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A fast and precise dynamic phasor estimation algorithm using an adaptive improved second order Levenberg–Marquadt algorithm under stressed and off-nominal frequency circumstances is proposed in this paper. The proposed algorithm ventures a faster quasi-second-order method and in addition, the learning rate is also made adaptive to speed up the convergence. Moreover, the projected estimator essentially filters harmonics, estimates transients in a signal during faults and effectually performs in complex modulated setting which is found commonly in a power system with distributed energy resources. Outcomes reveal that the estimation errors and the convergence time of the proposed approach is way below the minimum requirement of the IEEE C37.118.1 standard. Besides, due to its reduced complexity, the estimator has been successfully implemented on the field programmable gate array (FPGA) platform and has been verified on a practical microgrid scenario including photo voltaic systems. The performance during testing indicates that the proposed technique has potential ability to perform under stressed conditions conforming the accuracy and speed of response. [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]

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]

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]

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 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]

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]

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]

Parasitic capacitance has a considerable impact on conductive electromagnetic interference (EMI), especially on the common mode (CM) noise. In vehicular applications, such as more electric aircraft and electrical vehicles, the value of CM stray capacitance can vary during the drive cycle, due to some change in environmental, and operating conditions, resulting in fluctuations in EMI level. Designing EMI filters according to potentially overestimated CM capacitance results in additional weight, volume, and cost which should be avoided. In this paper, for the first time, the parasitic capacitance relevant to CM noise is estimated in real-time using unscented Kalman filter (UKF). The UKF is employed due to its ability in dealing with nonlinearity and stochastic nature of the EMI as well as the measurement noises. The proposed method is tested on a typical dc–dc buck converter in different operating conditions. The effectiveness of the proposed method is tested using some hardware-in-the-loop experiments using dSPACE1104 development platform. [ABSTRACT FROM AUTHOR]

This paper presents a new implementation method for efficient simultaneous localization and mapping using a forward-viewing monocular vision sensor. The method is developed to be applicable in real time on a low-cost embedded system for indoor service robots. In this paper, the orientation of a robot is directly estimated using the direction of the vanishing point. Then, the estimation models for the robot position and the line landmark are derived as simple linear equations. Using these models, the camera poses and landmark positions are efficiently corrected by a local map correction method. The performance of the proposed method is demonstrated under various challenging environments using dataset-based experiments using a desktop computer and real-time experiments using a low-cost embedded system. The experimental environments include a real home-like setting. These conditions contain low-textured areas, moving people, or changing environments. The proposed method is also tested using the robotics advancement through web publishing of sensorial and elaborated extensive datasets benchmark dataset. [ABSTRACT FROM AUTHOR]

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]

Conventional permanent magnet (PM) machine open-phase fault detection relies on the estimation of current harmonics using available phase current sensors. Because the magnitudes of current harmonic are proportional to the machine load condition, it is still a challenge to detect a phase fault at light load when fault-induced current harmonics are too small to estimate. This paper investigates the phase fault detection specifically for the conditions under low fault harmonic magnitudes. Both fault-induced harmonics in dq currents and neutral point (NP) voltage are analyzed to find a suited signal for the fault detection. By adding a voltage sensor to measure the NP voltage in a PM machine, a better fault detection performance is achieved because the fault harmonic in NP voltage results in the reduced effect on torque loads and sensor measurement errors. A 50-W PM machine is tested to verify the phase fault detection performance under low fault harmonic magnitudes. This paper includes the experimental evaluation on the detection limitation in terms of load capability using current and voltage fault signals. [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]

Data-driven soft sensors, estimating the pivotal quality variables, have been widely employed in industrial process. This paper proposes a novel soft sensor modeling approach based on a two-stream ${\rm{\lambda }}$ gated recurrent unit ($TS - {\rm{\lambda }}$ GRU) network. First, factors ${{\rm{\lambda }}_1}$ and ${{\rm{\lambda }}_2}$ are implemented to alter the linear constraint existing in the original GRU unit, enriching the information passing through. Then, a two-stream network structure is designed, equipped with some advanced network parameter adjustment techniques, such as batch normalization and dropout rate, to learn diverse features of the various process data. Finally, the learned features from the two streams are fused and a supervised learning regression layer is employed to decrease the error between the output and label. The application in melt viscosity index estimation for a real polymerization industrial process has demonstrated that the proposed $TS - {\rm{\lambda }}$ GRUs algorithm for soft sensor modeling is more accurate and promising than other existing methods. [ABSTRACT FROM AUTHOR]

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]

This paper deals with a multipurpose distributed sparse (DS) control approach for a single stage solar photovoltaic (PV) energy generation system (SPEGS). This SPEGS is interfaced here to the three phase grid at varying solar irradiance and compensating the nonlinear load tied at point of common interconnection. The SPEGS performs multitasks. It feeds the generated solar PV power to the local three phase grid. It reduces the harmonics of loads and furnished balanced currents of local three-phase grid. The SPEGS uses a solar PV array, a voltage source converter (VSC), a nonlinear load, a three phase grid, and a dc-link capacitance. In case, when the solar irradiance is not available, the proposed system works as distribution static compensator by utilizing same VSC. For extracting maximum power from the PV source, the traditional perturb and observe (P&O) scheme is utilized here. The tracking performance and efficiency of P&O technique are also examined here at rapid changing climatic conditions to show the behavior of the P&O scheme. The DS control approach is capable to estimate required fundamental component to find out reference grid currents. The proposed control approach is validated on a developed prototype in the laboratory. [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 stator winding structure of the main exciter of the brushless synchronous starter/generator (BSSG) is changed at high speed for flux weakening, leading to a sudden excitation decrease of the main generator (MG) of BSSG. Consequently, when proportional-integral (PI) current loop-based control strategies are applied, large current overshoots will occur due to the lack of fast current control ability. This paper adopts model predictive current control (MPCC) to replace PI current control for a better dynamic performance. Besides, a parameter estimation scheme, based on the recursive least square (RLS) algorithm, is proposed to provide MG parameters for MPCC. An effectiveness analysis of the RLS algorithm is performed and two rules are given. According to the two rules, rather than the parameter dynamic characteristics adopted by traditional methods, three MG parameters, dq-axes inductances, and excitation flux linkage are estimated with two decoupled RLS estimators. In addition, a method is proposed to adjust the forgetting factor to track fast changing parameters of MG when the excitation changes suddenly. With the estimated parameters, MPCC is conducted to start BSSG. Experimental results show that fast current control of MG is achieved and large current overshoots caused by the sudden excitation change are avoided. [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]

The fast detection of magnitude, frequency, and phase angle of the fundamental-frequency component is very useful in many three-phase power system applications. Most techniques to accomplish this task are based on different versions of three-phase phase-locked loop (PLL) schemes. Some variables of three-phase systems may experience phase angle jumps after some disturbances. In this paper, a method for the fast detection of phase angle jump occurrence, estimation of the phase angle jump, and its use for improving the performance of the generalized delayed signal cancelation (GDSC) PLL is proposed. The proposed method is based on the detailed evaluation of the GDSC filter transient behavior after a phase jump. Results considering typical three-phase grid disturbances are used to evaluate the proposed scheme, in comparison with other usual PLL techniques. The use of the proposed phase jump angle estimator allowed a reduction in the GDSC-PLL convergence time of about 50%. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive time-delay control method to handle the balance of a class of biped robots. Considering model uncertainties and unknown external disturbances, a high-dimensional integral Lyapunov function has been developed, then time-delay estimation technique (TDE) is utilized, and in order to compensate for the errors produced by TDE, an adaptive control law is designed. Based on the evolution of the error trajectories, the adaptive control law can automatically regulate the control gains without any threshold value. By the Lyapunov synthesis, the semiglobally uniformly ultimately boundness (UUB) of the closed-loop control system has been theoretically proved. We have conducted the experimental studies on biped robot to demonstrate the effectiveness of the proposed control method. [ABSTRACT FROM AUTHOR]

This paper presents a novel position sensorless control for 6/4 switched reluctance motors (SRMs) using only one-phase current sensor in medium and high speed. In order to eliminate phase voltage sensors, a virtual phase voltage is established to replace the real-time phase voltage in the whole conduction cycle, so as to calculate the phase flux linkage which is called virtual flux linkage. A nonlinear model is utilized to estimate the reference flux linkage-curve of any rotor position. With only one-phase current sensor, the determination of turn off angle position and phase commutation position is achieved by the comparison of reference flux linkage and virtual flux linkage. The speed estimation is performed by two reference flux linkage-current curves at particular rotor positions. Based on the obtained turn off pulse of the present phase and the turn on pulse of next phase, the remaining trigger pulse signals of three phases are derived from a delay-time strategy. The proposed scheme requires only one-phase current sensor, without any pulse injection or complicated decoupling algorithm, achieving a simple and cost-effective drive. Simulations and experiments are carried out on a 6/4-pole prototype SRM drive system to verify the correctness and effectiveness of the proposed sensorless control scheme. [ABSTRACT FROM AUTHOR]

A novel closed-loop numerical algorithm for subspace state space system identification (CN4SID) is proposed in this paper. Different from standard schemes, CN4SID algorithm can extend the standard LQ decomposition to the closed-loop cases by incorporating the controller information. Moreover, the proposed method can deliver unbiased pole estimation in the closed-loop framework. To this end, CN4SID algorithm shows superior pole estimation performance compared with a class of subspace identification method via principal component analysis algorithms. The effectiveness of the proposed CN4SID algorithm is finally verified through a practical dc motor system. [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]

It is an effective approach to estimate rotor position of doubly salient electromagnetic machine (DSEM) from self-inductances, which changes with rotor position. To overcome the restrictions of low estimation accuracy as well as the inevitable commutation lag in the conventional sensorless startup method, this paper proposes a novel rotor angular position estimating technique for the DSEM sensorless startup. It starts with injecting three test pulses A + C- (phase A is connected to the positive rail of dc bus, and phase C is connected to the ground rail), B + A- and C + B- on armature windings, so two-phase series self-inductances can be identified according to the resultant currents. Then, the estimating expression of the DSEM rotor angular position is further obtained based on the geometrical relation in the rectangle composed of self-inductances, and the inevitable commutation lag in startup process is likely to be eliminated with the estimated rotor angular position. Moreover, the error of rotor angular position introduced by the nonideal linearity of self-inductances are analyzed in detail, and the boundaries of neighboring sectors are distinguished from normal sectors to compensate the estimation error of rotor angular position. Finally, to enhance the output torque performance during the DSEM startup process, a novel selection method for test pulses concerning rotor sector is developed. Compared to the conventional method, a quicker and superior startup performance is achieved since no negative torque is generated by the test pulses. The experiments on a 12/8-pole DSEM validate the correctness and feasibility of the proposed sensorless control methods. [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]

How to achieve competitive accuracy and less computation time simultaneously for background estimation is still an intractable task. In this paper, an effective background subtraction approach for video sequences is proposed based on a subsuperpixel model. In our algorithm, the superpixels of the first frame are constructed using a simple linear iterative clustering method. After transforming the frame from a color format to gray level, the initial superpixels are divided into K smaller units, i.e., subsuperpixels, via the k-means clustering algorithm. The background model is then initialized by representing each subsuperpixel as a multidimensional feature vector. For the subsequent frames, moving objects are detected by the subsuperpixel representation and a weighting measure. In order to deal with ghost artifacts, a background model updating strategy is devised, based on the number of pixels represented by each cluster center. As each superpixel is refined via the subsuperpixel representation, the proposed method is more efficient and achieves a competitive accuracy for background subtraction. Experimental results demonstrate the effectiveness of the proposed method. [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]

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]

Aluminum electrolytic capacitors (AECs) are connected in a bank to meet the requirement of capacitance, voltage, and current rating for dc-link of power electronic converters. The performance of capacitor bank deteriorates with time due to the degradation of AECs in the bank. Further, each AEC in the bank degrades differently based on its core temperature, which in turn depends on the current and its equivalent series resistance (ESR). Therefore, health monitoring of a bank would require monitoring of individual AECs in the bank. However, extending the available literature on health monitoring of an AEC to the capacitor bank would require voltage/current sensors for individual AECs. To address the aforementioned issue, this paper suggests a method to assess the health of individual AECs online in the bank without using sensors for each AECs. The proposed method involves estimation of current through each capacitor, which is used to determine the core temperature by using a thermal model of AEC. The core temperature is used for estimation of ESR and capacitance of each capacitor using physics-of-failure based degradation model. For the consistent matching of the degradation model to the actual degradation of the capacitor, the coefficients of the model are sequentially updated. For updating, an algorithm based on extended Kalman filter is used. The proposed scheme is experimentally validated on laboratory prototype of single-phase, grid-connected solar PV inverter under variable load conditions. [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]

This paper proposes a light, flexible, and yet reliable multiparametric estimation algorithm for grid-connected voltage-source-converter-based systems. This approach combines the nonlinear least squares (NLSQ) fitting algorithm and the steady-state extended harmonic domain (EHD) model to take into account the harmonic effects. The EHD model is used to take advantage of the harmonic content of the electrical signals to provide robustness and improved performance to the NLSQ estimation methodology. In addition, this harmonic formulation reduces the necessity of synchronized measurements. Details of the implementation are provided by an experimental case study, in which the grid equivalent (Thevenin voltage, inductance, and resistance), the ac-side filter (inductance and resistance), and the converter switching and conduction loss resistance are accurately estimated for three different operating conditions. [ABSTRACT FROM AUTHOR]

This paper is concerned with the problem of distributed multitask learning over networks, which aims to simultaneously infer multiple node-specific parameter vectors in a collaborative manner. Most of the existing works on the distributed multitask problem modeled the task relatedness by assuming some similarities of parameter vectors in an explicit way. In this paper, we implicitly model the similarity of parameter vectors by assuming that the parameter vectors share a common low-dimensional predictive structure on hypothesis spaces, which is learned using the available data in networks. A distributed structure learning algorithm for the in-network cooperative estimation problem is then derived based on the block coordinate descent method integrated with the inexact alternating direction method of multipliers technique. Simulations on both synthetic and real-world datasets are given to verify the effectiveness of the proposed algorithm. In the case that each node shares a common predictive subspace, it is demonstrated that the proposed multitask algorithm outperforms the noncooperative learning algorithm. Moreover, the use of the inexact approach can significantly reduce the communication bandwidth and still provide the same optimal solution as the corresponding centralized approach. [ABSTRACT FROM AUTHOR]

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]

In this paper, a method is proposed to estimate the detailed muscular strength of a human limb. The detailed muscular strength discussed in this paper is not the strength produced by coordinated muscle actions but the joint torque generated by each muscle group classified according to its function. It is assumed that a human limb is modeled as a two-link manipulator actuated by three pairs of six muscle groups. Although the model theoretically derives the output force distribution at an end effector from the joint torques, the joint torques are not calculated directly from the distribution. The antagonistic ratios of the output forces at the end effector are introduced to solve this problem. However, the estimation results cannot be compared with actual values in a real environment because it is impossible to measure the output of only a certain muscle in a noninvasive way. Instead, simulations and experiments using a robotic arm demonstrate that the proposed method estimates the output ratios of muscle groups with a satisfactory accuracy. [ABSTRACT FROM AUTHOR]

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]

Although it is widely known that the saliency-based position-sensorless drive is able to achieve the closed-loop control at zero and low speed, there is little literature addressing the consideration on the selection of injection voltage frequency. This paper evaluates the square-wave injection voltage at different frequencies for the design of an interior permanent-magnet (PM) machine sensorless drive. It is shown that more flux saturation on high-frequency (HF) d-axis inductance occurs than the saturation on q-axis inductance due to the magnetic relaxation. The HF saliency ratio \rm(L\rm{qhf}\rm{/ }L\rm{dhf}\rm{)} is increased by increasing the injection frequency. The performance of a saliency-based sensorless drive can be enhanced by properly designing the frequency of injection voltage. This paper also includes the experimental comparison between closed-loop encoder-based and closed-loop saliency-based sensorless operation of an interior PM machine drive. [ABSTRACT FROM AUTHOR]

To interconnect low-voltage solar photovoltaics (PV) with dc system, a dc–dc boost converter is required. To minimize the switching frequency oscillations in solar PV voltage, aluminum electrolytic capacitors (AECs) are connected between solar PV and the converter. Operational life of AECs depends on electrical and environmental parameters and is less than that of solar PV panels. Equivalent series resistance (ESR) of AEC increases with life, thereby increasing the voltage ripple across PV terminals. This results in less power extraction from solar PV. This paper proposes a method for health monitoring of AEC using the parameters measured for maximum power point tracking (MPPT) (PV voltage and current). The proposed technique is applicable for both continuous conduction mode and discontinuous conduction mode of operation of the converter. Effect of temperature on ESR is incorporated for accurate determination of the health of capacitor. Key advantage is that no additional current or voltage sensor is required to implement this technique, thereby offering a low-cost solution. Further, the proposed technique does not require significant processing capabilities and is implemented in the same digital controller used for MPPT. Detailed simulation studies are carried out and results are included in this paper. A laboratory prototype of dc–dc boost converter is developed for experimentation. Results are in agreement with that obtained from simulation studies. [ABSTRACT FROM AUTHOR]

This paper aims to realize compact actuation systems by sensorless approach. This study adopts solenoid actuators which have advantageous characteristics from the view point in inexpensive cost, simple structure, and high-output force in spite of difficulty in low cost position and force sensing. Some sensorless position estimation techniques for solenoid actuators were reported. However, position and force simultaneous estimation has not sufficiently been achieved. In addition, any real-time feedback control has not been reported. This paper proposes sensorless feedback position control for compact solenoid actuators. The force generation and position/force estimation are conducted by using a dc supply power superposed by an ac component. This control technique has three main features: 1) separation of the power supply into ac and dc components for the position estimation, 2) simultaneous real-time estimation of position and force, and 3) robust feedback position control. Simulations and experiments were conducted for verification of the proposed control with the above three features, and the results indicated that error between the estimation and actual positions was within 0.2 mm. This proposed system offers a new avenue for compact and inexpensive application field. [ABSTRACT FROM PUBLISHER]

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]