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This paper investigates the use of large-scale wayside energy storage to reduce operational costs of ac electric traction power networks. This paper also introduces a method of estimating the battery capacity, based on manufacturer's charging and discharging specifications. The battery model is simple and easy to incorporate into load flow simulation algorithms. The concept and the battery model are validated through a feasibility study within Aurizon's heavy haul electric railway network. [ABSTRACT FROM AUTHOR]

Inclusion of power electronics allows increased controllability and stability in power systems. The simulation of such systems on a large-scale is challenging due to the presence of a large number of switches and nonlinear devices. This paper presents an advanced simulation algorithm to solve the aforementioned problem. The algorithm considers separation of differential algebraic equations (DAEs) on the basis of numerical stiffness and applies hybrid discretization algorithms to simulate the DAEs. The DAEs, in this paper, represent the nonlinear nonautonomous switched system dynamics of power systems. Stability analysis is performed on a general class of nonlinear nonautonomous switched systems to show the constraints under which the proposed algorithm is stable. To show the validity of the proposed algorithm, two case studies are considered: 1) single high-voltage direct current (HVdc) substation based on the modular multilevel converter (MMC); and 2) an example three-terminal MMC-HVdc system. Relaxation techniques are introduced to create a stable interface for the separated DAEs. The developed algorithms are also validated with PSCAD/EMTDC-detailed reference models. [ABSTRACT FROM AUTHOR]

Precise positioning of a robot plays an very important role in advanced industrial applications, and this paper presents a new kinematic calibration method based on the extended Kalman filter (EKF) and particle filter (PF) algorithm that can significantly improves the positioning accuracy of the robot. Kinematic and its error models of a robot are established, and its kinematic parameters are identified by using the EKF algorithm first. But the EKF algorithm has a kind of linear truncation error and it is useful for the Gauss noise system in general, so its identified accuracy will be affected for the highly nonlinear robot kinematic system with a non-Gauss noise system. The PF algorithm can solve this with non-Gauss noise and a high nonlinear problem well, but its calibration accuracy and efficiency are affected by the prior distribution of the initial values. Therefore, this paper proposes to use the calibration value of the EKF algorithm as the prior value of the PF algorithm, and then, the PF algorithm is used further to calibrate the kinematic parameters of the robot. Enough experiments have been carried out, and the experimental results validated the viability of the proposed method with the robot positioning accuracy improved significantly. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid battery parameterization technique for the purpose of battery state-of-charge (SOC) and state-of-power (SOP) monitoring in real time. The proposed technique is centered around an opportunistic initialization of a dual extended Kalman filter (DEKF) algorithm using pseudorandom binary sequence (PRBS) battery excitation. A second-order electrical equivalent-circuit battery model is used whose parameters are identified using a carefully designed 10-bit 10-Hz PRBS signal while the battery is in a zero- or low-current quiescent mode. The PRBS-identified resistive elements of the battery model are then utilized to provide an initial estimate for the battery's SOP. Once in load conditions, the DEKF algorithm is implemented recursively to provide an accurate estimate of the battery's parameters, SOC, and subsequently its SOP in real time. The experimental results obtained from an electrochemical impedance spectroscopy method give confidence to the performance of the proposed hybrid battery parameterization technique. [ABSTRACT FROM AUTHOR]

Finite control set model predictive control is an emerging technique for the control of power electronic converters. This paper introduces a simple and efficient predictive torque control (PTC) algorithm for induction motor drive. The proposed technique eliminates the need for flux-weighting factor for the conventional PTC. As a result, tedious offline tuning is no longer required. At the same time, the proposed technique ensures that the relative importance between torque and flux ripples is determined in an online fashion. Moreover, unlike the conventional method that needs to evaluate the cost function seven times (for two-level three-phase inverter case), the proposed method needs only to test four voltage vectors at each control sample which leads to a significant reduction in the computation time and switching frequency without sacrificing the performance. The effectiveness of the proposed method is demonstrated by both simulation and experimental results with comprehensive comparisons with the reported literature. [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]

High-dynamic-range (HDR) images require tone mapping to be displayed properly on lower dynamic range devices. In this paper, a tone-mapping algorithm that uses histogram of luminance to construct a lookup table (LUT) for tone mapping is presented. Characteristics of the human visual system (HVS) are used to give more importance to visually distinguishable intensities while constructing the histogram bins. The method begins with constructing a histogram of the luminance channel, using bins that are perceived to be uniformly spaced by the HVS. Next, a refinement step is used, which removes the pixels from the bins that are indistinguishable by the HVS. Finally, the available display levels are distributed among the bins proportionate to the pixels counts thus giving due consideration to the visual contribution of each bin in the image. Quality assessment using both quantitative evaluations and user studies suggests that the presented algorithm produces tone-mapped images that are visually pleasant and preserve details of the original image better than the existing methods. Finally, implementation details of the algorithm on GPU for parallel processing are presented, which could achieve a significant gain in speed over CPU-based implementation. [ABSTRACT FROM AUTHOR]

This paper develops data-driven Type-2 Takagi–Sugeno (T–S) fuzzy modeling and control for bilateral teleoperation with dynamic uncertainties and time-varying delays. The Type-2 T–S fuzzy model identified based on input–output data samples describes the nonlinear teleoperation system by a weighted sum of a group of linear local models, which offers a platform to design robust control algorithms by means of mature linear theories. The fuzzy-model-based four-channel control laws are proposed to guarantee the motion synchronization and enhance the operator's force perception for the environment when the time-varying delays and large dynamic uncertainties, especially the gravity of a heavy end effector of the slave, exist. Markov processes are applied to model the time delays. The stability of the closed-loop system is proved by using the Lyapunov–Krasovskii functions. All the conditions are expressed as linear-matrix inequalities (LMI). By using the MATLAB LMI toolbox, the optimized control gains for each of the fuzzy rules are derived to achieve the optimal performance. Finally, experiments based on an experimental platform consisting of two haptic devices prove the superiority of the proposed strategy through comparison with previous work. [ABSTRACT FROM PUBLISHER]

This paper presents a new concept in active damping techniques using a reduced model of an LCL -filtered grid-connected inverter. The presence of the LCL filter complicates the design of the inverter control scheme, particularly when uncertainties in the system parameters, especially in the grid inductance, are considered. The proposed control algorithm is addressed to overcome such difficulties using a reduced model of the inverter in a state observer. In this proposal, two of the three state variables of the system are obviated from the physical inverter model, and only the inverter-side current is considered. Therefore, the inverter-side current can be estimated emulating the case of an inverter with only one inductor, thus eliminating the resonance problem produced by the LCL filter. Besides, in the case of a distorted grid, the method allows us to estimate the voltages at the point of common coupling free of noise and distortion without using any phase-locked loop based synchronization algorithm. This proposal provides the following features to the closed-loop system: first, robust and simple active damping control under system parameters deviation; second, robustness against grid voltage unbalance and distortion; and third, an important reduction in the computational load of the control algorithm, which allows us to increase the switching frequency. To complete the control scheme, a theoretical stability analysis is developed considering the effect of the observer, the system discretization, and the system parameters deviation. Experimental and comparative evaluation results are presented to validate the effectiveness of the proposed control scheme. [ABSTRACT FROM PUBLISHER]

This paper deals with the stability problem of selected induction motor speed estimators in the case of the regenerating mode operation. Two effective solutions are taken into consideration: adaptive full-order observer and model reference adaptive system-type estimator based on the current flux model and stator current estimation. The two estimation algorithms are compared in detail, namely their mathematical models, theoretical stability analysis, unstable operation areas, and stability improvement methods. The speed adaptation mechanism is extended with an additional shift angle in both cases. The theoretical analysis and simulation test results are validated by wide experimental tests. [ABSTRACT FROM PUBLISHER]

This paper proposes the design of robust repetitive control with time-varying sampling periods. First, it develops a new frequency domain method to design a low-order, stable, robust, and causal IIR repetitive compensator using an optimization method to achieve fast convergence and high tracking accuracy. As such, a new stable and causal repetitive controller can be implemented independently to reduce the design complexity. The comprehensive analysis and comparison study are presented. Then, this paper extends the method to design a robust repetitive controller, which compensates time-varying periodic signals in a known range. A complete series of experiments is successfully carried out to demonstrate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a new control scheme for the enhancement of the low-voltage ride-through (LVRT) capability of doubly fed induction generators. The LVRT capability is provided by extending the range of operation of the controlled system to include typical postfault conditions. Controllers are designed simultaneously for both the rotor- and grid-side converters using a linear quadratic output-feedback decentralized control strategy. The nonlinear terms in the power system model are represented in this paper by an uncertain term derived from the Cauchy remainder of the Taylor series expansion. A genetic algorithm is used to calculate the bound on the uncertainty. The robust controller resulting from this design provides acceptable performances to enhance voltage and transient stability margins and thereby to limit the oscillations, the peak value of the rotor current, and the dc-link voltage fluctuations. The performance of the designed controller is demonstrated under different operating conditions by large-disturbance simulations on a test system. [ABSTRACT FROM PUBLISHER]

This paper investigates the data-based optimal control for a class of networked industrial processes with a double-layer architecture. Without knowing the dynamics of subsystems at the device layer, the index prediction function is constructed via the input/output signals, and radial basis function neural networks. The tuning laws for the index prediction function are obtained through the optimal control strategy. Then, by treating the network-induced phenomenon as random round-trip time delay and introducing the predictive algorithm, the compensation scheme is designed at the operation layer to dynamically decompose the setpoints. Finally, two simulation examples are given to further illustrate the effectiveness of the proposed compensation strategy. [ABSTRACT FROM AUTHOR]

This paper presents a small-signal analysis of an islanded microgrid composed of two or more voltage-source inverters connected in parallel. The primary control of each inverter is integrated through an internal current and voltage loops using proportional resonant compensators, a virtual impedance, and an external power controller based on frequency and voltage droops. The frequency restoration function is implemented at the secondary control level, which executes a consensus algorithm that consists of a load-frequency control and a single time delay communication network. The consensus network consists of a time-invariant directed graph and the output power of each inverter is the information shared among the units, which is affected by the time delay. The proposed small-signal model is validated through simulation results and experimental results. A root locus analysis is presented that shows the behavior of the system considering control parameters and time delay variation. [ABSTRACT FROM PUBLISHER]

This paper investigates the feasibility of a sensorless field-oriented control combined with a finite-control set model predictive current control (FCS-MPC) for an interior permanent-magnet synchronous motor. The use of an FCS-MPC makes the implementation of most of the existing sensorless techniques difficult due to the lack of a modulator. The proposed sensorless algorithm exploits the saliency of the motor and the intrinsic higher current ripple of the FCS-MPC to extract position and speed information using a model-based approach. This method does not require the injection of additional voltage vectors or the periodic interruption of the control algorithm and consequently it has no impact on the performance of the current control. The proposed algorithm has been tested in simulation and validated on an experimental setup, showing promising results. [ABSTRACT FROM PUBLISHER]

A main problem in autonomous vehicles in general, and in unmanned aerial vehicles (UAVs) in particular, is the determination of the attitude angles. A novel method to estimate these angles using off-the-shelf components is presented. This paper introduces an attitude heading reference system (AHRS) based on the unscented Kalman filter (UKF) using the three-axis attitude determination (TRIAD) algorithm as the observation model. The performance of the method is assessed through simulations and compared to an AHRS based on the extended Kalman filter (EKF). The paper presents field experiment results using a real fixed-wing UAV. The results show good real-time performance with low computational cost in a microcontroller. [ABSTRACT FROM PUBLISHER]

In this paper, the application of optimal state estimation and optimal state feedback algorithms for real-time active magnetic bearing control is treated. A linear quadratic Gaussian controller, consisting of an extended Kalman filter and an optimal state feedback regulator, is implemented. It is shown that this controller yields improved rotor positioning accuracy, better system dynamics, higher bearing stiffness, and reduced control energy effort compared to the conventionally used proportional–integral–differential control approaches. In addition, a method for compensating unbalance-caused forces and vibrations of the magnetically levitated rotor is presented which is based on the estimation of unknown disturbance forces. All results achieved in this paper are verified by means of measurements. [ABSTRACT FROM PUBLISHER]

This paper presents an adaptive method for tracking the amplitude, phase, and frequency of a time-varying sinusoid in white noise. Although the conventional techniques like adaptive linear elements and discrete or fast Fourier transforms are still widely used in many applications, their accuracy and convergence speed pose serious limitations under sudden supply frequency drift, fundamental amplitude, or phase variations. This paper, therefore, proposes a fast and low-complexity multiobjective Gauss–Newton algorithm for estimating the fundamental phasor and frequency of the power signal instantly and accurately. Further, the learning parameters in the proposed algorithm are tuned iteratively to provide faster convergence and better accuracy. The proposed method can also be extended to include time-varying harmonics and interharmonics mixed with noise of low signal-to-noise ratio with a great degree of accuracy. Numerical and experimental results are presented in support of the effectiveness of the new approach. [ABSTRACT FROM AUTHOR]

Maximum power point (MPP) tracking (MPPT) techniques are widely applied in photovoltaic (PV) systems to make PV array generate peak power which depends on solar irradiation. Among all the MPPT strategies, the incremental-conductance (INC) algorithm is widely employed due to easy implementation and high tracking accuracy. In this paper, a novel variable step-size incremental-resistance MPPT algorithm is introduced, which not only has the merits of INC but also automatically adjusts the step size to track the PV array MPP. Compared with the variable step-size INC method, the proposed scheme can greatly improve the MPPT response speed and accuracy at steady state simultaneously. Moreover, it is more suitable for practical operating conditions due to a wider operating range. This paper provides the theoretical analysis and the design principle of the proposed MPPT strategy. Simulation and experimental results verify its feasibility. [ABSTRACT FROM AUTHOR]

This paper presents a novel algorithm based on polynomial approximations (PAs) for an efficient error compensation of magnetic analog encoders (MAEs) in permanent-magnet synchronous machines (PMSMs) intended for electric vehicle (EV) propulsion. The proposed PA algorithm requires a negligible memory space compared to a very high-resolution look-up table (LUT). The use of polynomials allows compensating every possible input rotor position without carrying out an interpolation or a rounding to the nearest quantized value. The PA algorithm has been implemented to work in real time on a TM4 EV drive controlling an 80 kW PMSM. The performance of the algorithm has been validated at 6000 and 9000 r/min under +85 and {\pm}55 Nm of torque, respectively. The electromagnetic interference (EMI) effects have been minimized using a type-2 phase-locked loop (PLL). The proposed PA algorithm assisted with the PLL is capable of reducing the total position error to a range as small as {\pm}\text{0.2}^\circ$. The combination of these two algorithms is a promising solution for compensating the position error in quadrature analog encoders. The experimental results obtained with the 80 kW PMSM demonstrate the feasibility of low-cost MAEs for achieving high-performance field-oriented control (FOC) of PMSMs in EV drives. [ABSTRACT FROM PUBLISHER]

This paper deals with a specific explicit design of Generalized Predictive Control (GPC) for speed and position of three-phase Permanent Magnet Synchronous Motors (PMSM). Predictive algorithms are designed with single and double integrations with respect to step and ramp reference signals. Needful field weakening and current limitation are solved by specific procedures based on a local indirect tuning/amplification of the relevant GPC parameters. The proposed solution due to low computational demands is suitable for real applications. Designed algorithms and procedures are demonstrated by figures and oscillogram screenshots of representative variables measured on the 10.7 kW PMSM drive. [ABSTRACT FROM PUBLISHER]

In this paper, a model-based approach is proposed to detect and diagnose stator winding fault in the Brushless wound-field synchronous generator (BWFSG). The extended Kalman filter is used as a state and parameter estimation technique for the proposed model-based approach. The mathematical model of the BWFSG with stator winding fault is developed and simplified for online implementation. An experimental test-rig is used to acquire the required inputs for the developed state estimation technique. The estimated rotor currents and fault parameter are analyzed to identify key signatures for condition monitoring (CM). The harmonic components such as the second harmonic components of the estimated field and damper currents, and the rms value of the estimated fault parameters are identified as suitable signatures for winding fault and diagnose. Based on the identified signatures, a model-based CM algorithm is proposed and validated in real time. The validation results confirmed that the proposed algorithm is able to detect and diagnose winding inter-turn short-circuit faults in real-time reliably. [ABSTRACT FROM PUBLISHER]

In this paper, the design of a nonlinear observer-based fault diagnosis approach for polymer electrolyte membrane (PEM) fuel cell air-feed systems is presented, taking into account a fault scenario of sudden air leak in the air supply manifold. Based on a simplified nonlinear model proposed in the literature, a modified super-twisting (ST) sliding mode algorithm is employed to the observer design. The proposed ST observer can estimate not only the system states, but also the fault signal. Then, the residual signal is computed online from comparisons between the oxygen excess ratio obtained from the system model and the observer system, respectively. Equivalent output error injection using the residual signal is able to reconstruct the fault signal, which is critical in both fuel cell control design and fault detection. Finally, the proposed observer-based fault diagnosis approach is implemented on the MATLAB/Simulink environment in order to verify its effectiveness and robustness in the presence of load variation. [ABSTRACT FROM PUBLISHER]

The need to deal with rapid change in an environmentally and economically friendly manner has led to renewed interest in data-driven, online process optimization. Although various methods, such as economic model predictive control (EMPC), are available to achieve this goal, they require that the process model be available and relatively accurate and that there be no process changes. Recently, the focus has shifted to using economic key performance indices (KPIs) to design supervisory controllers to regulate the process. In order to accomplish this, accurate models of the highly nonlinear KPIs are needed. A solution to this problem is to develop a two-step control strategy consisting of a static, offline component and a dynamic, online component. This paper proposes the use of a linear, BILIMOD method combined with a self-partitioning algorithm for the static component and gradient-based optimization method for the dynamic component. In order to deal with process changes, the static model parameters are updated. The proposed new controller strategy is tested on the wastewater treatment process. It is shown that the proposed method can quickly and effectively achieve the desired optimal point with minimal disturbance to the overall process. [ABSTRACT FROM PUBLISHER]

The means of distributing dense passive radio-frequency identification (RFID) tags has been widely utilized for accurate indoor localization. However, they suffer a disadvantage on low localization precision due to the increasing interference of RFID tag collisions and the variation of behavior of tags. Current localization algorithms used in passive RFID location systems are mostly deterministic and have a limited capability on improving localization precision in a dynamic environment with uncertain sensor measurement. This paper investigates the feasibility of using particle filter technique as an efficient localization approach to deliver both relatively good accuracy and precision in dense passive RFID tag distribution applications. A position feature-based system model is first built to apply the typical particle filter technique in passive RFID location applications. Then, a new particle filter algorithm by using a moving direction estimation-based feature improvement scheme is proposed to enhance localization precision in a dense passive RFID tag environment. Experimental results show that the proposed method can provide relatively good accuracy and precision for passive RFID location applications, with an improved performance over the typical particle filter algorithm and a state-of-the-art deterministic method. [ABSTRACT FROM PUBLISHER]

The amount of distributed energy resources (DERs) has increased constantly worldwide. The power ratings of DERs have become considerably high, as required by the new grid code requirement. To follow the grid code and optimize the function of grid-connected inverters based on DERs, a phase-locked loop (PLL) is essential for detecting the grid phase angle accurately when the grid voltage is polluted by harmonics and imbalance. This paper proposes a novel low-pass notch filter PLL (LPN-PLL) control strategy to synchronize with the true phase angle of the grid instead of using a conventional synchronous reference frame PLL (SRF-PLL), which requires a d-q-axis transformation of three-phase voltage and a proportional–integral controller. The proposed LPN-PLL is an upgraded version of the PLL method using the fast Fourier transform concept (FFT-PLL) which is robust to the harmonics and imbalance of the grid voltage. The proposed PLL algorithm was compared with conventional SRF-PLL and FFT-PLL and was implemented digitally using a digital signal processor TMS320F28335. A 10-kW three-phase grid-connected inverter was set, and a verification experiment was performed, showing the high performance and robustness of the proposal under low-voltage ride-through operation. [ABSTRACT FROM PUBLISHER]

Prognosis of hybrid systems is a challenging problem because multiple faults may happen simultaneously at a mode where these faults have different detectabilities. In other words, at the fault-initiating mode, some of the faults are detectable while others are nondetectable. As a result, decision making based on only one observation of abnormal behavior is not reliable under this condition. This paper focuses on the development of a model-based prognosis framework for hybrid systems where a dynamic fault isolation scheme is proposed to facilitate the prognostic tasks. The degradation behavior of each faulty component is mode dependent and can be estimated by a hybrid differential evolution algorithm. Thereafter, the remaining useful life of the faulty component that varies with different operating modes is calculated by using both the estimated degradation model and the user-selected failure threshold. Experiments are carried out to validate the key concepts of the developed methods, and results suggest the effectiveness. [ABSTRACT FROM PUBLISHER]

This paper presents the dynamic behavior of multiport power electronic interface (MPEI) under load/source disturbances. A cascaded topology for MPEI is considered. This system is capable of power management for local generations, loads, and storage systems. Large-signal and small-signal models for MPEI are developed. These models are combined with corresponding control algorithms. Simulated and experimental transient responses to disturbances on each port are presented. The behavior of MPEI in response to each disturbance is analyzed. Simulation and experimental results are compared in order to verify the model. [ABSTRACT FROM PUBLISHER]

This paper presents the suppression of hunting in an ironless linear permanent-magnet synchronous motor (ILPMSM) driver system using a hunting compensator. In high-precision motion control servo systems, hunting induced by nonlinear elements such as friction and saturation will reduce the system performance. Hunting means that limit cycle occurs in the system, causing a series of sustained oscillations. The hunting compensator is designed based on the circle criterion to ensure system stability. The effectiveness of the proposed control scheme is verified by simulation and experimental results. The proposed algorithm is experimentally tested on an ILPMSM drive system, and the experimental results confirm the ability of the hunting compensation scheme to suppress the effects of hunting. [ABSTRACT FROM PUBLISHER]

This paper presents a new subharmonic analysis for buck converters with constant on-time control and ramp compensation. For constant on-time control, subharmonic oscillation can be eliminated by adding a compensation ramp with a fixed slope during the off time and a fixed level during the on time. Based on the inductor current information, the compensation ramp, and the charge variations of the output capacitor, the minimum amount of compensation ramp to avoid subharmonic is derived, and the effect of circuit propagation delay is quantified. A prototype of buck converter is built by using constant on-time control with ramp compensation. Experimental results demonstrate the detailed theoretical analysis. [ABSTRACT FROM PUBLISHER]

This paper presents a new formulation for direct power control (DPC) with several improvements over previous DPC formulations such as an exact discrete-time expression for the predicted power variations yielded by a given inverter switching state and the inclusion of the computing delay time in the model. Based on this formulation, a variable-frequency optimal DPC algorithm is presented that selects the inverter switching state that minimizes the power error. This algorithm shows an excellent precision in estimating power variations, therefore reducing power ripple and unwanted current harmonics, while retaining the fast dynamics inherent to variable-frequency DPC. This makes this algorithm suitable for interfacing distributed generation to microgrids, where a fast and accurate power control is desirable in order to perform voltage and frequency regulation. Additionally, studies are carried out regarding the average switching frequency and the influence of the model parameters on the algorithm robustness. [ABSTRACT FROM PUBLISHER]

This paper presents evaluations among the most usual maximum power point tracking (MPPT) techniques, doing meaningful comparisons with respect to the amount of energy extracted from the photovoltaic (PV) panel [tracking factor (TF)] in relation to the available power, PV voltage ripple, dynamic response, and use of sensors. Using MatLab/Simulink and dSPACE platforms, a digitally controlled boost dc–dc converter was implemented and connected to an Agilent Solar Array E4350B simulator in order to verify the analytical procedures. The main experimental results are presented for conventional MPPT algorithms and improved MPPT algorithms named IC based on proportional–integral (PI) and perturb and observe based on PI. Moreover, the dynamic response and the TF are also evaluated using a user-friendly interface, which is capable of online program power profiles and computes the TF. Finally, a typical daily insulation is used in order to verify the experimental results for the main PV MPPT methods. [ABSTRACT FROM PUBLISHER]

Permanent magnet linear motors are a type of linear motors that are generally used in precision motion control applications. However, the position of motor is easily disturbed by external force, disturbance, and variation in parameters of plant. Therefore, the construction of the high-precision linear motion system is a difficult task. This paper implements a modified disturbance observer and compensator, which includes a novel variable gain, to overcome the effects of unknown parameters of the motor, to minimize the effect of the disturbance, and to reduce the response time of the disturbance. This compensated linear motor is further controlled by the internal model reference control algorithm so that the position of the motor can be tracked with expected response precisely. The authors conducted the experiments and verified the feasibility of the high-precision positioning control. Compared with the case of normal disturbance compensator, the experimental results also illustrate the improvements of the novel variable gain, which reduces the response time toward the command signal and the external disturbance. [ABSTRACT FROM PUBLISHER]

A new distributed fusion filtering algorithm for linear multiple time-delayed systems is proposed. The multisensory distributed fusion filter is formed by the summation of local Kalman filters having time delays (LKFTDs) in both the system and measurement models. The proposed distributed filter has a parallel structure that enables processing of multisensory measurements; thereby, it is more reliable than the centralized version if some sensors turn faulty. The key contribution of this paper is the derivation of recursive error cross-covariance equations between the LKFTDs to compute the optimal matrix fusion weights. In the particular case of multisensory dynamic systems having time delays in only the measurement model, the obtained results coincide with the previous work of Sun. The high accuracy and efficiency of the proposed distributed filter are then demonstrated through its implementation on a vehicle suspension system. [ABSTRACT FROM PUBLISHER]

Tracking control is a fundamentally important issue for robot and motor systems, where smooth velocity commands are desirable for safe and effective operation. In this paper, a novel biologically inspired tracking control approach to real-time navigation of a nonholonomic mobile robot is proposed by integrating a backstepping technique and a neurodynamics model. The tracking control algorithm is derived from the error dynamics analysis of the mobile robot and the stability analysis of the closed-loop control system. The stability of the robot control system and the convergence of tracking errors to zeros are guaranteed by a Lyapunov stability theory. Unlike some existing tracking control methods for mobile robots whose control velocities suffer from velocity jumps, the proposed neurodynamics-based approach is capable of generating smooth continuous robot control signals with zero initial velocities. In addition, it can deal with situations with a very large tracking error. The effectiveness and efficiency of the proposed neurodynamics-based tracking control of mobile robots are demonstrated by experimental and comparison studies. [ABSTRACT FROM PUBLISHER]

In this paper, a new variable structure control law is proposed in order to obtain the shaft positioning of a symmetrical six-phase induction machine when phases are lost and when large variations of inertia occur. The algorithm based on a time-varying switching line guarantees the existence of a sliding mode since the beginning of the shaft motion. Indeed, the surface is first designed to pass through the initial representative point and, subsequently, to move toward a predetermined desired surface via shifting. With this algorithm, the induction machine behavior is exactly the same in healthy or in faulted modes when one or more stator phases are lost and whatever the mechanical shaft configuration is. The capacities of this control technique with a mechanical sensor have been tested in simulation and then validated experimentally on a specific setup. [ABSTRACT FROM PUBLISHER]

The direct torque control (DTC) technique of permanent-magnet synchronous motors (PMSMs) receives increasing attention due to its advantages in eliminating the current controllers and quicker dynamic response, compared with other motor control algorithms. However, high torque and stator flux ripples remain in the system when using DTC technologies. This means large stator voltage and current harmonic contents exist in the PM motors. Since the variation of motor electromagnetic torque is related to the voltages that are applied to the motor, by analyzing the relationships between stator flux, torque, and voltages, a PMSM torque predictive control scheme is proposed in this paper. In each digital signal processor cycle, the optimized voltage is utilized to reduce torque ripple, and the voltage vector angle is determined by the output of torque and flux hysteresis controllers. The proposed scheme is simulated and experimentally verified. Both simulation and experimental results have shown that low torque ripple and reduced stator current harmonics are achieved by using the proposed scheme. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel hybrid model for an internal combustion engine, with the power generated due to combustion as the input and the crankshaft speed fluctuations as the output. The individual cylinders of the engine are considered as subsystems for which a nonlinear model, based on the physical principles, is derived. The proposed model is linearized at an operating point, and a switched linear model is formed. The simulation results of the proposed model are validated by matching the results with the experimentally observed data. Using the properties of the validated model, it is shown that the crankshaft speed variations observed in the engine are a Markov process. A novel algorithm that is based on the Markov chain is proposed to detect the misfire in the spark ignition engines. In the ensuing engine rig experiments, an igniter misfire is introduced in the system and is successfully detected. The analysis of the data shows that the engine also has an air leakage in a cylinder (a developing misfire), which is experimentally confirmed later. [ABSTRACT FROM PUBLISHER]

Antilock braking systems (ABSs) are usually designed based on controlling the wheel slip ratio so as to maintain each wheel in a presumed stable region. Since the optimal slip ratio (which results in maximum tire–road friction) varies with the type of road, current methods are not efficient in the sense of achieving the shortest possible stopping distance. This paper introduces an efficient ABS, in which the brake commands directly maximize the longitudinal component of tire–road friction at each wheel of the vehicle independently. The tire–road friction is estimated using a torque balance equation at each wheel, and within those equations, real-time estimates of the effective radius of tire are used. A step-by-step algorithm for computing the brake commands that maximize the tire–road friction is also presented. Three challenging braking scenarios were tested using the comprehensive CarSim simulation environment. The results show that, in comparison to conventional ABS, our method significantly reduces the stopping distance and improves the vehicle stability. [ABSTRACT FROM PUBLISHER]

Requirements for system availability for ultrahigh reliability electronic systems such as airborne and space electronic systems are driving the need for advanced health monitoring techniques for the early detection of the onset of damage. Aerospace electronic systems usually face a very harsh environment, requiring them to survive the high strain rates, e.g., during launch and reentry, and thermal environments, including extremely low and high temperatures. Traditional health monitoring methodologies have relied on reactive methods of failure detection often providing little or no insight into the remaining useful life of the system. In this paper, a mathematical approach for the interrogation of the system state under cyclic thermomechanical stresses has been developed for six different lead-free solder alloy systems. Data have been collected for leading indicators of failure for alloy systems, including \Sn3\Ag0.5\Cu, \Sn0.3\Ag0.7\Cu, \Sn1\Ag0.5\Cu, \Sn0.3\Ag0.5\Cu0.1\Bi, \Sn0.2\Ag0.5\Cu0.1\Bi0.1\Ni, and 96.5\Sn3.5\Ag second-level interconnects under the application of cyclic thermomechanical loads. The methodology presented resides in the prefailure space of the system in which no macroindicators such as cracks or delamination exist. Systems subjected to thermomechanical damage have been interrogated for the system state and the computed damage state correlated with the known imposed damage. The approach involves the use of condition monitoring devices which can be interrogated for damage proxies at finite time intervals. The interrogation techniques are based on the derivation of damage proxies and system prior-damage-based nonlinear least square methods, including the Levenberg–Marquardt algorithm. The system's residual life is computed based on residual-life computation algorithms. [ABSTRACT FROM AUTHOR]

In this paper, a novel maximum power point tracking algorithm based on the perturb-and-observe (P&O) technique is introduced. The novelty of the approach is represented by the perturbation of more control variables, rather than just one. This allows the increase of the power extracted from the photovoltaic (PV) field as compared to the case of perturbation of a single control variable. The proposed technique overcomes the limitations of any existing tracking technique dedicated to PV arrays exhibiting a unique maximum power point, thus not only of the P&O approach, whenever dynamic constraints ensuring the correct system behavior must be fulfilled too. The proposed multivariable approach is described by means of its application to a single-stage one-cycle controlled PV inverter. [ABSTRACT FROM AUTHOR]