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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 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]

The concept of energy Internet (EI) is very promising due to its ability to integrate distributed generators and storage devices. Energy routers (ERs) enable highly efficient, reliable, and flexible power system operation. To dynamically control and optimize energy flow in EI, an extensive communication is required. Considering the diversity of generators and ERs, it is crucial that such communication is achieved in a standardized and interoperable way. In this paper, an improved energy routing algorithm and its impact in EI operation are demonstrated. Furthermore, a full communication solution is developed for ERs and their interaction with grid operator. This paper proposes an International Electrotechnical Commission (IEC) 61850 communication-based power routing algorithm in microgrids. Optimal selection of source and routing path is achieved according to the proposed algorithm and communicated to the ERs via IEC 61850 based communication. The effectiveness of the proposed IEC 61850 communication-based routing algorithm is verified on network emulation platform comprising of IEC 61850 emulator tools and network simulator tool. [ABSTRACT FROM AUTHOR]

This paper proposes and investigates a novel near-optimal finite-control-set model predictive control (NOP-MPC) algorithm to control the grid-connected, pulsewidth-modulator-driven voltage source converters with LCL filters. Exploiting the convex and elliptical paraboloid properties of the cost error, NOP-MPC adopts a systematic iterative algorithm within each control cycle to progressively synthesize finite sets of virtual voltage vectors (VVs) for the control optimization stage. The synthesis has the inherent features of respecting the converter voltage limits and converging the sets of VV candidates toward the global optimal point. The fixed-switching-frequency feature of NOP-MPC is expected to ease the LCL filter design. Effects of computational delay, pulsewidth modulation delay, and deadtime are considered and compensated successfully. A two-vector-variable cost function is used to actively damp the inherent LC resonance through an adjustable, weighting-factor-based damping level. This paper is substantiated by theoretical consideration, simulation and experimental results, parameter sensitivity study, and a comparative study with the standard finite-control-set model predictive control that uses only actual VVs. [ABSTRACT FROM AUTHOR]

In most multiobjective optimization problems of electrical machines, the weighted function method is used to convert them into single-objective optimization problems. This paper applies a kind of new multiobjective evolutionary algorithms (MOEAs), called adaptive multiobjective black hole (AMOBH) algorithms, to achieve effective multiobjective optimization of a tubular coreless linear permanent magnet synchronous motor (LPMSM). To reduce the computation cost of the MOEAs, a one-layer analytical model (AM) is presented for the tubular coreless LPMSM in this paper. The accuracy of the simplified one-layer AM is verified by comparisons with multilayer AM and finite element analysis (FEA) under different structure parameters. It is found that the simplified AM has good accuracy and can decrease the computation cost significantly. AMOBH algorithm is subsequently introduced. The optimal Pareto front with regard to thrust, copper loss, and permanent magnet volume are analyzed, and more diversified optimization results are provided. The final Pareto solution can be selected directly by practical physical values according to the application requirements. Finally, a prototype is fabricated for the selected design; its experimental results are provided and compared with those of the FEA results. [ABSTRACT FROM AUTHOR]

In this paper, we propose adaptive algorithms of gain tuning for Kalman filters and switching Kalman and $\mathcal {H}_{\infty }$ filters for discrete systems. Both of the gain tuning and switching rely on square means of innovations. This paper also provides stability analyses on time-varying Kalman filters and derives a sufficient condition for their asymptotic stability, on which our gain-tuning algorithm is based. It should be noted that the stability condition is available for even nonstabilizable systems having their uncontrollable poles on the unit circle. To illustrate those algorithms, we perform simulations using a harmonic oscillator model that is nonstabilizable and has its poles on the unit circle. Furthermore, we apply the algorithms to estimation of a ship's oscillation, particularly, with time-varying frequencies by simulations and model experiments. Consequently, all the results of stability analyses, simulations, and experiments have convinced that the algorithms are solid and effective. [ABSTRACT FROM AUTHOR]

This paper proposes the design and implementation of a model predictive control (MPC) algorithm for a micro-permanent-magnet synchronous motor (micro-PMSM)-based five-finger control system. First, the kinematic analysis and inverse kinematics of the micro-finger system are developed. The relationship between the rotor position of micro-PMSM and the position of the micro-fingertip is determined. Then, an MPC algorithm is designed to improve the performance of the micro-PMSM control system. An embedded system, cRIO-9024/9112, is used as a control center to execute the position control algorithm and force control algorithm. Experimental results show that the proposed system provides satisfactory transient responses, load disturbance responses, and tracking responses. In addition, the proposed five-finger system can grasp some objects by using the proposed force feedback control and a trajectory planning algorithm. [ABSTRACT FROM PUBLISHER]

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

As one of the most promising types of fuel cells, the proton exchange membrane fuel cells (PEMFCs) can be utilized in many applications. However, it still faces two main challenges before large-scale industrial applications, namely short lifetime and high costs. The aim of this paper is to establish an accurate online short-term prognostics method to help users extend the lifetime and reduce the cost of PEMFCs. First, we compare the short-term prognostics accuracy and computational efficiency of several different methods including the Elman neural network, the group method of data handling, the adaptive neuro-fuzzy inference system (ANFIS) with different fuzzy inference system creation strategies, and the wavelet decomposition approach. Test results show that the ANFIS with fuzzy c-means (ANFIS-FCM) strategy has the best short-term prognostics performance. Then, we propose an automatic parameter adjustment method for ANFIS-FCM by using the particle swarm optimization (PSO) algorithm. Test results show that the PSO algorithm can effectively adjust the parameters and achieve improved prognostics results. Finally, the proposed prognostics methods are verified on a PEMFC experimental platform. Experimental results show that the proposed methods have great potential for practical applications. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method for detection and isolation of wastegate (WG) faults in a turbocharged (TC) gasoline engine. This paper starts with a fault effect analysis on the WG faults, including WG stuck open and stuck closed, which is an early step in a detection strategy design. Then, a second-order sliding-mode observer (SOSMO) is proposed to capture the exhaust manifold dynamics. The observer uses experimentally validated engine models to estimate the WG position and a virtual force. The virtual force represents the external disturbances that disrupt the WG operation and enables the proposed SOSMO to estimate the WG position robust to the faults. Using this force, a detection and isolation algorithm is presented and experimentally verified on a modern TC gasoline engine. Test bench results show that the proposed strategy can successfully detect and isolate the two different WG faults. [ABSTRACT FROM PUBLISHER]

In this paper, a photovoltaic (PV) microinverter capable of operating in both island mode and grid-connected mode by means of a reconfigurable control scheme is proposed. The main advantage of control reconfiguration is that in grid-connected mode, the microinverter works as a current source in phase with the grid voltage, injecting power to the grid. This is the operation mode of most commercial grid-connected PV microinverters. The idea is to provide those microinverters with the additional functionality of working in island mode without changing their control algorithms for grid-connected mode, which were developed and refined over time. It is proposed that in island mode, the microinverter control is reconfigured to work as a voltage source using droop schemes. These schemes consist in implementing P/Q strategies in the inverters, in order to properly share the power delivered to the loads. The aim of the paper is to show that the proposed control reconfiguration is possible without dangerous transients for the microinverter or the loads. Simulation and experimental results on an 180-W PV microinverter are provided to show the feasibility of the proposed control strategy. [ABSTRACT FROM PUBLISHER]

Reactive power planning has always been a key research area in power distribution engineering; technically and economically. However, the problem needs to be revisited to consider several aspects of modern distribution systems, such as a high penetration level of renewable resources with intermittent nature; the microgrid concept and the possibility of system operation in grid-connected or isolated single microgrid mode, or isolated multiple microgrids; and probabilistic or hourly load profile. Motivated by these needs and considering all these aspects, this paper presents a generalized approach for probabilistic optimal reactive power planning in modern distribution systems. A new index is defined to probabilistically assess the success of microgrids in terms of real and reactive power adequacy and voltage limit constraints. Afterward, the reactive power planning is performed to reduce the annual energy losses of the grid-connected system and increase the defined microgrid success index. The problem formulation and solution algorithms are presented in this paper. The well-known PG&E 69-bus distribution system is selected as a test case, and through several sensitivity studies, the effect of optimization coefficients on the design and the robustness of the algorithm are investigated. A cost–benefit case study is also presented to determine the optimum total size of distributed reactive sources for the system under study. [ABSTRACT FROM PUBLISHER]

Time–frequency analysis (TFA) is considered as a useful tool to extract the time-variant features of the nonstationary signal. In this paper, a new method called demodulated high-order synchrosqueezing transform (DHST) is proposed. The DHST introduces a two-step algorithm, namely, demodulated transform and high-order synchrosqueezing method to achieve a compact time–frequency representation (TFR) while enabling the reconstruction of the signal from TFR. The performance of the proposed DHST method in this paper is validated by both the simulated and experimental signals including bat echolocation and a vibration signal. The results show that the proposed TFA method is more effective in processing the nonstationary signals with fast varying instantaneous frequency. [ABSTRACT FROM AUTHOR]

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]

To drive the machine tool along a curve, a commercial computer-aid design and computer-aided manufacturing system usually breaks the curve into a set of line segments. This paper provides a systematic way for generating smooth and fast machining velocity planning for contouring line segments in a machine tool. It is especially useful for mass manufacturing. In this paper, the particle swarm optimization algorithm is employed to optimize the design parameters. The algorithm allows for as many motion limits as possible, not only the limits along the path, but those on the axes, as well. It can automatically determine the existence of acceleration, constant speed, and deceleration zones of the S-curve velocity profile. Also, it can determine the optimal corner speed with a given corner error. The computation time of the algorithm is fast so that it allows for online operation. Simulation and experimental results demonstrate that machine tools using the proposed algorithm go fast where possible and slow down where needed, dramatically increasing the productivity. [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]

This paper proposes the energy-based controller (EC) incorporated with radical basis function (RBF) neural-network compensation (ECRBFC), which is used to swing up the Pendubot and raise it to its uppermost unstable equilibrium position. First, for the known dynamics model of the two-link arm, the EC is designed. In the EC, the singularity is successfully avoided by constructing an appropriate energy evaluation function. Second, as for the friction of the Pendubot, because of the time-varying characteristics, an accurate friction dynamics model cannot be known absolutely; thus, the RBF neural network is introduced to offset the bad effect of friction. Finally, in order to evaluate the performance of ECRBFC, the numerical simulations and the experimental results are given, and by comparing the results with that of other algorithms, it is found that ECRBFC proposed in this paper has better performance under the same conditions. [ABSTRACT FROM PUBLISHER]

A dual two-level voltage source inverter (VSI) can synthesize a three-level voltage space vector employing an open-end winding induction motor. Space-vector-based pulsewidth modulation (PWM) variants for this dual VSI are proposed in this paper that offer the dual advantage of limiting the switching power loss to a single VSI at all instants and also reducing the switching commutations in the dual VSI by 50%. The influence of different error volt seconds (affected with different PWM variants) on the motor phase current in the dual VSI is critically analyzed. To this end, two analytical approaches (one using error-voltage trajectory information and the other using switching state information of the dual VSI) are also proposed in this paper to predict the current trajectory and the ripple content in the drive system. Expressions for rms ripple current are developed with different PWM variants. The efficacy of the proposed analytical approaches to predict the current trajectory and the ripple content is confirmed from the experimental results. All the PWM variants are first simulated using MATLAB and verified experimentally by conducting tests on a three-phase open-end winding induction motor drive controlled with volts per hertz control. The implementation of the PWM algorithms only requires instantaneous magnitudes of three-phase reference voltages and completely avoids the sector identification and lookup tables. [ABSTRACT FROM PUBLISHER]

This paper presents an application of model predictive control to a variable-speed drive system operating in the multi-megawatt range. The variable-speed drive system comprises a synchronous machine fed by a line-commutated rectifier and a load-commutated inverter. The control task is to regulate the dc-link current, and hence the machine torque, to ensure the machine speed follows a given reference. The proposed control approach is model predictive control, where both the rectifier- and inverter-firing angles are considered as control inputs. The nonlinear model predictive torque controller has been implemented on an embedded system and applied in an industrial-scale pilot plant installation. The experiments show the successful operation of model predictive control on a plant with more than 48 MW power. [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]

One of the main technical challenges associated with the modular multilevel converter (MMC) for any application is the optimal control of its states. Optimal control of the MMC requires determination of the following: 1) an accurate MMC model, 2) the MMC states to be controlled and their corresponding reference trajectories, and 3) an appropriate controller type/form and its parameters. The aforementioned tasks lead to the formulation of a multistate control optimization problem, which is tackled in this paper. This paper enhances the accuracy of the existing MMC state-space model by including the following: 1) a piecewise affine insulated-gate bipolar transistor/diode model and 2) the impact of dead time. This paper also proposes a simulation-based gradient-descent optimization algorithm (cosimulation) to optimize the controller gains, given the controller type/form and the reference state trajectory. The theoretical proofs of the gradients required in the optimization algorithm are also provided. The aforementioned optimization algorithm is applied to a variable-frequency MMC-based drive system. Experimental results on a scaled-down prototype and simulation results on a real-world application are provided to validate the accuracy of the proposed MMC model and to show the effectiveness of the proposed optimization algorithm. [ABSTRACT FROM AUTHOR]

In this paper, simplified necessary and sufficient conditions are derived for H∞ static output feedback (OPFB). This paper also presents a numerically efficient solution algorithm to solve the coupled design equations for the bounded L2 gain static OPFB controller. No initial stabilizing OPFB gain is needed. The effectiveness of the proposed method is demonstrated through implementation on an industrial-type mass spring damper system. [ABSTRACT FROM AUTHOR]

A fusion of inertial navigation system and global positioning system (GPS) has been proposed to implement a robust navigation system for a transporter. In several cases, the GPS signals are not reliable to localize a transporter. In order to overcome this difficulty, a fault detection and isolation (FDI) algorithm has been used to detect and isolate the contaminated satellite signals. When the FDI algorithm is used alone, and the number of reliable satellite signals becomes less than four owing to GPS signal errors, the receiver cannot estimate the position precisely. In this paper, a modified FDI algorithm has been proposed and applied for the navigation of a transporter on a flat yard in order to avoid losing its positioning while it passes through a structured area. In the modified FDI algorithm, signal-to-noise ratio, measurement of quality indicator, and Doppler shift data are incorporated. In order to verify the effectiveness of the proposed algorithm, the navigation experiments have been conducted on a campus using a mobile robot to simulate the transporter on a shipbuilding yard and the results are demonstrated in this paper. [ABSTRACT FROM AUTHOR]

Force estimation methods enable robots to interact with the environment or humans compliantly and safely without additional sensing device. In this paper, we present a novel method for estimating unknown contact forces exerted on a robot manipulator. The force estimation method is divided into two steps. The first step is to identify a robot dynamics model. A parametric model is derived first based on rigid-body dynamic (RBD) theory. To improve the model accuracy, a nonparametric compensator trained with multilayer perception (MLP) is added to compensate for errors of the RBD model. The result is a semiparametric model that provides better model accuracy than either the RBD model or the MLP model alone. The second step is to construct a force estimation observer. A novel estimation method called disturbance Kalman filter (DKF) is developed in this paper. The design of DKF based on a time-invariant composite system model is presented. DKF can take both manipulator's dynamics model and disturbance's dynamics model into account. As with Kalman filter, it can provide robust and accurate estimation against uncertainty. Simulation and experimental results, obtained using a six-degrees-of-freedom Kinova Jaco2 manipulator, demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents a practical discrete-time fractional order terminal sliding mode (DFOTSM) control strategy for high-precision tracking tasks based on a linear motor. In particular, the practical parametric uncertainties involving sliding friction, uncertain payload, and disturbance in tracking tasks are considered in this paper. Combining Grünwald–Letnikov fractional order definition and terminal sliding mode technique, the proposed method synthesizes a novel DFOTSM control law to drive the sliding mode dynamics into the stable region in finite steps theoretically, even though the system is suffering from uncertainties and disturbances, and the motion on the surface can guarantee higher tracking precision than the conventional discrete-time terminal sliding surface by selecting suitable controller parameters. The theoretical analyses give out the guideline of parameter selection, and experiments are carried out on the linear-motor-based test platform to demonstrate that the proposed controller is easily implemented and can achieve high-precision tracking, fast response, and considerable robustness to uncertainties. [ABSTRACT FROM AUTHOR]

In this paper, we design a linear time-invariant two-input-two-output controller for the permanent magnet synchronous motor (PMSM). First, the nonlinear system of a PMSM is approximated using a linear system with structured uncertainties according to the geometric structures of PMSM. We then design a linear controller for the approximated linear system using a standard $\mu$-synthesis robust control method. The main contribution of this paper is that we recognize that the nonlinear PMSM can be reduced to a linear system to apply the mature modern control theory, without referring to classical PID control, thus largely reducing the design effort. The newly designed robust controller is not only easy to calculate, it is also easy to implement and requires less sensors. By virtue of modern robust control theory, it responds fast to exogenous inputs, and robust against parameter uncertainties as well. The applicability of the designed robust controller is both numerically simulated and experimentally verified on a TMS320 F28335 based control board, with performance further compared with the widely used field-oriented controller. [ABSTRACT FROM PUBLISHER]

As inverters are often used in critical applications, reliability is an important issue. In particular, the power electronic switches and gate drivers, the most essential components of the inverter, are vulnerable parts in real live operation. Therefore, this paper focuses on open-switch fault detection for multilevel inverters. When a single-switch open-circuit fault occurs in one of the power electronic switches, the algorithm can detect the fault and the switch that is causing it. The detection is worked out for both a linear resistive inductive load and an induction motor. The proposed algorithm is an extension of an already available finite-set model-based predictive control algorithm. Therefore, no extra hardware or measurements are required. This paper also discusses a suggested method for reconfiguration after fault detection. Computer simulation and experimental verifications validate the proposed methods. [ABSTRACT FROM PUBLISHER]

This paper studies the optimal control problem for large-scale systems with unknown parameters and dynamics. By using robust adaptive dynamic programming (RADP) method, a decentralized optimal control design is given for large-scale systems with unmatched uncertainties. The convergence of the proposed RADP algorithm and the asymptotic stability of the closed-loop large-scale system are studied rigorously. Finally, a numerical example of a large-scale power system is adopted to illustrate the effectiveness of the obtained algorithm. [ABSTRACT FROM PUBLISHER]

As a step toward the self-commissioning of the next generation of ac drives, this work deals with an automatic procedure for the identification of the inverse-\Gamma equivalent circuit of inverter-fed induction motors (IM) at standstill. The algorithm, cut out for the modern microprocessors combines different test signals with the aim of extracting and mapping the magnetic model nonlinearity. As a key feature, the procedure uses the existing inverter as a precise voltage probe to get the complete parameter set usually required by the advanced control of ac drives. The theoretical investigations are supported by experimental results and are reported in the paper. A distinguishing characteristic of the proposed method among the other existing solutions is the accuracy of the results confirmed by the comparison with finite-element analysis tools and tailored to the laboratory IM prototypes. [ABSTRACT FROM PUBLISHER]

The application mapping problem is an NP-hard combinatorial optimization problem in network-on-chip (NoC) design. Applications of size ($n$ $ >$ 30) cannot be solved optimally by an exact algorithm in reasonable time, and the evolutionary algorithms have drawn the attention of NoC researchers. In this paper, we propose a new effective optimization method based on the discrete particle swarm optimization framework, which includes the novel principles for representation, velocity computing, and position-updating of the particles. In our proposed method, particles are allowed to swing between elite and regular pools, and a simple local search procedure is applied on elite particles to exploit the promising solutions. Extensive computational studies using standard benchmark instances and task graphs for free (TGFF) random instances reveal that the proposed optimization algorithm is able to attain the best results, and thus competes very favorably with the previously proposed heuristic approaches. A stability analysis and the two-sided Wilcoxon rank sum tests are also presented to shed light on the robust behavior of the algorithm. [ABSTRACT FROM AUTHOR]

An accurate and efficient model for selective catalytic reduction (SCR) systems plays an important role in diagnosis and control of diesel-engine after-treatment systems. In this paper, we investigate the data-driven modeling of SCR systems and outlet NO $_{x}$ concentration estimation based on the developed data-driven model and the algorithm of unbiased finite impulse response (UFIR) filtering. The structure used for the data-driven model is an autoregressive exogenous (ARX) model and the method of partial least square is utilized to identify the parameters of the corresponding ARX model. Moreover, the approach of fuzzy c-means is employed to partition the data and derive multiple local linear models with a better performance on approximating the system nonlinearities. Finally, the algorithm of UFIR filtering is adopted to estimate the outlet NO $_{x}$ concentration due to its strong robustness without the statistics of process and measurement noises. The performance of proposed approaches on SCR systems is validated with simulations based on experimental data. In addition, comparisons show the improvement of the adopted algorithm on the estimation accuracy. [ABSTRACT FROM AUTHOR]

In this paper, a precise control algorithm for active brake pressurization based on our self-designed electro-booster (Ebooster) brake system is proposed. First, the inertial characteristic, nonlinear hydraulic characteristic, and dynamic friction characteristic of the Ebooster system are analyzed and modeled. We design a cascade control algorithm including a pressure loop by the feedforward-feedback method, position loop by the triple-step nonlinear method, and current loop by the current distribution, flux-weakening, and current control, and consider the system's nonlinear and dynamic characteristics. We evaluate the controller through simulations and hardware in the loop tests, and the results demonstrate satisfactory accuracy and response speed. [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]

This paper is devoted to the industrial practices and theoretical approaches for detection and isolation of quality-related multiple faults in large-scale processes. In contrast to the previous schemes, the main innovations are as follows: 1) it is the first time a hierarchical detection and isolation framework for quality-related multiple faults in large-scale processes is developed; 2) a combination method of adaptive kernel canonical variable analysis and Bayesian fusion for real-time and hierarchical detection of varying and unknown quality-related multiple faults is presented; and 3) a robust sparse exponential discriminant analysis algorithm for accurate isolation of multimode quality-related multiple faults is proposed. Finally, the whole framework is applied to a typical large-scale process, i.e., hot strip mill process, where the performance and effectiveness are further demonstrated from real industrial data. [ABSTRACT FROM AUTHOR]

Due to the private ownerships of various resource and equipment in a microgrid, it is essential to design a proper power trading strategy for a mutually satisfying partnership among suppliers and users. This paper proposes a novel discounted min-consensus algorithm to discover the optimal electrical power trading route with minimal power loss in dc microgrids. The proposed distributed algorithm only requires information exchange among neighbors based on a peer-to-peer communication protocol, which is robust to topology changes and enables parallel data processing to achieve fast decision-making. The convergence and optimality of the proposed discounted min-consensus algorithm are guaranteed by rigorous analysis. Simulation results of the 10-bus, 68-bus, and 1062-bus power systems demonstrate the effectiveness of the proposed distributed optimal routing strategy for electrical power trading. Moreover, the proposed novel distributed algorithm sheds light on applying distributed control and optimization approaches to solve problems arising in artificial intelligence. [ABSTRACT FROM AUTHOR]

Grid condition information extraction is very important for converter control systems in order to perform special functionalities, especially grid support during low voltage ride through (LVRT). As a part of grid condition detection, the voltage sag detection provides information about the start of a fault and obtains indices that determine the behavior of the whole control system of the converter during a fault. Estimation of the magnitude of voltage sag (MoVS) is essential for obtaining proper grid support. This paper presents an algorithm for MoVS (as defined in IEEE Std 1564-2014) prediction. The existence of correlation between magnitudes of a set of low-order harmonics during the transient of voltage PQ events with MoVS is for the first time determined and statistically proved. Correlation is mathematically formulated and the prediction function is obtained on the basis of real grid measurements. Complete algorithm for magnitude prediction is explained, and its possible application in distributed generation control is prese-nted. It is shown that significant time gain is achieved, which may be used as an additional benefit in LVRT operation. [ABSTRACT FROM AUTHOR]

In this paper, a novel guided motion planning method is proposed for snake-like robots, which decomposes the configuration space into the fiber space and the gait space, with the motion planned in the low-dimensional fiber space, so as to avoid the dimensionality curse problem. More specifically, first, based on geometry mechanics, the kinematic connection is derived, which successfully maps the motion in the fiber space into that in the gait space. According to the kinematic connection, a novel bidirectional motion control method for snake-like robots is proposed to improve the flexibility that can produce not only the forward locomotion but also the backward locomotion additionally. The convergence of the bidirectional motion control method is proved based on averaging theory. Then, the motion planning problem is solved in the low-dimensional fiber space, for which the HJB equation is used to generate the optimal control in the fiber space to avoid the well-known curse of dimensionality. Finally, combining the bidirectional motion control and the motion planned in the fiber space, a guided motion planning method in the configuration space is proposed. Numerical simulations and experiments based on the real snake-like robot platform are performed, whose results demonstrate that the proposed algorithm is valid and robust. [ABSTRACT FROM AUTHOR]

This paper proposes a promising cost-effective phase current detection scheme for different multiphase switched reluctance motors (SRMs) by employing the multiplexed current sensors. According to the phase current characteristics for the odd-numbered multiphase SRM with m phases and even-numbered multiphase SRM with n phases, the phase windings are specially divided into (m + 1)/2 and n/2 groups, respectively, and each group shares one Hall-effect current sensor. By building the matrix functions to express the relationship between phase currents and sensor currents, the phase currents can be calculated in real time with a simple algorithm and fewer current sensors. Compared to existing phase current detection schemes, the developed method has high universality and can achieve the intact phase current detection without losing any excitation, freewheeling, and demagnetization currents. Moreover, the presented method is easy to implement without any converter change and pulse injection, which shows great potential for massive industrial application to avoid the voltage penalty, current distortion, higher switching loss, and further electromagnetic interference issues. The simulation and experiments are carried out to validate the feasibility and effectiveness of the developed phase current detection scheme. [ABSTRACT FROM AUTHOR]

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

This paper focuses on developing new navigation algorithms for cooperative unmanned vehicles in costmap environments. The vehicles need to plan optimal paths subject to mutual diversity. We first propose three algorithms that can find optimal paths in costmap environments with improved computational efficiency. The proposed algorithms include 1) the scaled A*; 2) the A* plus-plus; and 3) the A* plus-plus with reconstruction. The heuristics of the three algorithms are proved admissible. Then, a diversity-based path planning algorithm is proposed based on the new A star variant algorithms for multiple vehicles to plan diverse paths for risk management. In particular, we propose an iterative segment planner, which generates a set of spatially diverse paths by iteratively planning segments of all paths in a sequential manner. To prevent the generation of spatially close paths, the iterative segment planner utilizes a penalty function to increase the cost of paths that lie near the route(s) assigned to other vehicles. By varying the penalty gains, different degrees of path diversity can be achieved. Some illustrative examples are provided to compare the performance of the proposed algorithms with the standard A* and Dijkstra's algorithms, and show the effectiveness of the proposed diversity-based path planning algorithm. [ABSTRACT FROM AUTHOR]

In this paper, an optimal design for nuclear magnetic resonance and magnetic resonance imaging based multiphase flow meter is presented. The overall apparatus consists of an electromagnetic coil surrounded by 12 rings of Halbach arrays of 12 magnets each. Both the coil and the magnets arrays were simultaneously and optimally designed using a three-dimensional (3-D) finite-element method (FEM) package and particle swarm optimization algorithm. The aim of the design is to uniformly prepolarize the hydrogen spins of the atoms composing the flowing fluid, with a predefined static magnetic field, before they enter into the measurement area within which they are homogenously polarized with a perpendicular ac magnetic field. Cheap and lightweight hardware are the other two design parameters of the design. Results of extensive 3-D simulations of the design indicate that an optimized and homogenous static magnetic field distribution could be achieved within an area of 40 mm diameter and 606 mm length. In addition, using a multiturn coil of 6 cm diameter and 2000 turns, an ac magnetic field of 13 mT amplitude and 112 ppm homogeneity could be achieved, which is enough to handle fluids flowing at speed of up to 2 m/s. Experimental validation, which was done using newly constructed two Halbach arrays of cuboid and trapezoid magnet elements, respectively, indicates a good match with FEM simulations. [ABSTRACT FROM AUTHOR]

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

This paper formulates an optimization-based algorithm for the compensation of unwanted current terms by means of distributed electronic power converters, such as active power filters and grid-connected inverters. The compensation goal consists in achieving suitable load conformity factors, defined at the source side and within a feasible power region in terms of the power converter capability. Based on the measured load quantities and on a certain objective function, the algorithm tracks the expected source currents, which are thereupon used to calculate proper scaling coefficients and, therefore, the compensation current references. It improves the power quality at the point of common coupling and enables full exploitation of distributed energy resources, increasing their efficiency. The compensation is based on a decoupled current decomposition and on an optimization-based algorithm. In this paper, the strategy is applied to nonlinear and unbalanced three-phase four-wire circuit, under nonsinusoidal and asymmetrical voltage conditions. The steady-state and dynamic behaviors have been analyzed by theoretical, simulation, and experimental results. Furthermore, the proposed approach is also compared to other compensation strategies showing its effectiveness. [ABSTRACT FROM AUTHOR]

This paper presents predictive current-mode control for a single-phase high-frequency transformer-isolated dual-active bridge dc-to-dc converter. The predictive control algorithm increases the bandwidth of the current loop of the converter which enables tracking of the current reference within one switching cycle. The paper further demonstrates that the application of the predictive control algorithm can remove transient dc offset from the current in high-frequency isolation transformer within one switching cycle. Direct control of the converter current protects the transformer from saturation even at transient conditions. The control algorithm has been implemented on an experimental setup and transient tests have been performed to validate controller performance. Since the predictive control algorithm is dependent on the measured value of the leakage inductance of the transformer, a compensator has been implemented to improve the parameter insensitivity of the proposed controller. [ABSTRACT FROM PUBLISHER]

The ability of a robot vision system to capture informative images is greatly affected by the condition of lighting in the scene. This paper reveals the importance of active lighting control for robotic manipulation and proposes novel strategies for good visual interpretation of objects in the workspace. Good illumination means that it helps to get images with large signal-to-noise ratio, wide range of linearity, high image contrast, and true color rendering of the object's natural properties. It should also avoid occurrences of highlight and extreme intensity unbalance. If only passive illumination is used, the robot often gets poor images where no appropriate algorithms can be used to extract useful information. A fuzzy controller is further developed to maintain the lighting level suitable for robotic manipulation and guidance in dynamic environments. As carried out in this paper, with both examples of numerical simulations and practical experiments, it promises satisfactory results with the proposed idea of active lighting control. [ABSTRACT FROM PUBLISHER]

This paper revisits the circular formation control problem proposed in [G. S. Seyboth, J. Wu, J. Qin, C. Yu, and F. Allgower, “Collective circular motion of unicycle type vehicles with nonidentical constant velocities,” IEEE Transactions on Control of Network Systems, vol. 1, no. 2, pp. 167–176, Jun. 2014.] for multiple nonholonomic mobile robots with nonidentical constant forward speeds. Specifically, we study two types of circular formations: First, a circular motion with synchronized/balanced phase configuration; and second, a concentric circular formation with both circular orbits control and phase synchronization/balancing. Existing works on the above problems utilize a fixed-gain control input, which increases the workload of the engineers for selecting favorable algorithm parameters. To reduce this workload, we study the above problems from a new perspective by utilizing optimization methods, based on which two variable-gain control algorithms are proposed such that much greater flexibility on the selection of algorithm parameters is acquired. The global convergence properties of the proposed algorithms are analyzed under some mild assumptions. Both simulations and field experiments are presented to validate the effectiveness of the proposed formation algorithms. [ABSTRACT FROM AUTHOR]

Sensor-based application of environmental monitoring has a huge potential but the only limiting factor is that it is battery operated and thus energy constraint. Energy harvesting is a boon as it helps in environmental monitoring applications. Thermoelectric energy harvesting (TEH) has been explored by researchers and they have proposed the various architecture of TEH systems. The temperature difference is the critical factor for developing TEH system as it directly affects the output energy of the system. In this paper, a novel thermoelectric energy harvester is proposed for wireless sensor network (WSN) based environmental monitoring application, which considers the change in temperature in a cyclic manner. Phase change material (PCM) used is of high latent heat and an intelligent algorithm is proposed that manages the heat energy and maintain the temperature gradient up to 2  $^{\circ }$ C. This results in heat flow in interchangeable orders during day and night, thus improving the overall output electrical energy. The intelligent algorithm and optimized framework of TEH system considering parameters affecting TEH facilitates the maintenance of $\Delta T$. The system is fabricated and implemented using water as PCM. The overall energy output achieved is 10.23 J/g, which is sufficient to achieve a perpetual lifetime of WSN-based environmental monitoring systems. [ABSTRACT FROM AUTHOR]

In this paper, a fast and practical method is proposed for open-circuit (OC) fault diagnosis (FD) in a three-phase quasi-Z-source inverter (q-ZSI). Compared with the existing fast OC FD techniques in three-phase voltage source inverters, this method is more cost-effective since no ultrafast processor or high-speed measurement is required. Additionally, the method is independent of the load condition. The proposed method is only applicable to Z-source family inverters and is based on observing the effect of shoot-through intervals on the system variables during switching periods. The proposed algorithm includes two consecutive stages: OC detection and fault location identification. When both stages of the OC FD algorithm are done, a redundant leg is activated and utilized instead of the failed leg. The accuracy of the proposed method is confirmed by the experimental results from a low-voltage q-ZSI prototype. [ABSTRACT FROM AUTHOR]

To deal with the registration of infrared and visible image with significant difference in contrast and large geometric distortion in electro-optical pod, a robust and fast registration method by using common structural features is proposed in this paper. First, to adjust the scales of input images, a geometric transformation model is built based on parameters of the cameras. Then, a global structural feature extraction strategy is developed by using phase congruency and significance ranking space. Those features are not only invariant to the scale and contrast but also embody the structural of the input images maximally and uniformly. Finally, to enhance the robustness to contrast, blurring, and incompleteness of structures, an adaptive feature matching method with kernelized correlation filter is presented. Experimental results demonstrate that the proposed method has better performance than several state-of-the-art methods in robustness and precision, and also confirm its validity in the real environment. [ABSTRACT FROM AUTHOR]

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]