Search

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]

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]

Redundant manipulators have been investigated and employed in various fields, and its trajectory tracking is of much importance in the field of robotic control. In this article, a model predictive control (MPC) scheme for the trajectory tracking of redundant manipulators is constructed, which minimizes the tracking error, velocity norm, and acceleration norm simultaneously. The commonly used trajectory tracking schemes for redundant manipulators, such as the minimum-velocity-norm scheme and minimum-acceleration-norm scheme, handle joint limits at different levels by introducing additional parameters, which reduces the feasible region of decision variables. In contrast, the proposed scheme directly considers these limits at three different levels, without reducing the feasible region of decision variables. In addition, to compensate for the deficiencies of most existing algorithms in noise environments, an error-summation enhanced Newton (ESEN) algorithm is proposed for solving the MPC scheme. Through theoretical analysis, it is determined that the proposed ESEN algorithm has a small steady-state error under noise conditions. Finally, in contrast with the comparative trajectory tracking schemes, as determined through computer simulations and experiments, the proposed MPC scheme solved by the ESEN algorithm not only enables the redundant manipulator to perform the trajectory tracking task in excellent fashion, but also offers advantages of high efficiency, fast responsiveness, and strong noise tolerance. [ABSTRACT FROM AUTHOR]

Sensors provide insights into the industrial processes, while misleading sensor outputs may result in inappropriate decisions or even catastrophic accidents. In this article, the Bayesian estimation algorithms are developed to estimate unforeseen signals in sensor outputs without tuning. The optimal Bayesian estimation method is first derived by incorporating a Gaussian distribution specifying potential unmodeled dynamics into the measurement equation. Since its performance depends on tuning parameters, an iterative Bayesian estimation algorithm is developed using the variational inference technique. Specifically, an inverse Wishart distribution is introduced to describe the predicted covariance of abnormal signals. We then estimate it together with the other independent Gaussian distributions to conditionally approximate the joint posterior distribution, by which the effects of tuning parameters can be replaced adaptively. Testing the proposed algorithms through a simulated electromechanical brake model and a real experimental system shows that the proposed algorithm can satisfactorily estimate additive sensor faults online and services as a sensor monitor that simultaneously provides the locations and magnitudes of faulty signals without tuning. [ABSTRACT FROM AUTHOR]

The optimization of Litz-wire gapped-core high-frequency transformer (LGHT) for LLC converters is a challenging task owing to application requirements and complex relationship between multiple design-objectives and constraints. This necessitates the use of multiobjective optimization-techniques to obtain tradeoff design-solutions; however, these are cumbersome methods that require many iterations and resources. Therefore, this article proposes an improved and efficient Pareto-optimization method for LGHT-design. It combines multiobjective whale-optimization algorithm and analytical core-geometry factor model based core and winding selection method while keeping in consideration the LLC converter requirements. The proposed method utilizes benefits of both method-types; it obtains the best LGHT-design in shortest-time and minimum-iterations with optimized volume and losses, integrated magnetizing-inductance, and satisfied thermal-limit. The method is made resource-efficient by carefully selecting independent and necessary LGHT design-parameters as optimization variables keeping in view interdependency among the parameters and LLC converter performance; these include peak-flux-density, LGHT design-frequency, core-window utilization-factor, core-material, and core-shape. For best design-selection, supplementary decision functions (total-loss and loss-and -volume-product) are used together with the objective functions (core-loss, winding-loss, and total-volume) to enhance the optimal Pareto-solutions’ diversity and to assist designers in decision-making. The method is proven via comparison with existing benchmark-methods and validated through performance demonstration of LGHT-prototype in 400 Vdc-to-12 Vdc LLC converter. [ABSTRACT FROM AUTHOR]

The mass transferring (repairing) equipment of mini/micro LED chip has high requirements for the advanced machine vision detection algorithm to ensure that the chips can be identified in a high-efficiency and high-precision manner.The motivation of this article is to propose a novel chip repairing vision measure (NCR-VM) algorithm with the speed and accuracy intelligent adjustable property in XY-axes to handle this issue. An intelligent judgment mechanism is constructed by the NCR-VM algorithm. The information is analyzed and judged during the algorithm operation to ensure that each step of the algorithm is properly planned. Then, the reliability and robustness of the algorithm are analyzed and verified in the theory. Finally, the chip repairing visual detection experiments are carried out, including key parameters tests, comparative experiments with other five representative vision algorithms, and robustness tests. The experimental results uniformly prove that the NCR-VM algorithm has a great advantage in detection accuracy and speed (0.53 $\mu$ m, 124.21 ms) and the developed algorithm has a better stability, which is validated under different illumination conditions. In addition, the NCR-VM algorithm is of great robustness under the disturbances of the pepper–salt noise with a density up to 0.6/pixel and the Gaussian noise with the largest variance of 0.5. [ABSTRACT FROM AUTHOR]

With the extensive application of active magnetic bearings (AMBs), the robust multiobjective optimization of the structure seems to be a priority. However, it is a challenge due to the high dimension and huge computational cost of finite-element analysis. In this article, a robust multi-objective optimization method is proposed to pursue good performance for a three-pole AMB. To increase the efficiency of the optimization process, the Kendall correlation coefficient is applied to assist in determining the sensitivity. The parameters are divided into three layers, and a three-level multiobjective optimization structure is established. Meanwhile, Kriging model is employed to improve the optimization efficiency. The selection of the final solution in Pareto curves is always an issue. The proposed optimization structure can only ensure the performance of the AMB, rather than robustness. Thus, a robust solution selection method is proposed based on the climbing algorithm. The robustness can be easily shown in the Pareto curve obtained through the optimization structure. The final solution is selected with good robustness in terms of suspension force and force ripple. The experimental results based on a prototype are provided to verify the effectiveness of the proposed optimization method. [ABSTRACT FROM AUTHOR]

This article proposes a new active power flow controller for dc–dc multiactive bridge (MAB) converters with minimum current point tracking (MCPT) to minimize conductive losses. Unlike recent efforts in this area, the proposed controller does not require look-up table for implementation nor complex nonlinear converter modeling and is not circuit parameter dependent. The proposed control scheme is also generalized for MAB converters with any number of ports and is derived in per unit form to enable standard implementation for buck/boost/unity-gain operating modes. The proposed MCPT algorithm is based on an innovative adaptive perturb and observe tracking of the minimum current point (MCP) on the MAB converter current characteristic plane. This MCP is tracked while active power regulation is simultaneously achieved based on a new phase shift decoupler to enable direct regulation of individual port active power. Detailed design procedure of the proposed controller is presented and extensive simulation is carried out to validate the effectiveness of the proposed MCPT closed loop controller. Experimental implementation is also provided to substantiate the simulation results. [ABSTRACT FROM AUTHOR]

For grid-connected voltage-source converters (VSCs), it is commonly required that its power outputs can track the power references given by operators under various grid conditions. To achieve this objective, this article presents a Lyapunov-based nonlinear power control algorithm. The system dynamics is developed in the stationary frame, which facilitates the design of the control algorithm in the absence of phase-locked loops that may cause instability issues in ultraweak grids. A virtual resistance is then introduced to allow fast power tracking performance and relax the requirement on accurate system parameters. Furthermore, to simplify the control design, the quasi-stationary line impedance model is applied, based on which a nonlinear control algorithm that only utilizes the output voltage and current information is developed. Afterward, the stability of the closed-loop system is analyzed via the Lyapunov theory. The theoretical results illustrate that the power regulation goal can be achieved and the VSC can maintain synchronization with the power grid. Finally, experimental results demonstrate the effectiveness of the proposed control algorithm under temporary grid faults and various short-circuit ratios. [ABSTRACT FROM AUTHOR]

This article presents a control scheme for an omnidirectional wireless power transfer system. A system architecture is proposed to implement the current amplitude control. To focus power transfer towards targeted loads, a smart detection algorithm for identifying the positioning and orientation of receiver devices based on the input power information is presented. The system efficiency is further improved by a maximum efficiency point tracking function. A novel power flow control with a load combination strategy to charge multiple loads simultaneously is explained. Finally, the proposed control scheme is successfully applied to an example 6.78 MHz omnidirectional wireless charging bowl system. The experimental results confirm the accuracy of the load detection algorithm and validate the power flow control strategy. [ABSTRACT FROM AUTHOR]

This article is concerned with the design of a super-twisting algorithm (STA) based sliding mode controller for permanent magnet synchronous motor (PMSM) speed regulation system under the self-triggered mechanism. By using the strict Lyapunov function approach, it is shown that the tracking error converges to an ultimate domain within the finite-time sense under the proposed self-triggered STA. A feasible self-triggered strategy is designed for both cases with and without external perturbation. Moreover, a nonlinear optimization problem is formulated in terms of the tradeoff between the ultimate domain and the communication burden. The optimized STA gains are obtained by solving the above-formulated optimization problem via a particle swarm optimization algorithm. Finally, the applicability of the proposed self-triggered STA for PMSM is verified by simulation and experiment results. [ABSTRACT FROM AUTHOR]

Inpipe inspection technology is an important method for pipelines detection, and the smooth running speed of the inner detector is a prerequisite for ensuring the quality of inspection. Since the inner detector runs as a black box in the pipeline, it is difficult to observe the current speed state, and the energy source it carries is limited. To overcome the above problems, this article proposes an interval dynamic matrix control algorithm with additional margin (AM-IDMC). First, as an optimized control strategy, interval control can suppress disturbances and reduce energy consumption with smaller actions, which is a novel way to solve the speed control problem of the inner detector. Second, the dynamic matrix control can effectively predict the speed of the inner detector. At the same time, the additional margin method can avoid the actual speed from violating the expected interval to the greatest extent, allowing the controller to intervene in the control as soon as possible. Third, an adaptive penalty coefficient and interval reference trajectory are designed to ensure the rapidity and stability of the speed adjustment process. This article also systematically and completely builds the speed control model of the inner detector from pressurizing station and discharge device. The results have demonstrated that the AM-IDMC algorithm has good performance and has a good effect on the speed control of the inner detector. [ABSTRACT FROM AUTHOR]

Periodic disturbances occur during repetitive operations, and compensation for the periodic disturbances is an important issue to realize precise machine works because the periodic disturbances deteriorate the control precision. In addition, the periodic disturbance becomes a frequency-varying periodic disturbance when the periodicity of the operations changes, which makes the compensation difficult. To eliminate the frequency-varying periodic disturbance, an adaptive periodic-disturbance observer (APDOB) was proposed. However, the APDOB has a problem that the design of the APDOB is complicated with six design parameters. This article proposes a differential evolutionary algorithm with local search that optimizes the six design parameters of the APDOB for the optimal frequency-varying periodic disturbance compensation. The proposed method based on a memetic algorithm framework can explore globally using the differential evolutionary algorithm and explore locally using the local search including the Lévy flight. Moreover, the proposed method can reduce the number of the design parameters. [ABSTRACT FROM AUTHOR]

Four-level neutral-point-clamped (NPC) multilevel converter topologies have been proposed and actively studied for low/medium voltage applications. As an inherent issue with these topologies, the voltage balancing of dc-link capacitors is challenging, especially when it operates as a single-end inverter/rectifier with high power factors and high modulation indexes. This article proposes a closed-loop voltage balancing algorithm that is effective and simple to implement with regular level-shifted carriers for a four-level NPC converter, e.g., a π-type converter. This approach is based on the redundant level modulation (RLM), which utilizes one additional voltage level in one switching cycle to gain extra controllability of the capacitor voltages without distorting/undermining the fundamental-frequency output voltage. An algorithm based on analytical expressions and logical operations is developed to achieve a dynamic closed-loop voltage balancing with RLM. The proposed method is effective over the full span of linear modulation indexes (M = 0 ∼ 1.15) and power factors. The proposed algorithm is validated and evaluated in both simulations and experiments. [ABSTRACT FROM AUTHOR]

Hierarchical reinforcement learning (HRL) can learn the decomposed subpolicies corresponding to the local state-space; therefore, it is a promising solution to complex robotic assembly control tasks with fewer interactions with environments. Most existing HRL algorithms often require on-policy learning, where resampling is necessary for every training step. In this article, we propose a data-efficient HRL via off-policy learning with three main contributions. First, two augmented MDPs (Markov decision processes) are reformulated to learn the higher level policy and lower level policy from the same samples. Second, to learn higher level policy that leads to efficient exploration, a softmax gating policy is derived to determine the lower level policy for interacting with the environment. Third, to learn the lower level policies via off-policy samples from one lower level replay buffer, the higher level policy derived by the option-value network is adopted to select the appropriate option for learning the corresponding lower level policy. The data-efficiency performance of our algorithm is validated on two simulations and real-world robotic dual peg-in-hole assembly tasks. [ABSTRACT FROM AUTHOR]

In this article, a modified reduced order generalized integrator-based frequency-locked loop (MROGI-FLL) is proposed for controlling the interfacing inverter of the grid-tied photovoltaic system to mitigate the harmonics. Additionally, a fuzzy tuned PID controller is integrated in the proposed scheme to minimize the steady-state error, which results the voltage level is maintained constant at the common dc-link bus terminals. The MROGI-FLL is designed to evaluate the three-phase reference currents by extracting the fundamental constituents from the load currents and grid voltages. The proposed MROGI-FLL has several advantages such as better harmonic mitigation capability, control adaptivity, adaptive frequency and phase, grid synchronization, and low computation burden. The proposed reference current generator is simulated on MATLAB/Simulink platform under steady state and various transient conditions.The comparative study of proposed scheme is compared with the existing and adaptive control techniques, which assures the potency of the proposed control algorithm in-terms of harmonic mitigation, dc-offset rejection, frequency variation, and computation burden. Finally, the experimental results are verified through an experimental platform by using the dSPACE evaluation kit and both the simulation and hardware characteristics are well justified to the IEEE-519 standard limits. [ABSTRACT FROM AUTHOR]

In this article, we develop a novel multiobjective controller to regulate the power converters of a class of dc microgrids connected to nonlinear constant power loads and linear resistive loads. The suggested control approach uses the nondominating sorting binary genetic algorithm (NSBGA-II) to directly design the on/off switching signal of the converters without using the pulsewidth modulation technique. The multiobjective controller minimizes the tracking error of the dc bus voltage and at the same time tries to reduce the total number of switching actions. Thereby, the developed controller tracks the desired reference with a reduced converter switching action and power loss by using a proper Pareto solution. Moreover, by employing the NSBGA-II algorithm, it is feasible to involve the switching frequency in the design procedure to enhance the performance. Exploiting the binary genetic algorithm instead of the conventional genetic algorithm (GA) turns a continuous surface searching into a binary one, which not only makes it more compatible with the nature of the power converter control but also decreases the online computational burden. To illustrate the superiority of the proposed approach, real-time OPAL results are provided. [ABSTRACT FROM AUTHOR]

Roll-to-roll (R2R) printing systems provide a continuous process of thin, flexible material (called web) through printing cylinders, where the required patterns are printed on the web. Tension fluctuation caused by the upstream register control quantity may lead to the downstream register errors. Thus, it is a challenge to develop a register control algorithm to remove the couplings among the angular velocity, tension fluctuation, and register error. In this article, the register error is defined as the marks position misalignment between the first and the current printing unit. Based on the definition, a mathematical model of registration is derived. Based on the model, a fully decoupled proportional-derivative (FDPD) control algorithm is proposed to completely remove the coupling between the upstream register control quantity and the downstream register error. Simulations and experiments are carried out to demonstrate the effectiveness of the proposed control algorithm. Compared with the other compensation control methods, FDPD is a complete decoupling control method, which can control the register errors of all downstream printing units (more than seven printing units) within $\pm 0.05$ mm, even if the upstream printing unit has a 3 mm register error. Decoupling formula included in the FDPD control is a first-order compensation formula, which is easy to implement in the actual industry. Furthermore, the proposed FDPD can deal with the situation where two adjacent gravure cylinders have different web lengths, which is not taken into account by other control methods. [ABSTRACT FROM AUTHOR]

Fault localization is one of the most important issues for the modular multilevel converters (MMC) consisting of lots of switches. This article proposes a switch open-circuit fault localization strategy for the MMC, where a sliding-time window (STW) based features extraction algorithm (FEA) is proposed to extract the features of the MMC based on the feature relationship between neighboring STWs. Based on the extracted features, the fault in the MMC can be easily located with the 2-D convolutional neural networks. The proposed STW-FEA-based fault localization strategy can constructs concise low-data-volume features samples for the MMC in both time domain and frequency domain, and accordingly it can locate the fault with short time and high accuracy for the MMC. In addition, it does not require the creation of complex mathematical models and manual setting of empirical thresholds. Simulation and experiment are conducted, and the results confirm the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

This article proposes, a novel obstacle avoidance algorithm for a mobile robot based on finite memory filtering (FMF) in unknown dynamic environments. To overcome the limitations of the existing dynamic window approach (DWA), we propose a new version of the DWA, called the finite distribution estimation-based dynamic window approach (FDEDWA), which is an algorithm that avoids dynamic obstacles through estimating the overall distribution of obstacles. FDEDWA estimates the distribution of obstacles through the FMF, and predicts the future distribution of obstacles. The FMF is derived to minimize the effect of the measurement noise through the Frobenius norm, and covariance matrix adaptation evolution strategy. The estimated information is used to derive the control input for the robust mobile robot navigation effectively. FDEDWA allows for the fast perception of the dynamic environment, and superior estimation performance, and the mobile robot can be controlled by a more optimal path while maintaining real-time performance. To demonstrate the performance of the proposed algorithm, simulations, and experiments were carried out under dynamic environments by comparing the latest dynamic window for dynamic obstacle, and the existing DWA. [ABSTRACT FROM AUTHOR]

In spite of recent advancements in millimeter-wave (mmWave) communication technologies, the limited beamforming coverage due to the high propagation loss remains a critical impediment to Internet of Things (IoT) hand-held applications. This article introduces the concept of dynamic beam shaping consisting of resource-efficient beamforming algorithm associated with switchable antenna system configurations. The ability to control and modify the beam direction, polarization, and beam width is confirmed to be highly instrumental in ensuring an uncorrupted, spherical wireless coverage of 60 GHz Wi-Fi featuring multigigabit data rates. Empirical studies ascertain that the presented beamforming algorithm and cross-layer hardware design can globally search optimal beams with more than 90 % hit ratio. To the author's best knowledge this work is the first to successfully demonstrate the applicability of 60 GHz-based mmWave Wi-Fi for various IoT user scenarios such as 4 K ultra high definition and 720p high definition streaming investigated under real-life scenarios. [ABSTRACT FROM AUTHOR]

High accuracy of terrain identification is essential for intelligent control of tracked mobile robots. In this article, a learning-based identification framework is proposed to achieve precise driving torque prediction. Experiments under straight-line and steady-state turning maneuvers were conducted to develop terrain identification in three similar terrains. A multiple deep belief networks is applied as the identification layer with three kinds of signal sources. An equivalent weight algorithm with training experience effectively integrates the results from different signal sources to improve the identification accuracy. With the experiment and identification results, a method combining numerical approximation and Gaussian process (GP) is presented to predict driving torque. A combined Gaussian kernel with long- and short-term characteristics is selected to enhance prediction performance. The results from combined signal sources under straight-line maneuvers yield over 98% accuracy, which exceeds that from other sources. The integration algorithm obviously improves the identification accuracy and stability compared with a single signal source. The influence of window length in GP is explored with hyperparameters and results. The performance of torque prediction in different terrains is analyzed with inner and outer tracks. Compared with existing methods, the results validate the effectiveness and superior performance of the proposed framework. [ABSTRACT FROM AUTHOR]

An electronic interface for the maximization of the power extraction from train suspension energy harvesters is presented in this article. It is made up of a passive rectifier and a dc–dc converter equipped with a digital control unit that implements a novel maximum power point tracking technique. It can settle the voltage at the rectifier output to its optimal value, despite the time-varying train suspension vibrations. By exploiting the measurement of the generator speed, the proposed technique is able to almost instantaneously reach the maximum power point, allowing a power extraction higher than the widely used perturb and observe algorithm. Moreover, the proposed technique is equipped with an adaptive control for ensuring the power maximization despite the tolerances and time-variability of the system parameters. Experimental results validate the theoretical analysis and confirm the superior performance of the proposed interface. [ABSTRACT FROM AUTHOR]