基于EKF的永磁同步电机直接转矩控制研究

无速度传感器永磁同步电机控制中,鉴于直接计算法需要精确的数学模型、清模观测器由于开关切换动作的不连续造成的抖振问题以及智能算法特别复杂的缺点,本文对利用扩展卡尔曼滤波器(extended Kalman filter,EKF)对永磁同步电机(permanent magnet synchronous motor,PMSM)直接转矩控制(direct torque control,DTC)系统进行精确参数估计进而实现对无速度传感器永磁同步电机的控制的方法进行研究,并通过Madab/Simulink仿真实验验证了扩展卡尔曼滤波算法的有效性.     

基于EKF永磁同步电机无传感直接转矩控制研究

为了解决传统永磁同步电机直接转矩控制中磁链估算需要知道转子初始位置、纯积分导致的直流偏置以及机械传感器的引入导致系统鲁棒性降低等问题.这里提出了一种基于扩展卡尔曼滤波器的PMSM无传感控制方法.选择易检测的定子电压电流为输入输出变量,以d-q坐标系下定子磁链、转子转速和位置为状态变量,应用EKF(Extended Kalman Filter,EKF)算法来实现状态的准确估算.仿真表明,该方法克服了DTC定子磁链计算过程中的直流偏置和严格初值要求等问题;避免了直接以电机定子磁链为状态变量带来的磁链相位移动的问题和以定子电流为状态变量引起的多解问题;能对状态变量进行准确的估计,提高了PMSM-DTC系统性能,实现了一种高性能PMSM无传感控制.     

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.     

卡尔曼滤波器参数分析与应用方法研究

介绍卡尔曼滤波器及其各种衍生方法.首先给出卡尔曼滤波器的算法流程以及所有参数的含义,并对影响滤波效果的五个主要参数进行了讨论.然后通过仿真实验研究不同的参数取值对于卡尔曼滤波的影响.最后总结在不同应用场景下使用卡尔曼滤波器的宗旨和要点.     

动态环境下基于速度空间寻优的局部避障方法

在动态环境下的局部避障是移动机器人的一项基本功能.在各种速度空间方法,如曲率-速率法(CVM)、巷道-曲率法(LCM)和扇区-曲率法(BCM)的基础上,提出了一种适用于未知或部分未知动态环境的局部避障方法.该方法将碰撞预测模型与改进后的BCM有效结合,不仅兼备了CVM的平滑性、LCM的安全性和BCM快速性的优点,而且弥补了各种速度空间寻优方法的不足,使其能够适用于移动机器人在动态环境下的避障与导航.实际机器人的导航实验表明该算法是可行而有效的.     

动态迟滞Hammerstein系统的改进卡尔曼状态估计

迟滞特性具有非光滑、多值映射等复杂特性.而在实际的工程中,当输入电压变化频率超过一定的范围时,迟滞的特性是随着输入频率的改变发生变化,使得整个系统的状态估计工作更复杂.本文首先提出一种新的描述动态迟滞的方法,进而描述了动态迟滞Hammerstein系统的状态空间方程,根据此系统在传统卡尔曼滤波器的基础上进行改进得到一种新的非光滑卡尔曼滤波器.最后通过仿真和实验,比较了在输入信号变化频率比较大时,用动态迟滞Hammerstein系统来描述压电陶瓷和采用静态迟滞Hammerstein系统来描述压电陶瓷的特性,非光滑卡尔曼滤波器对这两种含有噪声的模型进行滤波,结果表明由于静态迟滞Hammerstein系统的建模不能很好的描述压电陶瓷的特性,模型存在着误差,因此对系统状态估计的结果也没有用动态Hammerstein系统的误差小,从而说明当输入电压频率变化比较大时研究动态的迟滞Hammerstein模型是很有意义的.     

基于参数调整的动态模糊神经网络算法

模糊逻辑与神经网络结合形成的模糊神经网络同时具有易于表达人类知识、存储与学习分布信息的优点,基于此,提出一种基于参数调整的动态模糊神经网络算法。采用扩展卡尔曼滤波器法将全局算法划分为线性和非线性部分,线性参数由最小二乘法和滤波器法决定,非线性参数由训练样本和启发式法直接决定,线性和非线性参数可进行实时更新。仿真结果表明,该算法能保证更简洁的结构和更短的学习时间。     

微传感器测量运动状态下的人体躯干倾角

精确地测量与控制躯干部的姿态对于人体和仿人机器人的运动学研究具有重要意义。先前的研究大多基于泛用算法,实验条件单一。基于陀螺仪和磁力计的数据融合,提出了一种用于估算运动状态下人体躯干倾角的算法。实验结果表明：在不同运动状态下,算法的均方根误差为1.81°±0.77°。该算法可以应用在有关人体和机器人运动平衡性的研究中。     

一种用于解决非线性滤波问题的新型粒子滤波算法

粒子滤波算法受到许多领域的研究人员的重视,该算法的主要思想是使用一个带有权值的粒子集合来表示系统的后验概率密度.在扩展卡尔曼滤波和Unscented卡尔曼滤波算法的基础上,该文提出一种新型粒子滤波算法.首先用Unscented卡尔曼滤波器产生系统的状态估计,然后用扩展卡尔曼滤波器重复这一过程并产生系统在k时刻的最终状态估计.在实验中,针对非线性程度不同的两种系统,分别采用5种粒子滤波算法进行实验.结果证明,文中所提出的算法的各方面性能都明显优于其他4种粒子滤波算法.     

Exact and approximate state estimation for nonlinear dynamic systems

This paper presents a modified approach to solve state estimation problems of nonlinear dynamic systems involving noise free, uncorrelated and correlated state and measurement noise processes. The basic approach makes use of the matrix minimum principle together with the Kolmogorov and Kushner's equations to minimize the error-variance, taken to be the estimation criterion. The filtering equations obtained for nonlinear systems with white noise process are exact, but for non-white noise processes the results obtained are approximate.

For systems with polynomial or product types non-linearities, the proposed algorithms can be evaluated without the need of approximation under the assumption that the estimator errors are Gaussian. Such an assumption is significantly different from the most commonly used assumption that the state is Gaussian. Simulation results obtained from the proposed filtering algorithms are compared to various other approximate nonlinear filters. The results indicate the superiority of the proposed filter over those of other filters investigated.     

Parameter and state estimation of kinetic and nonlinear dynamic systems

This paper presents a theoretical method for characterizing kinetic and dynamic systems. In this method the time courses of a system variables are approximated by linear combination of Legendre polynomials, and the least-squares criterion is used to find out the unknmown time courses and system parameters. The theory has been implemented by an algorithm which is described and its utility is illustrated by application to the kinetic system P + Q ⇄ PQ → Q + R.     

On the estimation of state variables and parameters for noisy dynamic systems

The problem of estimating state variables and parameters is considered for discrete-time systems in the presence of random disturbances and measurement noise. The solution of the linear problem is given and an approximation technique is developed for nonlinear systems. A dynamic programming formulation of the estimation problem is also developed.     

Linearization and state estimation of unknown discrete-timenonlinear dynamic systems using recurrent neurofuzzy networks

Model-based methods for the state estimation and control of linear systems have been well developed and widely applied. In practice, the underlying systems are often unknown and nonlinear. Therefore, data based model identification and associated linearization techniques are very important. Local linearization and feedback linearization have drawn considerable attention in recent years. In this paper, linearization techniques using neural networks are reviewed, together with theoretical difficulties associated with the application of feedback linearization. A recurrent neurofuzzy network with an analysis of variance (ANOVA) decomposition structure and its learning algorithm are proposed for linearizing unknown discrete-time nonlinear dynamic systems. It can be viewed as a method for approximate feedback linearization, as such it enlarges the class of nonlinear systems that can be feedback linearized using neural networks. Applications of this new method to state estimation are investigated with realistic simulation examples, which shows that the new method has useful practical properties such as model parametric parsimony and learning convergence, and is effective in dealing with complex unknown nonlinear systems.     

Active state estimation of nonlinear systems

In this paper, state observers for control systems with nonlinear outputs are studied. For such systems, the observability does not only depend on the initial conditions, but also on the exciting control used. Thus, for such systems, design of active control is an integral part of the design for state observers. Here some sufficient conditions are given for the convergence of an observer. It is also discussed, via a camera example, how to actively excite a system in order to improve the observability.     

Comments on: “Exact and approximate state estimation for nonlinear dynamic systems”

After deriving the realizable, nonlinear filtering algorithm for dynamic systems involving white and non-white processes, the above paper (Liang and Christensen, 1975) extended the algorithm to dynamic systems having noise-free observation. However, the resulted nonlinear noise-free filtering is not correct since the paper overlooked the Itô stochastic calculus to differentiate noise-free nonlinear measurements. Here we show the correct extension.     

Decentralized state estimation in large-scale systems

A novel approach to decentralized state estimation in a large-scale interconnected system is proposed. The method assumes a known model for the local subsystem only, and therefore is suitable when the other subsystem models and the interaction matrices are partially or totally unknown. An innovation representation suitable for decentralized subsystem state estimation is derived. The state estimation problem is then solved through the parametric identification of the innovation representation. The identification algorithm is based upon a pseudo-linear regression (PLR) principle that attempts minimization of the innovation variances.     

Near-optimal state estimation for interconnected systems

A new algorithm is proposed for estimating the state of a nonlinear stochastic system when only noisy observations of the state are available. The state estimation problem is formulated as a modal-trajectory, maximum likelihood estimation problem. The resulting minimization problem is analogous to the nonlinear tracking problem in optimal control theory. By viewing the system as an interconnection of lower-dimension subsystems and applying the so-called ε-coupling technqiue, which originated in the study of sensitivity of control systems to parameter variations, a near-optimal state estimation algorithm is derived which has the properties that all computations can be performed in parallel at the subsystem level and only linear equations need be solved. The principal attraction of the method is that significant reductions in the computational requirements relative to other approximate algorithms can be achieved when the system is large-dimensional.     

Moving-horizon partition-based state estimation of large-scale systems

This paper presents three novel moving-horizon estimation (MHE) methods for discrete-time partitioned linear systems, i.e., systems decomposed into coupled subsystems with non-overlapping states. The MHE approach is used due to its capability of exploiting physical constraints on states and noise in the estimation process. In the proposed algorithms, each subsystem solves reduced-order MHE problems to estimate its own state and different estimators have different computational complexity, accuracy and transmission requirements among subsystems. In all cases, proper tuning of the design parameters, i.e., the penalties on the states at the beginning of the estimation horizon, guarantees convergence of the estimation error to zero. Numerical simulations demonstrate the viability of the approach.