A review on MRAS-type speed estimators for reliable and efficient induction motor drives

Induction machines have recently been very popular in variable-speed drives, because of their robust construction and relatively low manufacturing costs (brushless), maintenance-free and well-matured control methods. However, for high-precision control and efficiency optimization one needs the information on the rotor speed which can be measured using different speed sensors. All sensors require a mounting space and cabling, they also generate extra costs and reduce system reliability. Therefore, many of the recent research efforts have been dedicated to sensorless or encoderless electrical drives offering such considerable advantages as: lower cost, reduced size and hardware complexity of the drive system, elimination of sensor cables, lower maintenance requirements, possible operation in aggressive environment, higher noise immunity, reliable and user friendly operation. In this article all well-known sensorless techniques are shortly addressed, but the main focus is on the solutions based on the Model Reference Adaptive System (MRAS) concept. The mathematical models and schemes of all types of MRAS-type speed estimators known from the literature are gathered in this article. The comparative analysis of these speed estimators is done from the following points of view: the speed adaptation mechanism derivation based on the Lyapunov theory, stability problems near zero speed and in the regenerating operation mode, and the sensitivity of MRAS estimators to induction machine parameter changes.     

Robustness of Zero Crossing Estimator

Zero crossing (ZC) statistic is the number of zero crossings observed in a time series. The expected value of the ZC specifies the first‐order autocorrelation of the processes. Hence, we can estimate the autocorrelation by using the ZC estimator. The asymptotic consistency and normality of the ZC estimator for scalar Gaussian processes are already discussed in 1980. In this article, first, we derive the joint asymptotic distribution of the ZC estimator for ellipsoidal processes. Next, we show the variance of the ZC estimator does not attain the Cramer–Rao lower bound (CRLB). However, it is shown that the ZC estimator has robustness when the process is contaminated by an outlier. In contrast with this, we observe that the quasi‐maximum likelihood estimator (QMLE) attains the CRLB. However, we can see that QMLE is sensitive for the outlier.     

Adaptive force–environment estimator for manipulators based on adaptive wavelet neural network

This study focuses on the accurate tracking control and sensorless estimation of external force disturbances on robot manipulators. The proposed approach is based on an adaptive Wavelet Neural Network (WNN), named Adaptive Force-Environment Estimator (WNN-AFEE). Unlike disturbance observers, WNN_AFEE does not require the inverse of the Jacobian transpose for computing the force, thus, it has no computational problem near singular points. In this scheme, WNN estimates the external force disturbance to attenuate its effects on the control system performance by estimating the environment model. A Lyapunov based design is presented to determine adaptive laws for tuning WNN parameters. Another advantage of the proposed approach is that it can estimate the force even when there are some parametric uncertainties in the robot model, because an additional adaptive law is designed to estimate the robot parameters. In a theorem, the stability of the closed loop system is proved and a general condition is presented for identifying the force and robot parameters. Some suggestions are provided for improving the estimation and control performance. Then, a WNN-AFEE is designed for a planar manipulator as an example, and some simulations are performed for different conditions. WNN_AFEE results are compared attentively with the results of an adaptive force estimator and a disturbance estimator. These comparisons show the efficiency of the proposed controller in dealing with different conditions.     

工料测量师在英国工程造价中的地位与作用

介绍了学习英国工程项目管理的意义和工料测量师的概念,从受雇于业主的工料测量师和受聘于承包商的估价师两方面,分析了他们参与的工程造价管理程序,有利于借鉴和吸收国外工程造价业管理的经验和做法。     

贝叶斯估计器先验模型参数的迭代感知方法

充分利用先验信息是提高统计推断性能的有效途径之一.贝叶斯估计的先验信息模型参数必须在设计阶段确定下来,与待探测环境模型参数之间必然存在不一致性,从而有可能导致估计质量的下降.该文首先给出了基于估计性能的先验模型参数感知的一般性框架.基于该框架,针对白高斯噪声中直流信号的贝叶斯估计器,分析了先验失配条件下的估计性能,给出了一种先验模型参数迭代感知的算法.利用计算机仿真分析了该估计器性能对先验模型参数的敏感性和稳健性,分析了不同条件下的迭代感知过程.计算机仿真结果表明,该文给出的迭代感知方法建立了从估计性能到先验模型参数的反馈,通过估计器与待探测场景的多次交互,可以使得先验模型与当前场景模型匹配.     

An Improved Maximum Exponentially Weighted Moving Average Control Chart for Monitoring Process Mean and Variability

Maximum exponentially weighted moving average (MaxEWMA) control charts have gained considerable attention for detecting changes in both process mean and process variability. In this paper, we propose an improved MaxEWMA control charts based on ordered ranked set sampling (ORSS) and ordered imperfect ranked set sampling (OIRSS) schemes for simultaneous detection of both increases and decreases in the process mean and/or variability, named MaxEWMA‐ORSS and MaxEWMA‐OIRSS control charts. These MaxEWMA control charts are based on the best linear unbiased estimators of location and scale parameters obtained under ORSS and OIRSS methods. Extensive Monte Carlo simulations have been used to estimate the average run length and standard deviation of run length of the proposed MaxEWMA control charts. These control charts are compared with their counterparts based on simple random sampling (SRS), that is, MaxEWMA‐SRS and MaxGWMA‐SRS control charts. The proposed MaxEWMA‐ORSS and MaxEWMA‐OIRSS control charts are able to perform better than the MaxEWMA‐SRS and MaxGWMA‐SRS control charts for detecting shifts in the process mean and dispersion. An application to real data is provided to illustrate the implementation of the proposed MaxEWMA control charts. Copyright © 2013 John Wiley & Sons, Ltd.     

Robust distributed attitude synchronization of multiple three-DOF experimental helicopters

Multiple experimental three-degrees-of-freedom (three-DOF) helicopters that are equipped with active disturbance systems constitute an attractive platform to validate robust control strategies. In this paper, a distributed synchronization controller is developed for such a platform, where each helicopter is subjected to unknown model uncertainties and external disturbances, and the desired trajectories are generated online, communicated through a network and not accessible by all helicopters. The controller is composed of a continuous tracker and a continuous uncertainty and disturbance estimator (UDE). The tracker makes the nominal closed-loop system globally asymptotically stable, and the UDE output is used to reject total uncertainties. The conditions that ensure zero-error tracking for each helicopter are identified; for the case with nonzero error, explicit relationship inequalities between the involved design parameters and the ultimate bound of error are revealed. Experimental results of four cases demonstrate improved tracking and synchronization accuracy of using the UDE with small parameters.     

Optimal control of uncertain quantized linear discrete‐time systems

In this paper, the adaptive optimal regulator design for unknown quantized linear discrete‐time control systems over fixed finite time is introduced. First, to mitigate the quantization error from input and state quantization, dynamic quantizer with time‐varying step‐size is utilized wherein it is shown that the quantization error will decrease overtime thus overcoming the drawback of the traditional uniform quantizer. Next, to relax the knowledge of system dynamics and achieve optimality, the adaptive dynamic programming methodology is adopted under Bellman's principle by using quantized state and input vector. Because of the time‐dependency nature of finite horizon, an adaptive online estimator, which learns a newly defined time‐varying action‐dependent value function, is updated at each time step so that policy and/or value iterations are not needed. Further, an additional error term corresponding to the terminal constraint is defined and minimized along the system trajectory. The proposed design scheme yields a forward‐in‐time and online scheme, which enjoys great practical merits. Lyapunov analysis is used to show the boundedness of the closed‐loop system; whereas when the time horizon is stretched to infinity as in the case of infinite horizon, asymptotic stability of the closed‐loop system is demonstrated. Simulation results on a benchmarking batch reactor system are included to verify the theoretical claims. The net result is the design of the optimal adaptive controller for uncertain quantized linear discrete‐time systems in a forward‐in‐time manner. Copyright © 2014 John Wiley & Sons, Ltd.     

Two guaranteed equilibrated error estimators for Harmonic formulations in eddy current problems

In this paper a guaranteed equilibrated error estimator is developed for the 3D harmonic magnetodynamic problem of Maxwell’s system. This system is recasted in the classical $\mathbf{A}?\phi$ potential formulation and solved by the Finite Element method. The error estimator is built starting from the $\mathbf{A}?\phi$ numerical solution by a local flux reconstruction technique. Its equivalence with the error in the energy norm is established. A comparison of this estimator with an equilibrated error estimator already developed through a complementary problem points out the advantages and drawbacks of these two estimators. In particular, an analytical benchmark test illustrates the obtained theoretical results and a physical benchmark test shows the efficiency of these two estimators.     

Limit behaviors of extended Kalman filter as a parameter estimator for a sinusoidal signal

In this note, the basic limit behaviors of the solution to Riccati equation in the extended Kalman filter as a parameter estimator for a sinusoidal signal are analytically investigated by using lim sup and lim inf in advanced calculus. We show that if the covariance matrix has a limit, then it must be a zero matrix.