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When adequately maintained, the mechanical system of rewinders and similar machines in paper industry may have a significantly longer service life than its electrical and control systems. Over the lifetime of the facility, a rewinder may experience several major maintenance cycles, and these generally include significant upgrades to the electrical drive and control system. These upgrades present an opportunity to modify the design of the control system to eliminate some of the sensors or to provide signal redundancy by using state estimation. During such major reconstruction of a paper-board rewinder, it was noticed that the shipping-roll radius and the web tension force can be accurately estimated from the measurements available within the drive's control system. This paper presents the implementation of the two estimators on a commercial production rewinder. Test results from the equipment in service show that the measured signals can be supplemented with the estimated ones to provide an alternative in the event of sensor malfunction or to enable trending of accuracy of calibration. Both applications have an entitlement to reduce the downtime of the rewinder, as the replacement of the faulted sensor can be scheduled for the next planned idle period of the machine. [ABSTRACT FROM AUTHOR]

The reliable operation of battery management systems depends critically on the accurate estimation of the state of charge (SOC) and characterizing parameters of a battery system. SOC estimation employs models that must be robust against variations in battery cell electrochemical features, aging, and operating conditions. This paper reveals that commonly used SOC estimation schemes are fundamentally flawed in providing the robustness of SOC estimation against model uncertainties. Parameter estimation methodologies and adaptive SOC estimation design are introduced in this paper to enhance SOC estimation accuracy and robustness. By a scrutiny of the impact of parameter variations on SOC estimation accuracy, the SOC–open-circuit-voltage mapping is identified to be the most critical function that must be accurately established. Identification algorithms are introduced, and their convergence properties are established. The integration of the identification algorithms and SOC estimation schemes lead to an adaptive SOC estimation framework that is superior over the existing methods in providing much improved accuracy and robustness. Experimental studies are conducted to validate the algorithms. [ABSTRACT FROM PUBLISHER]

An introduction is presented in which the editor discusses various reports within the issue on industrial electronics topics including induction machines, synchronous machines, and power components and power converters.

In this paper, a three-dimensional (3-D) motion tracking method is proposed for binocular microscopic vision system to precisely record the motion trajectories of millimeter size objects in the Cartesian space. Primarily two fundamental problems are solved. The first problem arises from the limited depth of field (DOF) of microscope. Considering the motion of the objects, the existing autofocusing methods requiring sequential images either in focus or defocus are not workable. Therefore, a one-shot prior autofocusing approach is desired, which needs to take the motion tendency of objects into account. Besides, the autofocusing process always lags behind the motion of objects, and there inevitably will be prediction deviation on the motion tendency of objects. This leads to the second problem to estimate the 3-D motion states from defocused images. In this paper, we first explain the defocusing process from the perspective of S-Transform, based on which the Bayesian inference inspired method to estimate the depth from defocus from one single image is derived thereafter. Afterwards, a motion states, including both the position and velocity, estimation approach is developed within the Kalman filter framework. The above two aspects mutually supply the necessary information for each other to be functional to accurately realize the DOF tracking and motion tracking of moving objects. Experiments were well-designed to validate the effectiveness of the proposed method, and experiments result showed a tracking precision of 3 μm was achieved. [ABSTRACT FROM AUTHOR]

This paper presents data-driven approaches to improve the active power output of wind turbines based on estimating their health condition. The main procedure includes estimations of fault degree and health condition level, and optimal power dispatch control. The proposed method can adjust the active power output of individual turbines according to their health condition and can thus optimize the total energy output of a wind farm. In the paper, extreme learning machine algorithm and Bonferroni interval are applied to estimate fault degree, while an analytic hierarchy process is used to estimate the health condition level. A scheme for power dispatch control is formulated based on the estimated health condition. Models have been identified from supervisory control and data acquisition data acquired from an operational wind farm, which contains temperature data of gearbox bearing and generator winding. The results show that the proposed method can maximize the operation efficiency of the wind farm while significantly reducing the fatigue loading on the faulty wind turbines. [ABSTRACT FROM AUTHOR]

Air pollutants do much harm to human safety. In particular, the fine particulate matter (PM2.5), a complex air pollutant which is composed of the particles beneath the aerodynamic diameters of 2.5 $\mu$ m, very possibly causes severe diseases since it is easy to intrude into the lungs. To that end, in this paper, we design a new picture-based predictor of PM2.5 concentration (PPPC), which employs the pictures acquired using mobile phones or cameras to make a real-time estimation of PM2.5 concentration. First, using a large body of pictures which were captured under the good weather conditions, i.e., very low PM2.5 concentration, naturalness statistics (NS) models are built upon entropy features in spatial and transform domains. Second, for a novel picture, we measure its deviation degree from the above-mentioned NS models, considering the fact that the naturalness of a picture tends to reduce with the PM2.5 concentration increased. Third, a simple nonlinear function is introduced to map the deviation degree to the PM2.5 concentration. In comparison to existing relevant state-of-the-art predictors, sufficient experimental results manifest the superiority of the proposed PPPC model in terms of prediction accuracy and implementation efficiency. [ABSTRACT FROM AUTHOR]

The ability to monitor the quality of the metal forming process as well as the machine's condition is of significant importance in modern industrial processes. In the case where a physical device (i.e., sensor) cannot be deployed due to the characteristics of the system, models that rely on the estimation of both the applied force and the dynamic behavior of the machine (i.e., system) are adopted. The development of such models and the corresponding algorithms used to estimate the above-mentioned quantities has attracted the interest of the community. The main contribution of this paper is the estimation of a servo press force by employing a novel dual particle filter based algorithm, achieving a maximum relative error in the force estimation of 3.6%. Moreover, to address real-time performance requirements, this paper proposes a field programmable gate array based accelerator that improves the sampling rate by a factor of 200 compared to a processor-based solution, thus enabling the deployment of the system in many realistic scenarios. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive study on current loop auto-tuning for self-commissioning of sensorless interior permanent magnet synchronous machine (IPMSM) drives. Well-tuned current controllers are essential for both high-performance operation and self-commissioning procedure. However, current controller design parameters, i.e., $L_{d}$ and $L_{q}$ , are not known at the beginning of the self-commissioning procedure where their estimation also has unique challenges. For example, an initial rotor position to determine the actual d- and q-axes is not known. Also, feedback controllers cannot be used in the estimation because they are not tuned yet. As a solution, this paper proposes a method to estimate the spatial inductance map by spatially scanning the motor via sinusoidal voltage injection in a controlled manner. Then it uses the estimated inductance values to tune the current controllers. The proposed method identifies $L_{d}$ and $L_{q}$ values using open-loop voltage injection without needing actual d- and q-axis positions. The whole procedure takes around a second. Practical considerations such as an automatic selection of the injection voltage, and digital control and dead-time effects are carefully addressed. The findings are experimentally verified on a 3-phase IPMSM drive. Furthermore, its applicability to different motor types is also demonstrated. [ABSTRACT FROM AUTHOR]

Supercapacitors are receiving significant interest in wireless sensor network applications due to their high power density and longer lifespan. In such applications, consideration of the leakage effect is crucial for online control and management of supercapacitors. Motivated by this fact, in this paper we propose an online scheme to estimate the state-of-charge (SOC) of supercapacitors, taking the leakage effect into account. First, we propose a completely observable equivalent circuit model (ECM) of a supercapacitor, which captures the leakage effect. Then, we design an unscented-Kalman-filter-based SOC estimator on the proposed ECM. To illustrate the proposed scheme, we consider a commercially available 5 F supercapacitor (Maxwell BCAP0005) and conduct experimental studies to identify the ECM parameters. We also perform simulation and experimental studies to test the effectiveness of the proposed estimation scheme, in this paper. Finally, we evaluate the robustness of the proposed scheme under parametric and measurement uncertainties. [ABSTRACT FROM AUTHOR]

This paper improves the position sensorless drive by sensing actual machine phase voltages. A high-bandwidth phase voltage measurement is developed for pulsewidth modulation (PWM) voltage inverters. On the basis, the actual phase voltage is obtained based on the digital integration of PWM voltage using the capture modulator in existing drive microcontrollers (MCU's). Comparing to existing phase voltage measurement, no separated A/D converter and communication hardware are required because PWM pulses are directly measured using MCUs. However, for standard machines without neutral points, only line-to-line ac PWM voltages can be measured for the phase voltage reconstruction. Since the capture based on transistor-transistor logic (TTL) logics receives only digital signals, a preprocess circuit to convert ac PWM line voltages to equivalent digital signals is proposed. This paper clearly explains the voltage sensing hardware using MCU capture modulator. According to experimental results, a 150-MHz sampling rate for phase voltage measurement is achieved based on the proposed capture-based voltage measurement. Although the physical limitation of back electromotive force estimation still appears, the proposed phase voltage measurement substantially enhances the sensorless drive performance at low speed. [ABSTRACT FROM AUTHOR]

This paper studies a single beacon-based three-dimensional multirobot localization (MRL) problem. Unlike most of existing localization algorithms which use extended Kalman filter or maximum a posteriori, moving horizon estimation (MHE), and convex optimization are novelly designed to perform MRL with constraints and unknown initial poses. The main contribution of this paper is three-fold: 1) a constrained MHE-based localization algorithm, which can bound localization error, impose various constraints and compromise between computational complexity and estimator accuracy, is proposed to estimate robot poses; 2) constrained optimization is examined in the perspective of Fisher information matrix to analyze why and how multirobot information and constraints are able to reduce uncertainties; 3) a semidefinite programming-based initial pose estimation, which can efficiently converge to global optimum, is developed by using convex relaxation. Simulations and experiments are conducted to verify the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

In this paper, the use of the Dual Kalman Filter for the identification of photovoltaic system parameters is presented. The system includes the photovoltaic source, the dc/dc converter and the battery/dc bus and both its states and parameters in the actual operating conditions are identified. In particular, the proposed approach gives the confidence interval for the system settling time, which is used for the real-time optimization of the perturbative maximum power point tracking algorithm. The proposed technique is implemented by using a Field-Programmable Gate Array and it is validated by means of both simulation and experimental results. [ABSTRACT FROM PUBLISHER]

Chaotic sinusoidal pulse width modulation (SPWM) can be used to suppress the electromagnetic interference in power converters. In this paper, a mathematical method for estimating power losses in insulated-gate bipolar transistors and freewheeling diodes occurring under chaotic frequency modulation SPWM (CFM-SPWM) switching is proposed. This method is based on the features of the chaotic mapping and uses semiconductor datasheet information to calculate the power losses in a discrete way. Analytical expressions and approximations for estimating the losses for the cases of SPWM and CFM-SPWM switching, respectively, are presented. This paper includes a quantitative comparison of the losses under the two previously mentioned cases. It is confirmed that CFM-SPWM does not result in higher power losses when it greatly improves electromagnetic compatibility of power converters, and hence, its adoption will not require any modified converter thermal design. Experimental results taken from a single-phase ac–dc converter are presented to prove the validity of the proposed method and associated results. [ABSTRACT FROM PUBLISHER]

This paper focuses on the fault-tolerant control problem for an Internet-based three-tank system in the presence of possible sensor bias faults. The Internet-based three-tank system is an experimental setup that can be regarded as a typical networked system for evaluating networked fault-diagnosis and fault-tolerant control methods. Packet dropout phenomenon in the sensor-to-controller link is considered in this paper, and the fault type we deal with is chosen as the sensor bias fault. Fault-diagnosis unit is designed toward an auxiliary system. Sensor bias faults can be detected by comparing the residual signal generated by the fault detection filter and a prescribed threshold. After that, the fault can be isolated by using the residual analysis approach. Once the fault is isolated, it can be estimated iteratively in the least-squares sense. A fault accommodation method is proposed, and a fault-tolerant control strategy is achieved based on the fault information provided by the fault-diagnosis unit. The approach brought forward in this paper is demonstrated via an experimental study on the practical Internet-based three-tank system. Results show the effectiveness and the applicability of the proposed techniques. [ABSTRACT FROM PUBLISHER]

This paper presents a novel method to estimate the number of shorted turns in a permanent magnet synchronous machine (PMSM) following the detection of interturn short-circuit (ITSC) fault and its location. In this proposed method, PMSM is excited through a low sinusoidal voltage at standstill condition to obtain the winding resistance and synchronous inductance by current response. It is shown that the ITSC fault introduces variation in the current response, which can be used to calculate the number of shorted turns under zero fault resistance assumption. Using this practical procedure, the fault severity can be estimated directly in a straight-forward manner. In other words, the severity estimation for a given machine can be done without complex machine modeling or experiments on ITSC prototype with multiple taps. The findings in this paper are essential for a comprehensive solution including fault mitigation algorithms and postfault operations. In order to verify the findings, a three-phase equivalent circuit model supported by finite element analysis results is used to take saturation and space harmonics into account. In addition, experimental results are presented to demonstrate the validity and practicability of the severity estimation. [ABSTRACT FROM AUTHOR]

Partial discharge (PD) is the manifestation of insulation degradation, and it is regarded as a great risk in the field of industrial high-voltage insulation system. In this paper, the direction-of-arrival (DOA) estimation algorithm is used for the localization of partial-discharge source. The intersecting point of two rays, which come from two different directions measured by two uniform linear arrays (ULAs), can be regarded as the plane location of partial-discharge source. Fast rise times of partial-discharge pulses result in a wide electromagnetic spectrum, thus this paper focuses on the DOA estimation of wideband signals. The technique of multimodal joint sparse representation (MJSR) has been used to improve the performance of biometrics recognition. Here, we extend it to DOA estimation. The received data are represented by a sparse linear combination of potential steering vectors, while constraining the observations from different frequencies subject to sharing the same sparsity pattern. Based on the simultaneous orthogonal matching pursuit (SOMP) and alternating direction method of multipliers (ADMM), a hard threshold algorithm is proposed for DOA estimation. In order to reduce the computational complexity of hard threshold algorithm, an improved soft threshold DOA estimation algorithm based on the ADMM is proposed as well. The effectiveness of the proposed algorithms is validated by simulations and real tests. [ABSTRACT FROM AUTHOR]

This paper proposes an online parameter estimation method based on a discrete-time dynamic model for the interior permanent-magnet synchronous motors (IPMSMs). The proposed estimation technique, which takes advantage of the difference in dynamics of motor parameters, consists of two affine projection algorithms. The first one is designed to accurately estimate the stator inductances, whereas the second one is designed to precisely estimate the stator resistance, rotor flux linkage, and load torque. In this paper, the adaptive decoupling proportional–integral (PI) controllers with the maximum torque per ampere operation, which utilize the previously identified parameters in real time, are chosen to verify the effectiveness of the proposed parameter estimation scheme. The simulation results via MATLAB/Simulink and the experimental results via a prototype IPMSM drive system with a TI TMS320F28335 DSP are presented under various conditions. A comparative study with the conventional decoupling PI control method is carried out to demonstrate the better performances (i.e., faster dynamic response, less steady-state error, more robustness, etc.) of the adaptive decoupling PI control scheme based on the proposed online parameter estimation technique. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel fuzzified mechanical sensorless speed estimation technique for permanent magnet direct current (PMDC) brushed motor operated with pulse width modulation (PWM) based terminal voltage. For replacement of conventional mechanical speed sensors, few mechanical sensorless speed estimation techniques have been reported recently in the field of PMDC brushed motor. In this paper, a novel technique is applied over a recently reported semianalytical dynamic time-domain PMDC brushed motor model incorporating space-domain effects, namely slotting effect and commutation phenomenon to simulate the proposed estimation process. Ripple and back electromotive force based standalone estimation approaches are hybridized in order to eliminate uncertainties in all regions of estimation. In this paper, a discrete fuzzified hybrid speed estimation model has been proposed with adequate dynamic simulation responses. Further the proposed discrete speed estimation model has been applied experimentally over a PWM-driven 24-V, 12-teeth, 100-W PMDC brushed motor and various dynamic real-time responses have been compared with conventionally captured speed samples to validate the proposed model. [ABSTRACT FROM PUBLISHER]

An important bottleneck in the design, operation, and exploitation of mechatronic powertrains is the lack of accurate knowledge of broadband external loading. This is caused by the intrusive nature of regular torque measurements. This paper proposes a novel nonintrusive approach to obtain torsional load information on mechatronic powertrains. Online coupled state/input estimation is performed through an augmented nonlinear Kalman filter. This estimation approach exploits general lumped-parameter physics-based models in order to create a widely applicable framework. This paper considers both extended (EKF) and unscented Kalman filtering approaches. Contrary to previous works, no considerable difference in accuracy is obtained from experiments, with a considerably lower computational load for the EKF. This paper reveals the benefits of including rotational acceleration measurements from a theoretical perspective, which is demonstrated through the experimental validation. This drastically increases the broadband accuracy. The result of this paper is an accurate and noninvasive virtual torque sensor with a sufficiently broad bandwidth for use in condition monitoring, control, and future design optimization. [ABSTRACT FROM PUBLISHER]

Bilateral control can obtain human motions based on the velocity and force information. Based on the obtained data of human motions, it is possible to reproduce them by position–force hybrid control. The possible applications of the position–force hybrid control can be in the training of experts, rehabilitation, and so on. In the position–force hybrid control, a more adaptive and flexible property is desired since the reproduced motion is strictly affected by the environmental situation in the saved phase. Thus, conventional position–force hybrid control is not sufficient for the training purpose because the reproduced motion is inflexible and its performance is limited. This paper shows a mixed reproduction of physical human operation and data-based motions. In the method, the indexes for human interaction under the automatic reproduction of a saved motion are introduced. The human operationality and the data-based motion reproducibility are stabilized based on frequency filtering. In addition, some comparisons between the conventional method and the proposed method are shown by performance and stability analyses. From these analyses, the effectiveness of the proposed method is confirmed. [ABSTRACT FROM PUBLISHER]

This paper proposes a new localization method for mobile robots based on received signal strength (RSS) in indoor wireless local area networks (WLANs). In indoor wireless networks, propagation conditions are very difficult to predict due to interference, reflection, and fading effects. As a result, an explicit measurement equation is not available. In this paper, an observation likelihood model is accomplished using kernel density estimation to characterize the dependence of location and RSS. Based on the measured RSS, the robot's location is dynamically estimated using the proposed adaptive local search particle filter (ALSPF), which adopts the covariance adaptation for correcting the system states and updating the motion uncertainty. To deal with low sensor density in large-space environments, we present a strategy based on the strongest signal with minimum variance to choose a subset of detectable access points (APs) for enhancing robot localization and reducing the computational burden. The proposed approaches are verified by realistic low-density WLAN APs to demonstrate the feasibility and suitability. Experimental results indicate that the proposed ALSPF provides approximately 1-m error and significant improvements over particle filtering. [ABSTRACT FROM PUBLISHER]

Owing to the merits of smaller voltage ripples, lower armature current and cost compared to full-control (FC), the angular position semicontrol (APSC) scheme is proposed in this paper for low-speed range of the doubly salient electromagnetic generator (DSEG) operating over wide speed range, which improve the output voltage without increasing the field current. In APSC, the energy is stored with positive current in the inductance rising area and negative current in the inductance falling area, by which the output power and voltage increase, and the excitation current reduces. Moreover, in order to improve the poor dynamic performance resulting from the large-storage capacitor required for APSC, first, an ideal output voltage model is established according to the ampere–second balance of the energy storage capacitor on the load side. Second, several correction methods are investigated to enhance the accuracy of the ideal model. Finally, based on the conduction angle estimation model, a novel output voltage control strategy is put forward. The feasibility and superior dynamic performance of the proposed strategy are validated by the experiment results. [ABSTRACT FROM AUTHOR]

In past years, LLC resonant converters have become a mainstream topology for dc–dc power conversion due to their advantages, such as the superior efficiency obtained with the soft switching of MOSFETs. In order to further improve the efficiency of the converter, synchronous rectification (SR) can be implemented as an alternative for diode rectification. As a result, the vast majority of the literature related to this field of study presents different LLC SR control algorithms, which aim to improve the operation of the rectification. Unlike prior work on SR controllers, this paper contributes to the area of power loss estimation using rectifier current equations (RCE). The developed method based on time-domain analysis of SR currents provides a new analytical framework to characterize the behavior of SR. Implications in SR power losses of different time delays are investigated using the developed loss estimation method. In addition, different converter design parameters, such as choice of inductance ratio, can be analyzed. The RCE captures the true discontinuous and complex behavior of SR, which is often oversimplified by the conventional first-harmonic approximation (FHA). As a result, the proposed method facilitates the design of LLC resonant converters and provides increased precision in SR power loss estimation when compared to FHA, and in a considerably faster fashion when compared with precise yet computationally intensive simulation software. This paper is validated with simulation and experimental results. [ABSTRACT FROM AUTHOR]

Sudden start of an IM load and frequent change of nonlinear load in a standalone distributed generation system (DGS) cause the dip in ac voltage and frequency. Moreover, these loads distort DGS currents. Therefore, in this paper, an improved-reweighted zero-attracting quaternion-valued least mean square (I-RZA-LMS)-algorithm-based voltage source converter (VSG) control is proposed for voltage and frequency regulation, and power quality improvement in DGS. Moreover, the control algorithm of dc–dc bidirectional converter (BDC) is used for dc link voltage regulation and MPPT of the solar photovoltaic array at IM starting and nonlinear loading. In this DGS, the I-RZA-LMS-based control algorithm estimates the active and reactive component currents of distorted load currents for effective harmonics mitigation, reactive power compensation, and point of common coupling voltages regulation. The proposed control algorithm rejects the dc-offset component from load currents and gives the fundamental load component. The dc link voltage of VSC is regulated using BDC with a solar feedforward term, which is used to enhance the capability of dc link voltage control under solar power variation, IM starting, and unbalanced loading. The effectiveness of the proposed DGS is validated through the simulation as well as experimental results obtained on a developed prototype of DGS. [ABSTRACT FROM AUTHOR]

The small-signal model of the incremental optical encoder introduced in this paper provides an insight on the impact of this sensor in the dynamics of the motion control loop of a motor drive. The model is derived and validated for the most commonly employed speed estimation methods: the pulse count and the elapsed time methods. Using the model, the reduction of the phase margin due to the encoder phase lag can be quantified at an early design stage. This model facilitates the design of control techniques to compensate for the phase margin reduction due to the associated feedback delays. [ABSTRACT FROM AUTHOR]

Accurate information of battery internal variables is crucial for health-conscious and optimal battery management. Due to lack of measurements, advanced battery management systems rely heavily on estimation algorithms that provide such internal information. Although algorithms for cell-level charge and health estimation have been widely explored in the literature, algorithms for electrode-level quantities are almost nonexistent. The main obstacle in electrode-level estimation is the observability problem where the individual electrode states are not observable from terminal voltage output. However, if available, real-time feedback of electrode-level charge and health can be highly beneficial in maximizing energy utilization and battery life. Motivated by this scenario, in this paper we propose a real-time algorithm that estimates the available charge and health of individual electrodes. We circumvent the aforementioned observability problem by proposing an uncertain model-based cascaded estimation framework. The design and analysis of the proposed scheme are aided by a combination of Lyapunov's stability theory, adaptive observer theory, and interconnected systems theory. Finally, we illustrate the effectiveness of the estimation scheme by performing extensive simulation and experimental studies. [ABSTRACT FROM AUTHOR]

The screen content (SC) pictures, such as webpages, serve as a visible and convenient medium to well-represent the Internet information, and therefore, the visual quality of SC pictures is highly significant and has attained a growing amount of attention. Accurate quality evaluation of SC pictures not only provides the fidelity of the conveyed information, but also contributes to the improvement of the user experience. In practical applications, a reliable estimation of SC pictures plays a considerably critical role for the optimization of the processing systems as the guidance. Based on these motivations, this paper proposes a novel method for precisely assessing the quality of SC pictures using very sparse reference information. Specifically, the proposed quality method separately extracts the macroscopic and microscopic structures, followed by comparing the differences of macroscopic and microscopic features between a pristine SC picture and its corrupted version to infer the overall quality score. By studying the feature histogram for dimensionality reduction, the proposed method merely requires two features as the reference information that can be exactly embedded in the file header with very few bits. Experiments manifest the superiority of our algorithm as compared with state-of-the-art relevant quality metrics when applied to the visual quality evaluation of SC pictures. [ABSTRACT FROM AUTHOR]

In this paper, a threshold signal detector is proposed to improve the state estimation accuracy of an extended Kalman filter (EKF) and is validated experimentally with a microelectromechanical system electrostatic microscanner. A first-order derivative of Gaussian filter is used to detect and locate rapid changes in voltage signal caused by crossing of a threshold angle determined by maximum overlap of capacitive electrodes. The event-triggered measurement is used in the update step of the EKF to provide intermittent but more accurate angle measurements than those of the capacitive sensor's continuous output. Experiments on the electrostatic microscanner show that with the threshold signal detector incorporated; the average position estimation accuracy of the EKF is improved by 15.1% with largest improvement (30.3%) seen under low signal-to-noise ratio conditions. A parametric study is conducted to examine sampling frequency and capacitance profile, among other factors that may affect detection error and EKF accuracy. [ABSTRACT FROM AUTHOR]

The Kalman filter (KF) algorithms based on traditional models, which, applied in real-time detection of grid voltage, have the margin to improve tracking accuracy. Their tracking models do not specify the covariance matrix of state noise in theoretical derivation. They can only be taken as a unit matrix. In this paper, a dynamic tracking model (DTM) is proposed. Further, a linear KF algorithm based on DTM model (DTM-KF) is presented. The proposed DTM-KF algorithm gives the covariance matrix of state noise and overcomes the defects of the traditional models based KF algorithms. It is compared with two traditional models–based KF algorithms by simulation and experimentation. The tracking accuracy of the fundamental component and the estimation accuracy of the harmonic components are analyzed and compared. The results show that the proposed DTM-KF algorithm has high tracking accuracy. [ABSTRACT FROM AUTHOR]

It is evident that the prevailing solution, myoelectric pattern recognition for prosthetic manipulation, constrains gesture-based interaction because of the lack of proportional control information such as exerted force. This paper reports an attempt, named simultaneous gesture recognition and muscle contraction force estimation, to realize proportional pattern recognition (PPR) control taking advantage of arm muscle deformation via wearable ultrasound sensing. We experiment with eight types of predefined hand motions, with a range of 0–60% maximum voluntary contraction (MVC) using a wearable multichannel A-mode ultrasound system. The experiment result demonstrates that above 93.7% of gestures are correctly recognized during dynamic muscle contraction forces (0–60% MVC), albeit only training at a slight force level (<6% MVC). Besides, the adopted nonparametric Gaussian process regression estimates the muscle contraction forces accurately and synchronously, with average coefficient of determination, ${\bf R}^{\bf {2}}$ , and normalized root-mean-square error ($nRMSE$) of 0.927 and 0.102, respectively. These research outcomes demonstrate the feasibility of ultrasound-based PPR control, paving the way for musculature-driven applications such as finer prosthetic control, remote manipulation, and rehabilitation treatment. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel method for pedestrian dead reckoning (PDR) using microelectromechanical system magnetic, angular rate, and gravity sensors, which includes a double-step unscented Kalman filter (DUKF) and hidden Markov model (HMM)-based zero-velocity-update (ZVU) algorithm. The DUKF divides the measurement updates of the gravity vector and the magnetic field vector into two steps in order to avoid the unwanted correction for the Euler angle error. The HMM-based ZVU algorithm is developed to recognize the ZVU efficiently. Thus, the proposed PDR method can reduce the position drift caused by the heading error and fault zero-velocity measurement. Experimental results demonstrate that the proposed method achieves better yaw estimate, as well as zero-velocity measurement, and obtains more accurate dead-reckoning position than other methods in the literature. [ABSTRACT FROM AUTHOR]

A novel control scheme is proposed in this paper for the sensorless control of interior permanent magnet synchronous machines (IPMSMs) with the automatic tracking of the maximum-torque-per-ampere (MTPA) trajectory. Differing from the conventional method that the estimation of the rotor position and the tracking of MTPA trajectory are two separate processes, the proposed control scheme can achieve sensorless MTPA control of IPMSMs in one step by defining and constructing a proper virtual q-axis inductance to directly calculate the current space vector angle along the MTPA trajectory rather than the rotor position, which greatly simplifies the whole implementation process. Meanwhile, considering that the virtual q-axis inductance is changing with the machine parameters and the applied load, a simple signal processing technique based on the virtual high-frequency signal injection is developed for the real time and accurate acquisition of the virtual q-axis inductance. Furthermore, in order to ensure the reliability and stability of the proposed control scheme, the effective range of the virtual q-axis inductance is analyzed in detail. Finally, the experimental results on an IPMSM demonstrate the proposed control scheme can achieve the high-performance sensorless control with accurate MTPA self-tracking operation. [ABSTRACT FROM AUTHOR]

In order to promote contouring performance in contour following tasks, many contouring control research works have been conducted based on real-time contouring error. Therefore, accurate contouring error estimation is always an important topic for multiaxis motion systems. In practice, accurate analytical expression of contouring error available to free-form spatial contours is hard to obtain. In this paper, a double Taylor expansion-based contouring error estimation method is proposed for spatial contours. Specifically, contouring error is first expressed by a third-order Taylor equation, which is related to the kinematics information of a datum point. Then, the datum point is determined by the Taylor approximation expanded at the desired point. Compared with the existing Taylor expansion based contouring error estimation strategies, the proposed one can not only achieve higher order accurate approximation, but also eliminate the inaccurate estimation caused by long distance between the datum point and the actual one. The effectiveness of the proposed estimation method is validated by a series of simulation and experiments conducted on a three-axis motion system. [ABSTRACT FROM AUTHOR]

Dimension reduction is a fundamental task of machine learning and computer vision, which is widely used in a variety of industrial applications. Over past decades, a lot of unsupervised and supervised algorithms have been proposed. However, few of them can automatically determine the feature dimension that could be adaptive to different data distributions. To obtain a good performance, it is popular to seek the optimal dimension by exhaustively enumerating some possible values. Clearly, such a scheme is ad hoc and computationally extensive. Therefore, a method which can automatically estimate the feature dimension in an efficient and principled manner is of significant practical and theoretical value. In this paper, we propose a novel supervised subspace learning method called multiple marginal Fisher analysis (MMFA), which can automatically estimate the feature dimension. By maxing the interclass separability among marginal points while minimizing within-class scatter, MMFA obtains low-dimensional representations with outstanding discriminative properties. Extensive experiments show that MMFA not only outperforms other algorithms on clean data, but also show robustness on corrupted and disguised data. [ABSTRACT FROM AUTHOR]

The early alert monitoring system for an effective scheduled maintenance strategy based on a wireless technology requires reliable transfer of the diagnostic information between the sensor and the gateway. This paper presents an industrial wireless sensor network (IWSN)-based machine condition monitoring (MCM) system capable of overcoming a false indication caused by temporary loss of data, signal interference, or invalid data. We use multisensor fusion driven by a quality parameter, which is produced by each sensor node according to the data history outliers and the actual state of the node. The fusion node provides a quality evaluation on its output as well. This novel approach enables the propagation of information about the uncertainty of a measured value from the source node to the sink node. Thus, potential degradation of acquired or transferred diagnostic information is minimized. Instead of raw data, the signal features are transferred, so that bandwidth savings are rapidly improved. The proposed concept was experimentally verified on real wireless sensor network (WSN) hardware. The performance evaluated using the signal-to-noise ratio and false-alarm rate detection demonstrates the effectiveness of the proposed approach. [ABSTRACT FROM PUBLISHER]

In this paper, a doubly fed induction machine (DFIM) operating in motor mode and supplied by two voltage source inverters—in stator and rotor sides—is presented. The aim is to analyze the sensorless rotor position operation of a dual direct torque control (DDTC) of the DFIM. This paper presents a sensorless DDTC for a DFIM speed drive in order to guarantee the static and dynamic system performances. The sensorless DFIM operation provides an analytical redundancy in the case of mechanical sensor failure and ensures the service continuity of the drive. SABER simulation and experimental results illustrate the satisfactory behavior of the system with or without the use of the rotor position sensor. These confirm the preservation of system performances, particularly during the low-speed sensorless control operation. The proposed solution is particularly relevant due to its simplicity. [ABSTRACT FROM PUBLISHER]

This paper aims to improve the full speed sensorless drive for permanent-magnet (PM) synchronous machines on the region of initial zero speed and the region of medium speed. At zero and low speed, the saliency-based drive using the square-wave voltage injection has demonstrated the comparable performance to the sensor-based drive using the encoder with hundreds of pulses per revolution. For PM machines at initial state, the magnet polarity is typically identified based on the injection of voltage pulses before the saliency-based drive. Instead of pulses injection, this paper directly adds the polarity detection capability in the saliency-based drive by superimposing the same square-wave voltage. A fast detection period and negligible current spikes are benefits for the drive using the same square-wave injection voltage for both the polarity detection and position estimation. At medium speed, electromotive force voltage can be estimated to replace the saliency for the sensorless drive. To achieve a smooth operation between two estimation methods, a transition algorithm is also proposed based on a single-phase-lock loop by blending two feedback position signals. According to the experimental evaluation, the reduced power consumption as well as the smooth switch between two different drives is achieved. All the sensorless methods are verified by an interior PM machine with a saliency ratio of 1.37 (Lq/ Ld\,= \,1.37 ). [ABSTRACT FROM AUTHOR]

Li-ion batteries have been widely used in electric vehicles, and battery internal state estimation plays an important role in the battery management system. However, it is technically challenging, in particular, for the estimation of the battery internal temperature and state-of-charge (SOC), which are two key state variables affecting the battery performance. In this paper, a novel method is proposed for realtime simultaneous estimation of these two internal states, thus leading to a significantly improved battery model for realtime SOC estimation. To achieve this, a simplified battery thermoelectric model is first built, which couples a thermal submodel and an electrical submodel. The interactions between the battery thermal and electrical behaviors are captured, thus offering a comprehensive description of the battery thermal and electrical behavior. To achieve more accurate internal state estimations, the model is trained by the simulation error minimization method, and model parameters are optimized by a hybrid optimization method combining a metaheuristic algorithm and the least-square approach. Further, time-varying model parameters under different heat dissipation conditions are considered, and a joint extended Kalman filter is used to simultaneously estimate both the battery internal states and time-varying model parameters in realtime. Experimental results based on the testing data of LiFePO$_4$ batteries confirm the efficacy of the proposed method. [ABSTRACT FROM PUBLISHER]

A novel position sensorless permanent-magnet synchronous motor (PMSM) drive that uses only a direct current (dc)-link sensor is presented in this paper. This concept is already known, but several new implementation details are shown in this paper. The proposed drive system is simple and can be implemented using low-cost control hardware. The dynamic performance of the proposed drive is evaluated and compared with the performance of position sensorless and full sensored drives. [ABSTRACT FROM PUBLISHER]

Islanding or nonislanding events in grid-connected distributed generation bring along a typical distinguishable transient signature in its frequency profile. This demarcation leads to the development of a new islanding protection approach, which is based on the estimation of frequency waveform parameter (transient's frequency) by Matrix pencil (MP) method. To demonstrate the efficacy of the proposed MP method, four critical scenarios are considered in this paper for covering all possible disturbance events. These events are also compared along with a discrete wavelet transform (DWT) based islanding detection method in simulations as well as in RT-LAB-based real-time environment. It is noteworthy to mention that the proposed MP method has been found to have a positive edge over the DWT-based method in terms of robustness and chances of misidentification. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for the conversion of an implicit current–voltage (I–V) expression of a double diode model (DDM) photovoltaic (PV) module to an explicit expression of current in terms of voltage. A new method is proposed for the estimation of maximum power point (MPP) of a DDM PV module using the explicit I–V expression of a PV module under uniform irradiance condition. Further, a new mathematical model of a partially shaded PV array is developed using the proposed explicit expression. Since the explicit I–V expression is used for the estimation of MPP of a DDM PV module under uniform and nonuniform irradiance conditions, the estimation of MPP is simple, accurate, and efficient. The accuracy of the explicit expression is verified considering three different PV modules. A comparison of MPP of a DDM PV module extracted using the proposed method with different existing methods under different environmental conditions (DECs) is presented. The results obtained using the proposed method are validated with the experimental results under DEC. Also, the accuracy of the estimated global MPP of a PV array under partial shading conditions is verified. [ABSTRACT FROM AUTHOR]

In sensor networks (SNs), how to allocate the resources so as to optimize data gathering and network utility is an important and challenging task. This paper studies the distributed optimization problem in SNs. A distributed hybrid-driven algorithm based on the coordinate descent method is presented for the optimization purpose. The proposed optimization algorithm differs from the existing ones since the hybrid driven scheme allows more choices of actuation time, resulting a tradeoff between communications and computation performance. Applying the proposed algorithm, each sensor node is driven in a hybrid event time manner, which removes the requirement of strict time synchronization. The convergence and optimality of the proposed algorithm are analyzed, and then verified by simulation examples. The developed results also show the tradeoff between communications and computation performance. [ABSTRACT FROM AUTHOR]

Accurate estimation of power capacity is critical to ensure battery safety margins and optimize energy utilization. Power capacity estimators based on online identified equivalent circuit model have been widely investigated due to the high accuracy and affordable computing cost. However, the impact of noise corruption which is common in practice on such estimators has never been investigated. This paper scrutinizes the effect of noises on model identification, state of charge (SOC) and power capacity estimation. An online model identification method based on adaptive forgetting recursive total least squares (AF-RTLS) is proposed to compensate the noise effect and attenuate the identification bias of model parameters. A Luenberger observer is further used in combination with the AF-RTLS to estimate the SOC in real time. Leveraging the estimated model parameters and SOC, a multiconstraint analytical method is proposed to online estimate the power capacity. Simulation and experimental results verify that the proposed method is superior in terms of estimation accuracy and the robustness to noise corruption. [ABSTRACT FROM AUTHOR]

This paper proposes a novel finite-memory (FM) estimation method for vehicle roll and road bank angles. We consider a three-degree-of-freedom model based on bicycle and vehicle roll models, where there exist uncertainties in the model parameters. The existing estimation methods may show poor performance for the considered model with uncertainties. The accumulated errors, which might be caused by modeling uncertainties and numerical errors, may degrade the performance of existing methods because they use all past measurements. To overcome the weaknesses of the existing methods, a new FM estimation method is proposed and can prevent accumulated errors by using recent finite measurements. Both the simulation and experimental results show that the proposed estimation method ensures more reliable and accurate performance than the existing methods under various driving conditions. [ABSTRACT FROM AUTHOR]

This paper deals with torque ripple estimation and reduction in a vector controlled double-inverter-fed wound rotor induction machine (DI-WRIM) drive by employing different pulsewidth modulation (PWM) techniques applied to stator and rotor side voltage source inverters (VSIs). The influence of PWM switching sequences on torque ripple is studied in terms of stator and rotor flux ripples. A novel rotor speed independent transformation has been proposed to visualize the nature of stator and rotor flux ripples from a common frame of reference. The expression for instantaneous torque ripple is formulated and a PWM combination is proposed resulting in lower root mean square (rms) and peak–peak torque ripple. The estimated instantaneous, rms, and peak–peak torque ripple is verified through simulation and experimental results from a 3-kW vector controlled DI-WRIM drive. [ABSTRACT FROM AUTHOR]

In this paper, a method is presented for antidisturbance constrained control of autonomous underwater vehicles subject to uncertainties and constraints. The uncertainties stem from uncertain hydrodynamic parameters, modeling errors, and unknown forces due to the ocean currents in an underwater environment. An antidisturbance constrained controller is developed by designing a command governor and a disturbance observer. Specifically, the disturbance observer is developed to estimate the lumped disturbance composed of parametric model uncertainties, modeling errors, and unknown environmental forces. The command governor is designed for optimizing command signals in the receding horizon within the state and input constraints. The command governor is formulated as a quadratically constrained quadratic programming problem. To facilitate online implementations, a neurodynamic optimization method based on a one-layer recurrent neural network is employed for solving the quadratic optimization problem subject to inequality constraints with finite-time convergence. The efficacy of the proposed antidisturbance constrained control method for autonomous underwater vehicles is substantiated via simulations and comparisons. [ABSTRACT FROM AUTHOR]

Bearing remaining useful life (RUL) prediction plays a crucial role in guaranteeing safe operation of machinery and reducing maintenance loss. In this paper, we present a new deep feature learning method for RUL estimation approach through time frequency representation (TFR) and multiscale convolutional neural network (MSCNN). TFR can reveal nonstationary property of a bearing degradation signal effectively. After acquiring time-series degradation signals, we get TFRs, which contain plenty of useful information using wavelet transform. Owing to high dimensionality, the size of these TFRs is reduced by bilinear interpolation, which are further regarded as inputs for deep learning models. Here, we introduce an MSCNN model structure, which keeps the global and local information synchronously compared to a traditional convolutional neural network (CNN). The salient features, which contribute for RUL estimation, can be learned automatically by MSCNN. The effectiveness of the presented method is validated by the experiment data. Compared to traditional data-driven and different CNN-based feature extraction methods, the proposed method shows enhanced performance in the prediction accuracy. [ABSTRACT FROM AUTHOR]

Measurements quality is important for process systems engineering. In this paper, an estimation scheme is proposed in the state-space form to monitor the degree of accuracy of measurements within a predefined horizon. Under the assumption that all the sensors are uncorrelated with each other, the distribution of measurement noise covariance as well as the distribution of state vector are estimated simultaneously. The key technique is to approximate the true posterior distribution by two independent proposal distributions using the variational Bayesian inference. It is shown that the proposed algorithm provides not only a complete picture of the working status of each sensor, but also satisfied estimates of the hidden states in the presence of faulty signals. Numerical examples with a moving target tracking model and a quadrate water tank experiment are conducted to demonstrate that the proposed method exhibits better performance than the existing methods, and even a small fluctuation of sensors can be accurately captured by the proposed algorithm. [ABSTRACT FROM AUTHOR]

Micro-short-circuit (MSC) is a latent risk in power batteries, which may give rise to thermal runaway and even catastrophic safety hazards. The motivation of this paper is to quantitatively analyze MSC in an initial stage, particularly for lithium-ion batteries. To verify the feasibility of the proposed method, an equivalent MSC experiment is carried out. Based on a cell difference model, the cell state of charge (SOC) differences with the mean SOC for a battery pack are estimated by extended Kalman filter. The evaluated SOC difference can track the actual value well. Furthermore, an MSC diagnostic method is developed by employing recursive least squares filter. The method is demonstrated to examine the short-circuit resistance accurately. The results also show that the proposed method requires low computational load for the SOC difference and short-circuit resistance diagnosis. [ABSTRACT FROM AUTHOR]

One of the future challenges in modular multilevel converters (MMCs) is how to size key components with compromised costs and design margins, while fulfilling specific reliability targets. It demands better thermal modeling compared to the state-of-the-arts in terms of both accuracy and simplicity. Different from two-level power converters, MMCs have inherent dc-bias in arm currents and the power device conduction time is affected by operational parameters. A time-wise thermal modeling for the power devices in MMCs is, therefore, an iteration process and time-consuming. This paper, thus, proposes a simple analytical thermal modeling method, which adopts equivalent periodic power loss profiles. More importantly, time-domain simulations are not required in the proposed method. Benchmarking of the proposed methods with the prior-art solutions is performed in terms of parameter sensitivity and model accuracy with a case study on a 30-MW MMC system. Experiments are carried out on a specifically designed scaled-down system to verify the electrothermal aspects. [ABSTRACT FROM AUTHOR]