF在遇到外部粗差干扰或内部估算粗差干扰时转速和磁链的变化.仿真和实验结果表明REKF较EKF而言具有更好的抗粗差性能,使系统遇到干扰时能更快收敛.     

无传感器PMSM中基于IGSO优化EKF的速度估计方法

为了提高无传感器永磁同步电机(PMSM)控制系统中速度控制性能,提出一种基于改进群搜索优化(IGSO)算法的扩展卡尔曼滤波(EKF)速度估计方案。首先,分析了PMSM磁场定向控制(FOC)系统模型;然后,将电机的d-q轴电压、电流和转子速度作为状态变量,构建EKF中的状态方程来估计转速和负载。同时,为了提高EKF的估计性能,以估计值与实际值的平方误差积分(ISE)作为适应度函数,通过IGSO算法来优化EKF中的噪声协方差矩阵Q和R,以此获得最优参数。仿真结果表明,提出的控制系统能够精确估计出电机转速并进行有效控制。