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In this paper, a novel repetitive control scheme is presented and discussed, based on the so-called B-spline filters. These dynamic filters are able to generate a B-spline trajectory if they are fed with the sequence of control points defining the curve. Therefore, they are ideal tools for generating online reference signals with the prescribed level of smoothness for driving dynamic systems, possibly together with a feedforward compensator. In particular, a continuous zero phase error tracking controller (ZPETC) can be used for tracking control of nonminimum phase systems but because of its open-loop nature it cannot guarantee the robustness with respect to modeling errors and exogenous disturbances. For this reason, ZPETC and trajectory generator have been embedded in a repetitive control scheme that allows to nullify interpolation errors even in nonideal conditions, provided that the desired reference trajectory and the disturbances are periodic. This paper is based on the results presented in the conference paper [L. Biagiotti, F. Califano, and C. Melchiorri, “Repetitive control of non-minimum phase systems along b-spline trajectories,” in Proc. IEEE 55th Conf. Decis. Control, 2016, pp. 5496–5501.], where asymptotic stability of the overall control scheme has been proved mathematically, but extends such results with an experimental validation based on a nonminimum phase system. Different models of the same physical system have been identified and used in the implementation of this model-based control scheme, allowing a real evaluation of the relationship between control system performance and model accuracy. [ABSTRACT FROM AUTHOR]

Presents IES society award recipients and distinguished reviewers for 2021. [ABSTRACT FROM AUTHOR]

The article announces winners of the periodical's 2020 outstanding papers including a paper by L. Guo on deep convolutional transfer learning network, and a paper by C. Li on modified neutral point balancing space vector modulation.

By combining radio-frequency identification (RFID) and paper-microfluidics technologies, a low-cost first-of-its-kind platform for comprehensive liquid sensing, i.e., the “smart test strip,” is presented, which enables portable wireless real-time liquid sensing with handhold devices (e.g., cell phones), and integration of various multifunctional electrical and chemical sensors, for numerous Lab-on-Chip applications, including manufacturing control, environmental monitoring, and point-of-care medical diagnostics. The fabrication of RFID tags and two types of microfluidics are accomplished by a single inkjet-printing process in a cost-effective environmental-friendly additive manufacturing approach, which makes possible the production of disposable, lightweight, and flexible sensing platforms. Taking advantage of the proposed smart test strips platforms, we demonstrate two proof-of-concept high-performance electrical sensors based on interdigitated electrode topologies: a resistivity-based sensor with a 1782 $\Omega$/( $\Omega$*m) sensitivity; nevertheless, the proposed permittivity-based sensor with a 15%/$\epsilon _r$ sensitivity, but the proposed integrated wireless platform, can facilitate the integration of even more chemical and electrical sensors. In addition, two on-strip antenna prototypes have been designed, optimized, and tested to work at 2.4 GHz and 13.56 MHz, respectively. Furthermore, the wireless interrogation of a complete proof-of-concept smart test strip is presented, which shows an excellent sensing resolution of 1.33 $\Omega$ over the range of 0–1371 $\Omega$. [ABSTRACT FROM PUBLISHER]

The remaining useful life (RUL) of transformer insulation paper is largely determined by the winding hot-spot temperature (HST). Frequently the HST is not directly monitored and it is inferred from other measurements. However, measurement errors affect prediction models and if uncertain variables are not taken into account this can lead to incorrect maintenance decisions. Additionally, existing analytic models for HST calculation are not always accurate because they cannot generalize the properties of transformers operating in different contexts. In this context, this paper presents a novel transformer condition assessment approach integrating uncertainty modeling, data-driven forecasting models, and model-based experimental models to increase the prediction accuracy and handle uncertainty. The proposed approach quantifies the effect of measurement errors on transformer RUL predictions and confirms that temperature and load measurement errors affect the RUL estimation. Forecasting results show that the extreme gradient boosting (XGB) algorithm best captures the nonlinearities of the thermal model and improves the prediction accuracy among a number of forecasting approaches. Accordingly, the XGB model is integrated with experimental models in a particle filtering framework to improve thermal modeling and RUL prediction tasks. Models are tested and validated using a real dataset from a power transformer operating in a nuclear power plant. [ABSTRACT FROM AUTHOR]

The rising cost of production testing for a system-on-a-chip (SoC) is one of the crucial matters to chip makers, due to long test time and costly automated-test-equipment. This paper proposes a spectral leakage-driven built-in self-test (BIST) scheme to precisely predict the nonlinearity of mixed-signal circuits in the loopback mode, thereby accomplishing cost-effectiveness (compared to previous BIST-based works). A digitally synthesized single-tone sinusoidal stimulus used for conventional harmonic testing is incoherently sampled by a device under test (DUT). The DUT output signal exhibits the correlation between the DUT harmonics and the spectral leakage introduced by the incoherent sampling. The DUT output signal is then fed to another DUT through a loopback path, so that the harmonics of a pair of DUTs are correlated with the spectral leakage on the loopback response; the magnitude of the spectral leakage is considered as a weighting factor on the harmonic magnitude of those DUTs. The correlation is quantitatively modeled as characteristic equations in , and postprocessing predicts the harmonics of the two individual DUTs, by simultaneously solving the characteristic equations using on-chip DSP core available in an SoC. Simulation and hardware measurements validated that this paper can be practically used for production testing by showing less than 0.3 and 0.6 dB of the prediction errors, respectively. [ABSTRACT FROM AUTHOR]

The fourteen papers in this special section focus on high performance light emitting diode (LED) drivers. In recent years, as the fourth generation of light source, light-emitting diodes (LEDs) have been widely used in many applications such as street lighting, automotive, and residential lighting because of their long lifetime, low maintenance requirement, and environment friendliness. As the performance of LEDs will be influenced obviously by the drivers, researchers have shown a growing interest in optimizing the drivers. Although these problems such as current balancing, eliminating electrolytic capacitor, and improving the topologies are not new, the high-frequency high-efficiency trend of LED drivers has given rise to additional challenges. These papers provide an insight on some of the newly emerging challenges and potential solutions to overcome those issues. [ABSTRACT FROM PUBLISHER]

Energy management strategy (EMS) is the key to the performance of fuel cell / battery hybrid system. At present, reinforcement learning (RL) has been introduced into this field and has gradually become the focus of research. However, traditional EMSs only take the energy consumption into consideration when optimizing the operation economy, and ignore the cost caused by power source degradations. It would cause the problem of poor operation economy regarding Total Cost of Ownership (TCO). On the other hand, most studied RL algorithms have the disadvantages of overestimation and improper way of restricting battery SOC, which would lead to relatively poor control performance as well. To solve these problems, this paper establishes a TCO model including energy consumption, equivalent energy consumption and degradation of power sources at first, then adopt the Double Q-learning RL algorithm with state constraint and variable action space to determine the optimal EMS. Finally, using hardware-in-the-loop platform, the feasibility, superiority and generalization of proposed EMS is proved by comparing with the optimal dynamic programming and traditional RL EMS and equivalent consumption minimum strategy (ECMS) under both training and unknown operating conditions. Results prove that the proposed strategy has high global optimality and excellent SOC control ability regardless of training or unknown conditions. [ABSTRACT FROM AUTHOR]

This paper proposes an intelligent modeling and optimization method to set up the smart energy hub model for the multiple energy system and optimize the operation of its associated energy hub model automatically. The core idea of the proposed method is to divide the complex energy hub model into several simple energy hub models. In this paper, the proposed computerized algorithm, which is applied to the smart energy hub model division, includes an energy loop intelligent searching method, a real nodes arrangement method, and a virtual nodes insertion method. One case study for the multiple energy system of a residential district in Guangzhou city is performed to validate the effectiveness of the proposed method. Compared with the traditional calculation method, the proposed method guarantees global optimal operation decision for the whole multiple energy system and the computational burden is significantly reduced. [ABSTRACT FROM AUTHOR]

DC microgrid has attracted more and more attention due to its unique characteristics, but the development of a dc microgrid is also facing some challenges, such as interruption and isolation of short-circuit fault currents. Therefore, the dc circuit breaker plays an important role in ensuring the normal work of a dc microgrid. In this paper, a novel bidirectional dc solid-state circuit breaker is proposed to realize the bidirectional flow of energy, which ensures the higher operating efficiency of the dc microgrid. Compared with other solid-state circuit breakers, the proposed novel topology has simpler structure, common ground, and fewer components. First, this paper makes a detailed analysis of the different working stages of the circuit when the fault occurs in combination with simulation results of the circuit topology. Then, the standard of selecting the components through the calculation and analysis of the circuit is given in this paper. Finally, the performance of the novel bidirectional solid-state circuit breaker is verified by experiments. [ABSTRACT FROM AUTHOR]

The development of the dc microgrid system on-board has promoted the development of the dc circuit breaker, but short circuit fault may cause serious damage to the system. Many problems exist in the traditional dc circuit breaker such as long periods of fault interruption, complex circuit structure, existing arc, low reliability, and low anti-interference. Aiming to solve these problems, a novel solid-state dc circuit breaker is proposed in this paper. First, this paper analyzes the working principle and process of the circuit in detail according to the results of the simulation based on Saber. Second, this paper gives the criteria on how to choose the parameters of the components correctly in the circuit. The concept of the maximum fault resistance according to the minimum fault current is also introduced. Moreover, this paper analyzes the influence of the minimum fault ramp rate on the circuit performance. Finally, experimental results verify that the performance of this novel solid-state circuit breaker agree with the theoretical analysis. [ABSTRACT FROM AUTHOR]

This special section on "Diagnosis and Prognosis for Complicated Industrial Systems?Part II? is the continuation of its previous part I (IEEE Transactions on Industrial Electronics, Vol. 63, Issue 4, 2016). The diagnosis and prognosis approaches can be roughly classified into two categories: 1) model-based and 2) data-driven approaches. The model-based approaches perform the diagnosis and prognosis relying on the physical models from first principles, whereas the data-driven approaches make full use of the process data to monitor the systems under consideration. Due to the increasing complexity of the industrial system, new challenges are encountered in diagnosis and prognosis of such complicated systems, and more efficient process monitoring approaches are needed. From this point of view, the papers included in this section are focused on the recent developments of diagnosis and prognosis for complicated industrial systems, providing novel ideas and referential directions for both the academic and industrial communities. [ABSTRACT FROM PUBLISHER]

Multiphase variable-speed drives and generation systems (systems with more than three phases) have become one of the mainstream research areas during the last decade. The main driving forces are the specific applications, predominantly related to the green agenda, such as electric and hybrid electric vehicles (EVs), locomotive traction, ship propulsion, “more-electric” aircraft, remote offshore wind farms for electric energy generation, and general high-power industrial applications. As a result, produced body of significant work is substantial, making it impossible to review all the major developments in a single paper. This paper therefore surveys the recent progress in two specific areas associated with multiphase systems, namely power electronic supply control and innovative ways of using the additional degrees of freedom in multiphase machines for various nontraditional purposes. [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 is the second-part paper of the survey on fault diagnosis and fault-tolerant techniques, where fault diagnosis methods and applications are overviewed, respectively, from the knowledge-based and hybrid/active viewpoints. With the aid of the first-part survey paper, the second-part review paper completes a whole overview on fault diagnosis techniques and their applications. Comments on the advantages and constraints of various diagnosis techniques, including model-based, signal-based, knowledge-based, and hybrid/active diagnosis techniques, are also given. An overlook on the future development of fault diagnosis is presented. [ABSTRACT FROM PUBLISHER]

An introduction is presented in which the editors discuss various reports within the issue including one on new emerging technologies in motion control systems, one on control of direct current (dc) motors, and one on linear actuator with high backdrivability.

Development of smart grid technologies has created a promising atmosphere for smart cities and energy trading markets. Especially, traditional electricity consumers evolve into prosumers who produce as well as consume electricity in modern power electric systems. In this evolution, the electric power industry has tried to introduce the notion of local energy markets for prosumers. In the local energy market, prosumers purchase electricity from distributed energy generators or the other prosumers with surplus electricity via a local power exchange center. For this purpose, this paper proposes joint geometric clustering and truthful auction schemes in the local energy markets. The proposed clustering scheme is designed for distribution fairness of the distributed energy generator for serving prosumers, where the scheme is inspired by expectation and maximization based unsupervised learning. Moreover, this paper proposes an auction mechanism for truthful electricity trading in a local energy market. In order to guarantee truthful electricity trading, the proposed auction mechanism is constructed based on the Vickrey–Clarke–Groves auction, which was proven to guarantee truthful operations. The Hungarian method is also considered in addition to the auction. The simulation results for the auction verify that the utilities of local market energy entities are maximized when the prosumers are truthful. [ABSTRACT FROM AUTHOR]

The evaluation of the state of transformer oil-immersed insulation based on the traditional methods becomes unreliable due to the nonuniform aging effect. To address this issue, a novel model for accessing the aging information of transformer oil-immersed insulation at the hotspot is proposed. In this article, frequency-domain spectroscopy (FDS) is selected as the carrier for characterizing the aging states of oil-immersed insulation. The multiobjective function for reversing the FDS of the hotspot is constructed based on the dielectric response equivalent circuit. Then, the nondominated sorting genetic algorithm with the multiconstraint scheme is proposed to solve the multiobjective function, by which the FDS data of the transformer hotspot region are acquired. The verification results indicate that the average standard deviation between the computed FDS of the hotspot region and the measured data is less than 0.26. The contribution of this article is in the exploration of the proposed model as a potential tool to extract the FDS data of the hotspot region. This will promote a more reliable aging evaluation of the transformer oil-immersed paper insulation at the hotspot. [ABSTRACT FROM AUTHOR]

A large class of three-phase electrical power systems possess symmetry conditions that make it possible to describe their behavior using single-input single-output transfer functions with complex coefficients. In such cases, an extended root locus method can be used to design control laws, even though the actual systems are multi-input multi-output. In this paper, the symmetric conditions for a large class of power systems are analyzed. Then, the root locus method is revisited for systems with complex coeffcients and used for the analysis and control design of power systems. To demonstrate the benefits of the approach, this paper includes two examples: 1) a doubly fed induction machine and 2) a three-phase LCL inverter. [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.

This paper proposes a new motion-copying system that is capable of preserving and reproducing the motion of human operators. In conventional motion-copying systems, when the operator moves quickly, the reproduced force does not correspond to the stored force, owing to the large accelerations. Therefore, the motion-copying system proposed in this paper uses acceleration responses provided by acceleration observers, so the force is reproduced more accurately. The acceleration observer is based on a disturbance observer; it estimates the acceleration of an actuator without acceleration sensors. Furthermore, the estimation performance is not affected by the mechanical impedance of the target environment. In this article, the proposed motion-copying system is compared with current methods, and the validity of the method is demonstrated. [ABSTRACT FROM PUBLISHER]

In this paper, the procedure for designing a sliding-mode controller for maximum power point tracking photovoltaic (PV) applications is proposed. It is applied to a single-ended primary inductor converter, thus to a fourth-order topology, but it can be extended to a wide class of converters suitable for PV applications. The reachability and existence conditions give rise to a number of design inequalities that add to the classical steady-state conditions in order to have the desired closed-loop converter's performances. The superiority of the control technique over the classical tracking methods is analytically demonstrated through the procedure proposed in this paper. The analytical results have been validated by means of both simulations and experiments. [ABSTRACT FROM PUBLISHER]

Short-term household electricity load forecasting is important for utility companies to ensure reliable power supplies. Traditional methods for load forecasting relied on historical records from one single data source and have limitations with insufficient or missing data. Recently, an emerging family of machine learning algorithms, multitask learning (MTL), has been developed and has the potential for load forecasting. By MTL, the electricity consumption data from multiple communities can be fused to improve forecasting accuracy. However, appropriate modeling of the relatedness to enable the between-community knowledge transfer remains a challenge. This paper proposes an improved MTL algorithm for a Bayesian spatiotemporal Gaussian process model (BSGP) to characterize the relatedness among the different residential communities. It hypothesizes on the similar impacts of environmental and traffic conditions on electricity consumption in improving short-term load forecasting. Furthermore, this paper proposes a low-ranked dirty model along with an iterative algorithm to improve the learning of model parameters under an MTL framework. This paper uses a real-world case study from two residential communities in Tallahassee, Fl, USA, to demonstrate the method effectiveness. The proposed method significantly outperforms state-of-the-art forecasting methods and effectively captures the relatedness to provide between-community knowledge transfer compared with other MTL methods. [ABSTRACT FROM AUTHOR]

This paper proposes an analytical model for the prediction of air gap magnetic field distribution for axial flux permanent magnet (AFPM) machine with various types of misalignments. The AFPM machine geometry is first transformed to a polar representation. Consequently, the subdomain model based on current sheet technique is developed. Then the stator coordinate system is chosen as reference coordinate to consider both static/dynamic angular and axis misalignments. The back electromagnetic forces and cogging torque are obtained accordingly based on Maxwell's equations. The results show that the proposed approach agrees with the finite-element method. The model is further validated by experiments under healthy, dynamic angular and axis misalignment conditions, which can validate the proposed approach. It turns out that the proposed approach can predict the performance of AFPM machines with types of misalignment quickly and effectively, which is greatly significant for further fault detection. [ABSTRACT FROM AUTHOR]

Modularization design has been widely used to reduce the detent force of permanent magnet linear synchronous motor (PMLSM). For modular PMLSM, ideally, unit motors are independent of each other and the detent force of the whole motor is the sum of that for unit motors; thus, the whole motor's detent force can be suppressed by adjusting the phase difference among the unit motors’ detent forces. But actually, there is coupling effect between adjacent unit motors, which has adverse effect on the detent force suppression. The coupling effect changes the magnetic field distribution, which is often neglected in current research. Hence, this paper carries out the research on magnetic field analysis and flux barrier design for modular PMLSM considering the coupling effect: the influence of the coupling effect on the air gap magnetic field is clarified through magnetic field analysis; the coupling force is isolated by finite element analysis, and then the effects of key parameters are discussed; the whole motor's detent force model considering the coupling effect is established, and an optimal design method of adjusting the flux barrier length is presented to suppress the detent force. Finally, a prototype is made to test the detent force characteristics with different flux barrier lengths, which verifies the research theory in this paper. The research results in this paper can provide guidance and reference for the design of modular PMLSMs. [ABSTRACT FROM AUTHOR]

In multiphase ac power systems, e.g., aircraft electric power systems (EPS), the proximity effect between the feeder cables can be severe and complex due to the tightly wired cable bundles. Simulation software is widely used to analyze such effects in order to guarantee proper system operation, but it is highly time consuming and requires extensive computational resources. Accordingly, this paper proposes an analytical calculation method for estimating the ac resistance of closely bundled power cables, namely multiconductor cables. Simulation and experimental tests are performed to validate the effectiveness and accuracy of the method, showing that the algorithm developed in this paper is easy to implement with a minimum accuracy of 92% in all the tests. It allows online estimation of cable resistances, which can be beneficial for advanced system monitoring and management to enhance safety and reduce losses. Aircraft EPS and cables are used as the example in this paper, while the proposed method is also applicable for other applications, such as shipboard EPS, renewable energy parks, or microgrids where multiconductor cables are used. [ABSTRACT FROM AUTHOR]

DC shipboard power systems have been considered as a promising solution for stricter environmental regulations on ships due to their main benefits in fuel savings with variable speed engines and easy integration of energy storage systems. In order to employ the dc solution in the shipboard power systems, the dc power systems have to be protected from a system fault with protection selectivity to minimize impacts of the fault or to avoid other undesirable situations in the system. For low-voltage dc shipboard power systems, a three-level protection has been proposed: 1) fast action (first)—bus separation by solid-state dc bus-tie switch; 2) medium action (second)—feeder protection by high-speed fuse; and 3) slow action (third)—generator–rectifier fault controls. This paper proposes a new method by means of additional bus capacitance added in main dc buses to help reliable operation of the three-level protection. The principle of the proposed method is introduced and the sizing of the additional bus capacitance is addressed in this paper. With the modeling of the dc shipboard power systems, the analyses of voltage drops for the bus separation failure and fault clearing time for the feeder protection are carried out to verify the proposed method. The results show that the proposed method not only mitigates the voltage drop for the bus separation failure, but also achieves the selectivity and the sensitivity for the feeder protection. [ABSTRACT FROM AUTHOR]

Finite set model predictive torque control (FCSMPTC) of induction machines has received widespread attention in recent years due to its fast dynamic response, intuitive concept, and ability to handle nonlinear constraints. However, FCSMPTC essentially belongs to the open-loop control paradigm, and unmatched parameters inevitably cause electromagnetic torque tracking error. In addition, the outer loop (i.e., the speed loop) based on a proportional-integral (PI) regulator cannot achieve optimal control between speed dynamic response and torque tracking error compensation. The traditional control paradigm is abbreviated as PI-MPTC. In order to solve the aforementioned problem, this paper proposes active disturbance-rejection-based model predictive torque control (ADR-MPTC). First, the influence mechanism of mismatched parameters on torque prediction error in PI-MPTC is studied, and then the performance of a traditional PI regulator used to compensate for torque prediction error is analyzed. Second, this paper introduces several parts of the proposed ADR-MPTC, including the design of the torque prediction error observer, nonlinear prediction error compensation strategies, an enhanced predictive torque control, and a simplified full-order flux observer. Finally, PI-MPTC and ADR-MPTC are studied experimentally. The experimental results show that compared with PI-MPTC, ADR-MPTC performs better in dynamic and steady states, and has stronger robustness. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of how to estimate the remaining useful life (RUL) for partially observed linear stochastic degrading systems with survival measurements. The motivation of this paper arises from two engineering facts—the measured degradation signals are taken from a survival degradation path and the degradation progression of the system is inevitably affected by multisource variability. To do so, we first revisit the linear degradation modeling framework driven by the Wiener process to account for the above two facts, and a new state-space model with non-Gaussian state transitions is constructed based on the constraint of survival measurements. Then, the particle filtering algorithm is applied to real-time estimate the non-Gaussian degradation state and random-effect parameter from measurements. Furthermore, we derive the probabilistic distribution of the RUL which can be real-time updated based on the available measurements. To apply the presented method, a two-stage estimation procedure based on particle expectation maximization algorithm is presented to determine and update the model parameters. The novelty of the presented method is to exclude the probability of failure before the current monitoring time and account for both impacts of the multisource variability and surviving degradation path on the RUL estimation. Finally, we demonstrate the proposed approach by a case study on gyros in the inertial platform. [ABSTRACT FROM AUTHOR]

A converter monitoring unit (CMU) that monitors the power modules of the converter in a multimegawatt test wind turbine is presented in this paper. A method of calculating the virtual temperature ($T_{v}$) of the power semiconductors is laid out. Contrary to previous work, the method enables $T_{v}$ characterization of the semiconductors by means of sampled data during normal operation of the converter, without the need for special calibration routines. The dynamic operation of the wind turbine and the large amount of data sampled in the CMU enables a statistical approach to generate reference data for $T_{v}$ calculations. The method was tested on machine side converter diodes of the test turbine, based on samples of collector-emitter voltage ($vce_{\text{on}}$) and current ($i_{c}$). Other temperature sensitive electrical parameters may also be used as input to the method. The CMU has been operating for more than a year in the test turbine, showing consistent data throughout the whole period. While the monitoring method and results are based on measurements on a single test turbine, the paper also link these to converter failure data from a large fleet of operating turbines. [ABSTRACT FROM AUTHOR]

Battery safety is one of the most crucial issues in the utilization of lithium-ion batteries (LiBs) for all-climate electric vehicles. Short circuit, overcharge, and overheat are three common field failures of LiBs. In this paper, online fault diagnosis for external short circuit (ESC) of LiB packs is investigated. The experiments are carried out to obtain and compare ESC characteristics of 18650-type NMC battery pack and single cell. Based on the analysis of experimental results, a two-step equivalent circuit model is established to describe the ESC process and an online model-based scheme is proposed to diagnose ESC faults of battery packs. The proposed scheme is evaluated by experimental data. The results show that it can effectively diagnose ESC faults in 3.5 s after their occurrences with the terminal voltage error less than 25 mV. The proposed scheme has shown great generalization ability. ESC faults of battery packs under different number of cells connected in series and unavailable current information can also be diagnosed at the terminal voltage error less than 48 and 60 mV, respectively. [ABSTRACT FROM AUTHOR]

In this paper, automated omnidirectional touch probe (ODTP) including adaptive tuning of the complex contour curve with optimal interpolation and motion planning based on a hybrid multiaxis robot (HMAR) with dual drive gantry-type machine (DDGM) are presented. It is called an “omnidirectional” because the 6-degrees of freedom ODTP in tool center point mode can always maintain normal direction towards the object contour curve. The new optimal solutions of the interpolation and motion planning are provided to tackle the small segments issues for a complex curvature object. We present an algorithm to maintain maximum acceleration, so that it generates smooth and efficient three-dimensional reverse object in a higher order complex contour trajectory composed of piecewise small segments. As a result, the constant moving speed is very important to certain applications, such as gluing, welding, and laser cutting, to preserve quality processing. The utilization of object symmetry, extension, and synthesis properties to simplify the process of performing reverse engineering is described. These methods reduce the time required for the touch probe to detect the points. The experimental implementation to demonstrate the success of this paper with video clip are presented. [ABSTRACT FROM AUTHOR]

In recent years, there has been an increasing interest in hand movements in various application fields. However, the human hand is too dexterous and only has limited space. Developing a proper method to monitor and capture hand motion is meaningful. Conventionally, an optical measurement method is widely used to capture hand gestures. Nevertheless, this method is costly and has the problem of line-of-sight shielding. In this paper, a sensor fusion algorithm is proposed to calculate a more accurate result by calculating acceleration, magnetic field strength, and angular speed from the inertial measurement unit (IMU). The core concept of this algorithm is based on the idea of feedback control. To validate the performance, the active rotary platform is set up to compare the measured results to the references directly. Most of the error results are less than 3°, and the standard deviations are less than 1°. The measurement results ensure the feasibility of the inertial measurement method with the sensor fusion algorithm proposed in this paper. [ABSTRACT FROM AUTHOR]

Active flow control by means of plasma actuators has potential advantages over conventional strategies, e.g., mechanical or hydraulic components may be replaced by lightweight, compact, fast response plasma actuators. In this paper, several designs of dielectric barrier discharge (DBD) plasma actuators are presented for aerospace applications and the focus is on the associated feedback control implementation. The interdisciplinary nature of aerodynamic feedback control with plasma, however, makes direct experimental demonstrating an outstanding challenge. Here, we propose a realistic experimental control implementation afforded by commercial off-the-shelf electric products and the major achievement is the detailed instruction (in both electricity and aerodynamics) and the successful demonstration of the closed-loop design in controlling the dominant modes from a cylinder flow setup. The essence of our approach is to drive DBD plasma actuations by a downstream sensor and excite aerodynamic velocity perturbations, which are further amplified on the shear flow from the cylinder, leading to airflow structures, such as vortex roll-up and randomization, which are measured by the downstream sensor to complete the whole loop. We benchmark our control approach by comparing to the predicted dominant frequencies of the controlled flow system, which can be achieved by the Barkhausen stability criterion after establishing the corresponding transfer function of the whole flow control system. Overall, this paper shall assist a host of new applications in aerospace applications in the near future. [ABSTRACT FROM AUTHOR]

This paper proposes a new force estimation approach based on the dither periodic component elimination (DPCE) Kalman filter and the disturbance observer (DOB) for realization of a fine sensorless force control system under the existence of static friction. A dither signal is inserted to the desired reference signal to eliminate the effect of static friction of a ball-screw system. The DOB estimates the periodical torque caused by dither signal. The DPCE Kalman filter uses the periodical torque for the estimation of the high-order torque responses to achieve the torque estimation with the elimination of periodic component. The problem of noise in the force sensation and the effect of static friction are reduced effectively by using the DPCE Kalman filter. Therefore, the bandwidth of the force sensing is increased and the wide band DOB is achieved. As a result, this paper has accomplished the fine sensorless force control realization. The force control experiments of a ball-screw system are carried out to verify the validity and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes and experimentally evaluates a method, which is able to measure the refrigeration capacity of compressors in extremely short times. Traditionally, the measurement of this parameter is done using specialized and expensive test rigs and can take up to 4.5 h for the measurement of a single compressor. To overcome this long measurement time, it is proposed to use a simpler measuring system, in which, instead of measuring the refrigeration capacity directly, the rate of pressure rise imposed by the compressor to a cylinder with fixed volume is measured. This paper shows that both variables have a high correlation factor when the test and compressor conditions are taken into account. As the correlation among the variables is nonlinear, it is not possible to use simple mathematical correlation functions, so neural networks are used for that purpose. By using the proposed method, the refrigeration capacity of a compressor can be determined in less than 7 s, which is approximately 0.05% of the time required by traditional methods. To guarantee the metrological reliability of the results presented by the proposed neural model, this paper proposes a novel approach based on the Monte Carlo and the bootstrap methods. [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]

The use of vision sensors for noncooperative rendezvous operations is attractive, especially for microspacecraft, because vision sensors are usually of small size, light weight, and low cost but provide rich information. However, vision-based rendezvous operations are extremely challenging. It is difficult for vision sensors to accurately measure the relative distance between a chaser spacecraft and a noncooperative target. In this paper, we propose a systematic angles-only navigation and control scheme for noncooperative rendezvous operations. The scheme is a hybrid framework that combines offline trajectory planning that integrates observability condition and online trajectory tracking based on nonlinear model predictive control. A navigation filter is used in the online trajectory tracking to estimate the relative motion by using only angle measurements. In the scheme, the chaser spacecraft can closely track the desired trajectory and simultaneously guarantee the estimation accuracy of the navigation filter. This paper provides a general and practical framework for angles-only rendezvous operations and the effectiveness is verified by extensive simulations. [ABSTRACT FROM AUTHOR]

This paper proposes a bioinspired reference model based fuzzy adaptive tracking control for active suspension systems. A general bioinspired nonlinear structure, which can present ideal nonlinear quasi-zero stiffness for vibration isolation, is adopted as tracking reference model. Fuzzy logic systems are used to approximate unknown nonlinear terms in nonlinear suspension systems. Particularly, a nonlinear damping is designed to improve damping characteristics of the bioinspired reference model. With beneficial nonlinear stiffness and improved nonlinear damping of the bioinspired reference model, the proposed fuzzy adaptive controller can effectively suppress vibration of suspension systems with less actuator force and much improved ride comfort, thus energy saving performance can be achieved. Finally, a quarter-vehicle active suspension system with considering payload uncertainties, general disturbance, and actuator saturation is provided for evaluating the validity and superiority of the bioinspired nonlinear dynamics based fuzzy adaptive control approach proposed in this paper. [ABSTRACT FROM AUTHOR]

In order to improve the pulp quality and to reduce the energy consumption, the fiber length distribution (FLD) is generally employed as one of the important technological indexes in the refining process. Considering that the traditional mean and variance of fiber length are unable to adequately characterize the non-Gaussian distribution properties, this paper proposes a novel geometric analysis based double closed-loop iterative learning control (ILC) method for probability density function (PDF) shaping of output FLD in the refining process. Primarily, a radial basis function (RBF) neural network with Gaussian-type is utilized to approximate the square root PDF in the inner loop, where the RBF basis function parameters (center and width) are tuned between any two adjacent batches by using an ILC law, and the subspace identification method can be applied to establish the state-space model of weight vector. Then, for the sake of accelerating the convergence rate of the closed-loop system, a geometric analysis based ILC method is adopted in the outer loop. Finally, both simulation and experiments demonstrate the effectiveness and practicability of the proposed approach. [ABSTRACT FROM AUTHOR]

Lithium-ion (Li-ion) batteries have been used in a wide variety of applications, ranging from portable electronics to electric vehicles. During repetitive charging and discharging, a battery's capacity fades due to electrochemical reactions such as solid electrolyte interphase growth. Li-ion batteries reach an end-of-life (EOL) point, after which using them is not recommended. However, some unhealthy batteries reach their EOL sooner than expected. A qualification test is usually conducted to evaluate the reliability of Li-ion batteries and classify unhealthy batteries, but this test requires several months. This paper develops a data-driven method to reduce the qualification time by detecting anomalies before EOL. This method detects an anomaly in the capacity fade curve of unhealthy batteries based on their capacity fade trend. Since the developed method detects anomalies of unhealthy batteries before EOL, the method is effective in reducing the time for the qualification test of Li-ion batteries. [ABSTRACT FROM AUTHOR]

Hydraulic reciprocating seals are dynamic sealing elements used in hydraulic actuators that play a very important role in preventing leakage. In this paper, a multisensor monitoring scheme including Fiber Bragg Grating sensors, fluid pressure sensors, and reciprocating displacement sensor was designed to analyze the leakage of hydraulic cylinder in a complete reciprocating work cycle. A complete hydraulic cylinder reciprocating cycle includes the extending process of piston rod, the full extension of piston rod, the retracting process of piston rod, and the full retraction of piston rod. Through an especially designed test bench, multisensor monitoring data were obtained under different wear degree of the piston seals. The analysis results show that there will be a “pull-back effect” in the return stroke of the piston seal. Therefore, leakage-based monitoring methods used in existing studies may not accurately reflect the wear of piston seals. If it is desired to detect wear of the piston seal during its actual operation, the contact pressure of the piston seal must be monitored. The paper also discusses the wear feature extraction method and how to deal with multiple features extracted from multisensor monitoring signals. [ABSTRACT FROM AUTHOR]

This paper proposes a modular on-road wireless power transfer system with interoperable power adjustment mechanism. The paper is anticipated to enhance the capability of on-road charging during the movement of automated guided vehicles (AGV), by which the traveling mileage is increased, while the battery volume is decreased. The system design includes an interoperable power adjustment technique based on the detected impedance, and the moving position of AGV can be, hence, comprehended so as to facilitate the adjustment of the output power from each transmission module in a more flexible way. Through the investigation of on-road charging efficiency, all of coil magnetic analysis, misalignment charging evaluation, system stress simulation, and resonant characteristics investigation are performed. Experimental results gained from software simulation and hardware realization are beneficial for AGV charging applications. [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]

Overhead lines are important parts of distribution networks at present. Assessment and prediction of their health status is of great significance to ensure the reliable operation of distribution network. This paper presents a method for assessing and predicting the health status of overhead lines based on health index (HI). First, the key features set of overhead lines including ontological and nonontological factors is determined. Then, a calculation method for evaluating HI based on analytic hierarchy process and technique for order preference by similarity to an ideal solution is proposed. Subsequently, the health assessment results of the actual 110 and 10 kV overhead lines located in China show that the proposed method could reflect the actual situation of overhead lines well. Finally, based on the proposed method, this paper takes two areas of China, Beijing and Nanjing, which have different climatic conditions, as research objects, and studies the influence of natural factors on the health status of overhead lines, respectively. Based on the analysis results, the health statuses of the overhead lines in the two regions are predicted. The research results of this paper can provide guidance for the operation and maintenance of overhead lines. [ABSTRACT FROM AUTHOR]

Industrial Wireless Sensor Networks (IWSNs) allow the use of battery-operated nodes to provide easy deployment, even in harsh environments, and are low maintenance. The optimal network design can be difficult because several constraints and requirements must be considered among all the power consumption. For this reason, specific approaches, also based on soft computing methods, can be applied in such a way as to further decrease the energy consumption in IWSNs. To this end, this paper proposes a fuzzy logic based mechanism that, according to the battery level and the ratio of throughput to workload, defines the sleeping time of sensor devices in an IWSN based on IEEE 802.15.4 protocol. A particle swarm optimization (PSO) algorithm is introduced to obtain the optimal values and parameters of the proposed fuzzy logic controller (FLC), i.e., optimizing the membership functions, by varying their range, to achieve the best results regarding the battery life of sensor nodes. This paper provides a detailed description of the FLC configuration, a logical analysis of the PSO algorithm for the derivation of best performance conditions values, and simulative assessments obtained through MATLAB simulations. [ABSTRACT FROM AUTHOR]

Although the concept of a multiphase drive system dates back to the middle of the 20th century, the initial pace of development was rather slow, as witnessed by the first two surveys of the area published in the beginning of this century. However, considerably new developments have resulted in the last decade of the 20th century and the beginning of this century, leading to an authoritative survey of the asymmetrical six-phase drive control and subsequently of the review of the complete area. This also initiated the organization and subsequent publication of the first IEEE Transactions on Industrial Electronics "Special Section on Multiphase Machines and Drives" in May 2008, which commenced with another survey paper, and that contained 12 original research papers. Since the publication of this Special Section in May 2008, the level of interest and pace of developments in the area have further accelerated and substantial new knowledge has been generatedwith an ever-increasing number of published research papers and reported new industrial applications. Such a trend has been emphasized in a recent paper. It therefore seemed appropriate to revisit the area and organize this Special Section as a sequel to the first one. The call for the Special Section papers resulted in 51 submissions, almost twice as many as the total back in 2008, thus confirming a substantial growth of the area. Indeed, the amount of new knowledge acquired since the publication of the first Special Section in 2008 has meant that it was not possible to provide a complete and thorough survey of the field in a single review paper. [ABSTRACT FROM AUTHOR]

The main objective of this two-part state-of-the-art paper called “Recent Advances in the Design, Modeling, and Control of Multiphase Machines” is to present latest contributions in the multiphase machines' field. The first part of this paper focuses on the recent progress in the design, modeling, and control, whereas the drive is in healthy operation. This second part presents relevant contributions in two not analyzed fields. The first is in relation with the use of the additional degrees of freedom of multiphase machines and the exploitation of their fault-tolerant capabilities without adding extra hardware. The second one analyzes multiphase generation, particularly in grid-connected wind energy conversion systems and stand-alone applications. Recent progresses are shown and open challenges and future research directions are discussed. [ABSTRACT FROM AUTHOR]

This paper suggests a cascade output voltage regulation strategy for dc/dc boost converters that embeds a robust inner-loop current controller against parameter variations. The contributions of this paper are divided into two parts. The first contribution is to present a simple and robust inner-loop current controller despite parameter uncertainties, guaranteeing the stability of the whole closed-loop system including the first-order internal dynamics. The second contribution is to show that an outer-loop proportional–integral regulator can stabilize the output voltage error without relying on any parameter information. Simulations and experiments using a 3-kW dc/dc boost converter showed that the proposed method offers satisfactory closed-loop performance in the presence of parameter uncertainties. [ABSTRACT FROM PUBLISHER]