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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]

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]

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 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]

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]

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]

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]

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 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]

Several modeling, estimation, and control strategies have been recently presented for simple reluctance devices like solenoid valves and electromagnetic switches. In this paper, we present a new algorithm to online estimate the flux linkage and the electrical time-variant parameters of these devices, namely the resistance and the inductance, only by making use of discrete-time measurements of voltage and current. The algorithm, which is robust against measurement noise, is able to deal with temperature variations of the device and provides accurate estimations during the motion of the armature. Additionally, an integral estimator that uses the start of each operation of the actuator as reset condition has been also implemented for comparative purposes. The performances of both estimation methods are studied and compared by means of simulations and experimental tests, and the benefits of our proposal are emphasized. Possible uses of the estimates and further modeling developments are also described and discussed. [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]

This paper proposes a new estimator design algorithm for state-of-charge (SoC) indication of lithium-ion batteries. A fractional-order model-based nonlinear estimator is first framed including a Luenberger term and a sliding mode term. The estimator gains are designed by Lyapunov's direct method, providing a guarantee for stability and robustness of the error system under certain assumptions. This generic estimation algorithm is then applied to lithium-ion batteries. A fractional-order circuit model is adopted to predict battery dynamic behaviours. Assumptions based on which the estimation algorithm is developed are justified and remarked. Experiments corresponding to electric vehicle applications are conducted to parameterize the battery model and demonstrate the estimation performance. It shows that the proposed approach is able to estimate SoC with errors less than 0.03 in the presence of initial deviation and persistent noise. Furthermore, the benefits of using the proposed estimator relative to other estimators are calculated over different cycles and conditions. [ABSTRACT FROM PUBLISHER]

This paper proposes a robust coupled multiple sliding-mode control (CMSMC) method for tracking control of underactuated hovercraft systems with nonholonomic constraints and external disturbances. First, a friction model for the hovercraft, one of the main disturbance factors, is proposed by considering viscosity, and its validity is demonstrated through experiments. Second, a disturbance model in the actual hovercraft system is estimated using a least-square-estimation-based disturbance observer. Third, pseudo forces and pseudo heading direction angle for tracking control and disturbance compensation are proposed considering the characteristics of underactuated hovercraft systems. Fourth, coupled multiple sliding surfaces (CMSSs) are newly introduced in terms of the tracking errors between the pseudo control variables and actual ones, and then, a CMSMC-based controller is proposed so that the CMSSs converge to zero within finite time in the case of zero disturbance estimation errors. In this way, three posture variables of the hovercraft converge to reference ones using only two control inputs. Finally, stability analysis and verification by simulations and experiments show that both the pseudo control tracking errors and posture tracking errors are ultimately bounded and asymptotically converge to zero when disturbance estimation errors become zero. [ABSTRACT FROM PUBLISHER]

Rolling element bearings cause the largest number of failures in induction motors. Predicting an impending failure and estimating the remaining useful life (RUL) of a bearing is essential for scheduling maintenance and avoiding abrupt shutdowns of critical systems. This paper presents a hybrid technique for bearing prognostics that utilizes regression-based adaptive predictive models to learn the evolving trend in a bearing's health indicator. These models are then used to project forward in time and estimate the RUL of a bearing. The proposed algorithm addresses some key issues in existing methods for bearing health prognosis that affect their prognostic performance, specifically determining the time to start prediction (TSP), handling random fluctuations in a bearing's health indicator, and setting a dynamic failure threshold. The proposed algorithm is validated on publicly available bearing prognostics data from the Center for Intelligent Maintenance Systems. Experimental results show that the proposed approach is effective in determining an accurate TSP and failure threshold, as well as handling random fluctuations. Moreover, this approach achieves excellent prognostic performance and estimates the RUL of bearings within the specified error bounds, even at points very close to the TSP, where traditional methods yield relatively poor RUL estimates. [ABSTRACT FROM PUBLISHER]

This paper is concerned with parameter and output estimation for industrial processes described by finite impulse response model in presence of randomly missing output measurements. The statistical models for describing the estimation problems are given and the prior distributions over unknown parameters and variables are constructed. The estimation problems with incomplete dataset are formulated in the variational Bayesian framework and the problems of randomly missing measurements, overfitting, and high sensitivity of parameter estimate to noise are handled simultaneously. The iterative formulas to estimate the posterior distributions of missing output data and unknown parameters based on available process data are derived. The simulation example and the hybrid tank system experiment are performed to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

Health monitoring of hybrid systems has attracted substantial attention in recent years. However, two issues exist in the monitored hybrid systems which could add to the difficulty of algorithm development: First, multiple faults with unknown distinct nature may occur simultaneously; and second, fault detection time and mode change time might be negatively correlated which obstructs the completion of fault estimation. To deal with these two problems, a scheduled health monitoring (SHM) method is developed in this paper. First, a unified fault modeling that enables the fault estimation, when the fault nature is unknown, is proposed. After that, to tackle the problem that the fault estimation cannot be completed when the time of fault detection is close to the following mode change time which makes the fault undetectable, a scheduling method is developed where the activation of fault estimator is controlled by the scheduler. For the purpose of fault estimation and prognosis under the SHM framework, a diversity enhanced particle filter (PF) is developed which alleviates the sample impoverishment problem in the traditional PF. Finally, experimental investigations are conducted to validate the proposed methodology. [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]

This paper presents two sets of estimator designs for distributed sensor networks in multitarget tracking under signal transmission faults due to the uncertain environments. A two-target tracking sensor network model is then established. Based on the tracking error systems, new augmented systems are proposed. It is proved that, using these systems, one can construct two different sets of estimators. The first estimation approach is to construct a fault estimator for the signal transmission faults occurred in the process of the two-target tracking in distributed sensor networks. The second estimation approach is to design a state/fault estimator. Alternatively, the second approach is proposed to design an estimator for both fault and state simultaneously in sensor networks tracking. Furthermore, the work can be extended to multitarget tracking problems in a more general setting. Finally, two application examples are used to illustrate the effectiveness of the theoretical results. [ABSTRACT FROM PUBLISHER]

Various functional candidates such as flux, back electromotive force, and reactive power are used to form a model reference adaptive system (MRAS) to estimate the speed of an induction motor drive. Of these, reactive power ( $Q$) based controllers perform well at low speeds and are inherently independent of stator resistance. However, such configuration fails to provide stability in the regenerative mode. This paper proposes a new formulation of reactive-power-based MRAS that is stable in all the four quadrants of operation. A detailed MATLAB/Simulink based simulation study is presented. Superior performance of the proposed controller is confirmed by prototype experimentation using a field-programmable-gate-array based controller in the laboratory. [ABSTRACT FROM PUBLISHER]

Many control schemes rely on an analytical model of the servomechanism to be controlled, and hence, accurate knowledge about the position- and time-dependent parameter variability becomes crucial in many contexts, such as robust control methods. Although a properly designed robust controller can cope with a large parameter variation, real-time identification of the system parameter behavior could lead to several advantages by means of monitoring the varying dynamics, e.g., the predetermined uncertainty region around the nominal value. This paper addresses issues in online parameter estimation of a linear tooth belt drive with a limited stroke. Particular attention is paid to detecting the position-dependent changes in the system dynamics by using recursive least squares algorithm and exciting the system in different cart positions in order to identify the varying dynamics. The algorithm used is based on an indirect output-error identification scheme. The experimentally estimated parameters are compared with the corresponding two-mass model parameters. The results show an acceptable agreement and demonstrate the feasibility of the estimation method to estimate the parameters of a closed-loop controlled servomechanism with a limited stroke and time-varying parameters. [ABSTRACT FROM PUBLISHER]

A state-space electromyography (EMG) model is developed for continuous motion estimation of human limb in this paper. While the general Hill-based muscle model (HMM) estimates only joint torque from EMG signals in an “open-loop” form, we integrate the forward dynamics of human joint movement into the HMM, and such an extended HMM can be used to estimate the joint motion states directly. EMG features are developed to construct measurement equations for the extended HMM to form a state-space model. With the state-space HMM, a normal closed-loop prediction–correction approach such as the Kalman-type algorithm can be used to estimate the continuous joint movement from EMG signals, where the measurement equation is used to reject model uncertainties and external disturbances. Moreover, we propose a new normalization approach for EMG signals for the purpose of rejecting the dependence of the motion estimation on varying external loads. Comprehensive experiments are conducted on the human elbow joint, and the improvements of the proposed methods are verified by the comparison of the EMG-based estimation and the inertial measurement unit measurements. [ABSTRACT FROM PUBLISHER]

Low-cost inertial/magnetic sensor units have been extensively used to determine sensor attitude information for a wide variety of applications, ranging from virtual reality, underwater vehicles, handheld navigation systems, to biomotion analysis and biomedical applications. In order to achieve precise attitude reconstruction, appropriate sensor calibration procedures must be performed in advance to process sensor readings properly. In this paper, we are aiming to calibrate different error parameters, such as sensor sensitivity/scale factor error, offset/bias error, nonorthogonality error, mounting error, and also soft iron and hard iron errors for magnetometers. Instead of estimating all of these parameters individually, these errors are combined together as the combined bias and transformation matrix. Two-step approaches are proposed to determine the combined bias and transformation matrix separately. For the accelerometer and magnetometer, the combined bias is determined by finding an optimal ellipsoid that can best fit the sensor readings, and the transformation matrix is then derived through a two-step iterative algorithm by exploring the intrinsic relationship among sensor readings. For the gyroscope, the combined bias can be easily determined by placing the sensor node stationary. For the transformation matrix estimation, the intrinsic relationship among gyroscope readings is explored again, and an unscented Kalman filter is employed to determine such matrix. The calibration methods are then applied to our sensor nodes, and the good performance of the orientation estimation has illustrated the effectiveness of the proposed sensor calibration methods. [ABSTRACT FROM PUBLISHER]

This paper proposes a multiparameter estimation scheme for permanent-magnet (PM) synchronous machines (PMSMs) under variable-speed control, of which the estimation model is full rank and has taken into account the estimation and compensation of voltage-source-inverter nonlinearity. During the proposed estimation, the PMSM will work under variable speed control, and two sets of machine data corresponding to two sets of different rotor speeds will be recorded and used for the calculation of the proposed estimation model. It shows that the proposed estimation model can be solved by using a conventional quantum genetic algorithm and can ensure the identifiability of all needed parameters owing to its inherent full rank feature. The performance test of the proposed estimation is then conducted on an interior PMSM, and it shows that parameters such as rotor PM flux linkage and winding resistance can be accurately estimated without the aid from nominal parameter values of PMSM. Therefore, the proposed method can be used for the condition monitoring of stator winding and rotor PMs. [ABSTRACT FROM PUBLISHER]

The maximum power point (MPP) tracking function allows one to extract the maximum power from the photovoltaic (PV) system in any operating condition. The perturb and observe technique is one of the mainly used algorithms aimed at this function. Its performances depend on the values of the two design parameters, which are the amplitude and the frequency of the perturbations that it applies in identifying the position of the MPP. In this paper, a method for the online optimization of the perturbation period is presented. It is based on the identification of the whole system, including the PV source and the dc/dc converter controlling it. The system impulse and frequency responses are evaluated by using the cross-correlation method. Such a technique, as well as the tracking algorithm, is effectively implemented by using a field-programmable gate array, and it is validated by means of simulation and experimental results. [ABSTRACT FROM PUBLISHER]

Surface-mounted permanent magnet synchronous machine with concentrated windings (cwSPMSM) is a high-performance drive machine and has been adopted in many applications. The difficulty of implementing its sensorless control at low and zero speeds is its multiple saliencies, which is much more significant than most other ac machines. The traditional decoupling methods provide successful results only under the condition that high-order saliencies are not stronger than half of the primary saliency. Furthermore, the behavior of the multiple saliencies is principally frequency dependent. Based on the characteristics of such machines, this paper proposes a multisignal injection method for realizing sensorless control. This method injects multiple high-frequency signals with different frequencies and magnitudes into the machine. Different frequency components in the response current signals are demodulated and then combined together to get the clear primary saliency signal, which is used to identify the rotor position. This new method was validated using a cwSPMSM at low speed. The experimental results proved the effectiveness and accuracy of the new method. [ABSTRACT FROM PUBLISHER]

This paper proposes a method that estimates the parameters of a velocity-controlled servo. A proportional–integral controller, which uses only position measurements, closes the loop. The proposed approach uses the steady-state response produced by steps and sine-wave signals; they do not produce high levels of vibration on the servo compared with random signals commonly used with the least squares algorithm; moreover, it relies on simple numerical calculations. The method, which is called in the sequel as the steady-state response method (SSRM), consists of two steps. The first step uses three constant reference inputs in order to identify a constant disturbance and the viscous and Coulomb friction coefficients of the servo. In the second step, the SSRM estimates the servo inertia using a sine wave plus a constant signal as a velocity reference input and employs the estimate of the viscous friction coefficient obtained in the first step. Experiments on a testbed employing a brushless servomotor allow comparing the results obtained using the SSRM and those produced by a standard recursive least squares method (RLSM). [ABSTRACT FROM PUBLISHER]