Editor’s Choice Articles

For a mobile robot, navigation in a densely crowded space can be a challenging and sometimes impossible task, especially with traditional techniques. In this paper, we present a framework to train neural controllers for differential drive mobile robots that must safely navigate a crowded environment while trying to reach a target location. To learn the robot’s policy, we train a convolutional neural network using two Reinforcement Learning algorithms, Deep Q-Networks (DQN) and Asynchronous Advantage Actor Critic (A3C) and develop a training pipeline that allows to scale the process to several compute nodes. We show that the asynchronous training procedure in A3C can be leveraged to quickly train neural controllers and test them on a real robot in a crowded environment. Full article

The limb-driving force mutation of the modular combination parallel manipulator (MCPM) affects the alignment process of optical axis. In this paper, a novel optimization method based on the force mutation penalty term is proposed to solve the problem of driving force mutation. The kinematics and dynamics models of the manipulator are established using a modularization idea, reducing the complexity of the modeling process, and verified using co-simulation. Moreover, particle swarm optimization (PSO) is applied as an optimization tool. The effectiveness of the proposed method is confirmed by comparing it with the minimize-the-maximum and Moore–Penrose (M–P) methods, which are widely used to solve parallel manipulators with redundant drives. Full article

Intelligent on-site fault diagnosis and professional vibration analysis are essential for the safety and stability of rotating machinery operation. This paper represents a fault diagnosis scheme based on two-stage compressed sensing for triaxial vibration data, which realizes fault diagnosis for rotating machinery based on compressed data and data reconstruction for professional vibration analysis. In the 1st stage, the triaxial vibration signals are compressed using a pre-designed hybrid measurement matrix; these compressed data can be used both for time-frequency transform and for vibration data reconstruction. In the 2nd stage, the frequency spectra of the triaxial vibration signals are fused and further compressed using another pre-designed joint measurement matrix, which inhibits the high-frequency noises simultaneously. Finally, the fused spectra are employed as feature vectors in sparse-representation-based classification, where the proposed batch matching pursuit (BMP) algorithm is utilized to calculate the sparse vectors. The two-stage compression scheme and the BMP algorithm minimize the computational cost of on-site fault diagnosis, which is suitable for edge computing platforms. Meanwhile, the compressed vibration data can be reconstructed, which provides evidence for professional vibration analysis. The method proposed in this study is validated by two practical case studies, in which the accuracies are 99.73% and 96.70%, respectively. Full article

The objective of this study was to evaluate the performance of a magnetorheological-fluid-based variable stiffness actuator leg under high impact forces through optimal tuning and control of stiffness and damping properties. To achieve this, drop testing experiments were conducted with the leg at various drop heights and payload masses. The results showed that while lower stiffness and higher damping can lead to lower impact forces and greater energy dissipation, respectively, optimal control can also protect the leg from deflecting beyond its functional range. Comparison with a rigid leg with higher damping showed a 57.5% reduction in impact force, while a more compliant leg with lower damping results in a 61.4% reduction. These findings demonstrate the importance of considering both stiffness and damping in the design of legged robots for high impact force resistance. This simultaneously highlights the efficacy of the proposed magnetorheological-fluid-based leg design for this purpose. Full article

In the last decade, cable-suspended parallel robots have attracted significant interest due to their large workspaces and high dynamic performances. However, a significant drawback is that cables must always be in tension to control the motion. Using launch motions to reach a target can enlarge the workspace of such robots. For a spatial translational cable robot suspended by six pairwise-parallel cables, an analytical method for planning point-to-point dynamic trajectories is proposed. Using a second-order Bézier curve trajectory, the mechanism starts from a static condition, passes through intermediate points, and finally launches an object towards a target. According to the kinematic constraint conditions on the position, the velocity and acceleration of the end-effector at a prescribed point, the parametric expressions for a dynamically-feasible trajectory can be determined. The feasibility of the trajectory is analyzed under the constraint that cable tensions must be positive at all times. By changing the position of the end point of the trajectory and the total motion time, the kinematic conditions on the position and velocity as well as the feasibility constraint can be satisfied. Finally, our point-to-point dynamic launch trajectories are verified by simulations and experiments. Full article

A spring-energized seal, whose PTFE plastic shell has excellent self-lubrication and a low temperature stability, is used widely in liquid fuel valves’ rotating end-face seals. However, in practical application, temperature has a larger effect on not only the physical and tribological properties of materials, but also on the leakage performance of spring-energized rings. Thus, a high and low temperature sealing test of the spring-energized seal that applies to an engine was carried out. In this paper, the leakage characteristics, friction torque and wear characteristics of a spring-energized ring under different temperatures were studied. The friction torque at high temperature was less than that at normal temperature, and a low temperature could effectively reduce the wear amount of PTFE material. In order to study the influence of temperature on PTFE filled with graphite, the friction and wear test of PTFE-2 was carried out. The results showed that the amount of wear of PTFE-2 was only 27.8% of that at the normal temperature but the friction coefficient was three times larger when the temperature was −45 °C. Full article

To date, hydraulic energy is still, among the renewable ones, the most widespread and most exploited to produce electricity. With the current trend to exploit any renewable source available, the limits for the economic convenience of hydroelectric power plants have significantly changed, making it interesting and convenient to use even small heads and low flow rates. In the specific applications of hydraulic turbines operating with low heads, the Kaplan turbine plays the predominant role among the available machines, also given the possibility of carrying out an “on cam” regulation, acting simultaneously on the geometry of the rotor and distributor rows, thus allowing a wide flow rate adjustment range. However, for applications characterized by very low heads and low available powers, it may not be convenient to use complex regulating devices. For this reason, these plants usually use axial machines characterized by a partial regulation (of the distributor or of the rotor), significantly reducing the operating range of the machine compared to the case of double regulation. In the last decade, the development of reliable and less expensive permanent magnet generators and power electronic converters and related new control strategies has paved the way for the concept of regulating hydraulic turbines by means of variable rotational speed. This regulation principle is based on the possibility of acting in the case of using synchronous permanent magnets electric generators and electronic power converters and on the variation of the rotational speed of the machine while keeping the grid frequency constant. The concept can be applied both to pure propellers with fixed a rotor and fixed distributor and to hydraulic axial turbines with regulation based on the modification of the variable guide vane opening angle. Although this new regulation approach, even in the case of the combined guide vane and rotational speed regulation, does not allow to recover most of the energy losses due to the variation of the operating conditions as effectively as the Kaplan double regulation does, the variation of the rotation speed, coupled with the variation of the opening of the distributor row, allows to reduce the tangential kinetic energy losses generated at the turbine exit during the off-design operations of a fixed blade opening angle rotor. At the same time, this type of regulation offers a simple and thus low-cost solution. The present study develops the theory underlying this regulation concept, based on the use of the turbomachinery fundamental equations, and reports the results of the off-design CFD analysis carried out for different combinations of rotation speeds and openings of the distributor, showing the improvement of the hydraulic efficiency over a large range of operating conditions with respect to the single regulation approach. Full article

The lack of indoor floor plans is one of the major obstacles to ubiquitous indoor location-based services. Dedicated mobile robots with high-precision sensors can measure and produce accurate indoor maps, but the deployment remains low for the public. Computer vision techniques are adopted by some existing smartphone-based methods to build the 3D point cloud, which have the cost of a quantity of the efforts of image collection and the risk of privacy issues. In this paper, we propose BatMapper-Plus which adopt acoustic ranging and inertial tracking to construct precise and complete indoor floor plans on smartphones. It emits acoustic signals to measure the distance from the smartphone to a neighbouring wall segment, and produces accessible areas by surrounding the building during walking. It also refines the constructed indoor floor plan to eliminate scattered segments, and identifies connection areas, including stairs and elevators among different floors. In addition, we propose an LSTM-based dead-reckoning model which is trained by outdoor IMU readings and GPS records, and use it to infer the step length during indoor walking, thereby improving the floor plan quality. We also elaborate how to use the constructed map for indoor navigation, i.e., a Dynamic Time Warping algorithm which automatically matches current inertial readings and historical sensory data during map construction to produce fine-grained walking guidance. To show our effectiveness compared with the state-of-the-art, we carry out extensive experiments in a teaching building and a residential building. It proves that our method is efficient without any privacy concerns and texture/illumination limitations. Full article

Dynamics models of planetary gear sets (PGSs) are usually established to predict their dynamic behavior and load-sharing characteristics. The accurate modeling of bearing support stiffness is essential to study their dynamics. However, in most of the existing PGS dynamic models, the effect of characteristics coupling the rolling bearing time-varying nonlinear stiffness with the translational property of PGSs on the dynamic responses was completely neglected. To investigate this problem, a refined dynamic model for PGSs is proposed considering the coupled relationship between the radial translation of the rotating components and the time-varying nonlinear support stiffness of the ball bearing. The refined dynamic model simultaneously considers the coupled effect of the time-varying characteristic caused by the orbital motion of the rolling elements and the nonlinear characteristic caused by Hertzian contact between the rolling elements and raceways of the ball bearing. Comparisons between the simulations and experimental results are presented, which indicate that the PGS vibration spectrums yielded by the proposed time-varying nonlinear stiffness model are much closer to the actual scenarios than those of traditional models. The analysis results provide theoretical guidance for fault monitoring and diagnosis of the rolling bearings used in the PGS. Full article

The tire/road peak friction coefficient (TRPFC) is the core parameter of vehicle stability control, and its estimation accuracy significantly affects the control effect of active vehicle safety. To estimate the peak adhesion coefficient accurately, a new method for the comprehensive adhesion coefficient of three-dimensional pavement based on a dimensionless data-driven tire model is proposed. Firstly, in order to accurately describe the contact state between the three-dimensional road surface and the tire during driving, stress distribution and multi-point contact are introduced into the vertical dynamic model and a new tire model driven by dimensionless data is established based on the normalization method. Secondly, the real-time assessment of lateral and longitudinal adhesion coefficients of three-dimensional pavement is realized with the unscented Kalman filter (UKF). Finally, according to the coupling relationship between the longitudinal tire adhesion coefficient and the lateral tire adhesion coefficient, a fuzzy reasoning strategy of fusing the longitudinal tire adhesion coefficient and the lateral tire adhesion coefficient is designed. The results of vehicle tests prove that the method proposed in this paper can estimate the peak adhesion coefficient of pavement quickly and accurately. Full article

This paper focuses on the synchronous vibration suppression of an active magnetic bearing (AMB) system without a rotating speed sensor. One of the most intractable problems with AMB systems is the synchronous vibration caused by the mass imbalance of the rotor. Moreover, practically all existing unbalance control algorithms require the rotating speed sensor to determine rotation speed. However, in some unique applications, it is impossible to install and use the rotating speed sensor as intended. This study provided a nonlinear adaptive control (NAC) algorithm and a modified frequency estimator to address the above issues. The proposed approach can suppress current and displacement vibrations by regulating the control structure. The frequency estimator calculates the rotating speed based on the position of the rotor at different moments, which has a quick response time, high precision, and effective tracking. The NAC algorithm can achieve unbalanced control based on the period iteration strategy. Additionally, the Lyapunov method is used to demonstrate the stability of the NAC algorithm. Finally, the experimental and simulation results also confirm the effectiveness and reliability of the overall control scheme. The results from simulations and experiments indicate that the novel frequency estimator can track the speed accurately and that its error can be regulated to within $\pm$0.05 Hz. The overall control schema can reduce the displacement vibration’s amplitude by 72.2% and the current vibration’s amplitude by 65.6%. Full article

The traditional method of relying on human hearing to detect foreign object debris (FOD) events during rocket tank assembly processes has the limitation of strong reliance on humans and difficulty in establishing objective detection records. This can lead to undetected FOD entering the engine with the fuel and causing major launch accidents. In this study, we developed an automatic, intelligent FOD detection system for rocket tanks based on sound signals to overcome the drawbacks of manual detection, enabling us to take action to prevent accidents in advance. First, we used log-Mel transformation to reduce the high sampling rate of the sound signal. Furthermore, we proposed a multiscale convolution and temporal convolutional network (MS-CTCN) to overcome the challenges of multi-scale temporal feature extraction to detect suspicious FOD events. Finally, we used the proposed post-processing strategies of label smoothing and threshold discrimination to refine the results of FOD event detection and ultimately determine the presence of FOD. The proposed method was validated through FOD experiments. The results showed that the method had an accuracy rate of 99.16% in detecting FOD and had a better potential to prevent accidents compared to the baseline method. Full article

For hundreds of years, water mills have supported the local economies of Piedmont by contributing to the production of flour, textile fibres, timber, and metal agricultural tools. Since the beginning of the last century, and in particular after the 1950s, many artefacts have been abandoned. Nonetheless, hundreds of mills are still present in southern Piedmont, both in the plains and in the mountains, sometimes in an excellent state of conservation. This work presents a hammer forge, the La Pianca mill in Busca, Cuneo, Italy, as a significant, detailed case study. The socio-economic context in which exists is analysed, its history is reconstructed, and the functioning of the machinery, including the water wheels, the motion transmission systems, and the various utilities consisting of tilt hammers, grinding wheels, and drills, is analysed in detail. Beyond the historical interest, concerning both the territory and the architecture, as well as the machines and mechanisms, this work aims to make a contribution to the prefiguration of effective scenarios for the reconversion of similar productive artefacts. Full article

An aircraft engine is expected to have a high-reliability system as a safety-critical asset. A scheduled maintenance strategy based on statistical calculation has been employed as the current practice to achieve the reliability requirement. Any improvement to this maintenance interval is made after significant reliability issues arise (such as flight delays and high component removals). Several publications and research studies have been conducted related to this issue, one of them involves performing simulations and providing aircraft operation datasets. The recently published NASA CMAPPS datasets have been utilised in this paper since they simulate flight data recording from various measurements. A prognostics model can be developed by analysing these datasets and predicting the engine’s reliability before failure. However, the state-of-the-art prognostics techniques published in the literature using these NASA CMAPPS datasets are mainly purely data-driven. These techniques mainly deal with a “black box” process which does not include uncertainty quantification (UQ). These two factors are barriers to prognostics applications, particularly in the aviation industry. To tackle these issues, this paper aims at developing explainable and transparent algorithms and a software tool to compute the engine health, estimate engine end of life (EoL), and eventually predict its remaining useful life (RUL). The proposed algorithms use hybrid metrics for feature selection, employ logistic regression for health index estimation, and unscented Kalman filter (UKF) to update the prognostics model for predicting the RUL in a recursive fashion. Among the available datasets, dataset 02 is chosen because it has been widely used and is an ideal candidate for result comparison and dataset 03 is employed as a new state-of-the-art. As a result, the proposed algorithms yield 34.5–55.6% better performance in terms of the root mean squared error (RMSE) compared with the previous work. More importantly, the proposed method is transparent and it quantifies the uncertainty during the prediction process. Full article

To improve the performance of the aerodynamic properties and reduce the aerodynamic noise of an axial flow fan in the outdoor unit of an air conditioner, this study proposed a bionic forked trailing-edge structure inspired by the forked fish caudal fin and implemented by modifying the trailing edge of the prototype fan. The effect of the bionic forked trailing edge on the aerodynamic and aeroacoustic performance was investigated experimentally, and detailed analyses of the blade load and internal vortex structures were performed based on large-eddy simulations (LES). It is shown that the bionic forked trailing edge could effectively adjust the blade load distribution, reduce the pressure difference between the pressure side and suction side near the trailing edge of the blade tip region, and weaken the intensity and influence range of the inlet vortex (IV) and the tip leakage vortex (TLV). The discrete noise caused by the vortices in the rotor tip area was also reduced, particularly at the blade passing frequency (BPF) and its harmonic frequency. The experimental results confirmed the existence of an optimal bionic forked trailing-edge structure, resulting in the maximum power-saving rate γ of 7.5% and the reduction of 0.3 ~ 0.8 dB of aerodynamic noise, with an included angle θ_t of 13.5°. The detailed analysis of the internal vortex structures provides a good reference for the efficiency improvement and noise reduction of axial flow fans. Full article

In the manufacturing process, digital twin technology can provide real-time mapping, prediction, and optimization of the physical manufacturing process in the information world. In order to realize the complete expression and accurate identification of and changes in the real-time state of the manufacturing process, a digital twin framework of incremental learning driven by stream data is proposed. Additionally, a novel method of stream data-driven equipment operation state modeling and incremental anomaly detection is proposed based on the digital twin. Firstly, a hierarchical finite state machine (HFSM) for the manufacturing process was proposed to completely express the manufacturing process state. Secondly, the incremental learning detection method driven by stream data was used to detect the anomaly of the job process data, so as to change the job status in real time. Furthermore, the F1 value and time consumption of the proposed algorithm were compared and analyzed using a general dataset. Finally, the method was applied to the practical case development of a welding manufacturer’s digital twin system. The flexibility of the proposed model is calculated by the quantitative method. The results show that the proposed state modeling and anomaly detection method can help the system realize job state mapping and state change quickly, effectively, and flexibly. Full article

Measurement technology based on machine vision has been widely used in various industries. The development of vision measurement technology mainly depends on the process of photosensitive components and the algorithm of processing a target image. In the high-precision dimension measurement of machined metal parts, the high-resolution imaging device usually exposes the cutting texture of the metal surface and affects the accuracy of measurement algorithm. At the same time, the edges of machined metal parts are often chamfered, which makes the edges of objects in the picture overexposed in the lighting measurement environment. These factors reduce the accuracy of dimensioning metal parts using visual measurements. The traditional vision measurement method based on color/gray image makes it difficult to analyze the physical quantities in the light field except for the light intensity, which limits the measurement accuracy. Polarization information can more carefully depict the edge contour edge information in the scene and increase the contrast between the foreground and the background. This paper presents a method to improve the measurement accuracy of machined metal parts by using polarization vision. The incident angle of the light source is optimized according to the complex refractive index of the metal material, and the degree of polarization image with enhanced edge contour features of the ROI (region of interest) is obtained. The high-precision measurement of cylindrical brass motor components is realized by using the method of reprojection transformation correction and maximum correlation template matching (NCC) for rough positioning, as well as the method of edge extraction and optimal fitting. The experimental results show that for copper parts with a tolerance range of $\pm 0.1$ mm, the average measurement error and maximum measurement error are within $0.01$ mm, which are higher than the existing color/gray image measurement methods. Full article

Ultrashort-Radius Horizontal Well (URHW) drilling engineering plays an important role in increasing the recovery factor of old oilfields. By sidetracking old wellbores at a very high build-up rate, the URHW can effectively exploit the residual oil near old wellbores. Currently, the main problem faced in URHW drilling engineering is the reduced torque received by drill bits owing to the increased friction between the flexible drilling assembly and wellbore as the horizontal section extends, which greatly limits oil production from a single trip. To tackle this problem, we proposed an innovative machine design, a Dynamic Flexible Drill Rod (DFDR), to provide extra torque near the drill bit to extend the horizontal section of the URHW. The interior structure and working principle of the DFDR were illustrated. The mechanical properties of the DFDRs critical load-bearing part were examined via simulation. The torque and pressure loss characteristics were analyzed using computational fluid dynamics. Corresponding modifications were made to optimize the design, with model machines produced accordingly. Field trials were carried out based on old wellbores in Chunliang District, Shengli Oilfield. The DFDR-based technique extended the URHWs horizontal section in this area by 13.38% on average. Full article

This work investigates the issue of a hybrid trajectory tracking control algorithm (HTCA) for robot manipulators (RMs) with uncertain dynamics and the effect of external disturbances. Following are some proposals for achieving the control target. Firstly, to achieve the active disturbance rejection, we propose a uniform second-order sliding mode disturbance observer (USOSMDO) to obtain directly the lumped uncertainties without their prior upper-bound information. Secondly, a fixed-time singularity-free terminal sliding surface (FxSTSS) is proposed to obtain a fixed-time convergence of the tracking control error (TCE) without the singularity in the control input. Then, using information on the proposed USOSMDO, our HTCA is formed based on the FxSTSS and the fixed-time power rate reaching law (FxPRRL). The control proposal not only stabilizes with the global fixed-time convergence but also attains high tracking accuracy. In addition, the chattering problem also is handled almost completely. Finally, numerical simulations verify the effectiveness and advantages of applying the proposed HTCA to a FARA robot. Full article

Multiphase drives have been presented as potential replacements for conventional three-phase machines, primarily because of their propensity to operate faultlessly. Due to the various stator phase arrangements, standard fault detection techniques are insufficiently applicable and cannot be used to diagnose faults in the various configurations of multiphase machines in closed-loop applications. The current study proposes an effective online diagnostic technique based on the computing and tracking of a significant severity factor, which is defined as the ratio of the zero, negative, and positive voltage symmetrical components employing a short-time least-square Prony algorithm (STLSP). In this study, the asymmetrical six-phase induction motor (ASPIM) was controlled by a model predictive control (MPC) algorithm, an attractive control scheme for the regulation of multiphase electric drives, since it easily exploits their inherent advantages. This article addresses stator faults in ASPIMs. The effectiveness of the suggested strategy was confirmed experimentally for various operating conditions in both steady and transient states. Full article

There are many undulating terrains in the wild environment. In order to realize the adaptive and stable walking of quadruped robots on unknown sloped terrain, a slope-adaptability model predictive control (SAMPC) algorithm is proposed in this work. In the absence of external vision sensors, the orientation and inclination of the slope are estimated based on the joint position sensors and inertial measurement units (IMU). In an effort to increase the stability margin, the adaptive algorithm adjusts the attitude angle and the touch-down point of the swing leg. To reduce the slipping risk, a nonlinear control law is designed to determine the friction factor of the friction cone constraint from the inclination of the slope. We validate the effectiveness of the framework through a series of simulations. The automatic smooth transition from the flat to the unknown slope is achieved, and the robot is capable of walking in all directions on the slope. Notably, with reference to the climbing modal of blue sheep, the robot successfully climbed a 42.4° slope, proving the ultimate ability of the proposed framework. Full article

Currently, interest in deep learning-based semantic segmentation is increasing in various fields such as the medical field, automatic operation, and object division. For example, UNet, a deep learning network with an encoder–decoder structure, is used for image segmentation in the biomedical area, and an attempt to segment various objects is made using ASPP such as Deeplab. A recent study improves the accuracy of object segmentation through structures that extend in various receptive fields. Semantic segmentation has evolved to divide objects of various sizes more accurately and in detail, and various methods have been presented for this. In this paper, we propose a model structure that reduces the overall parameters of the deep learning model in this development and improves accuracy. The proposed model is an encoder–decoder structure, and an encoder half scale provides a feature map with few encoder parameters. A decoder integrates feature maps of various scales with high area details and forward features of low areas. An integrated feature map learns a feature map of each encoder hierarchy over an area of previous data in the form of a continuous coupling structure. To verify the performance of the model, we learned and compared the KITTI-360 dataset with the Cityscapes dataset, and experimentally confirmed that the proposed method was superior to the existing model. Full article

As the main engineering power plant, diesel engines are irreplaceable in the future. However, the stringent emission regulations impose many tough requirements to their developments. Recently, flexible fuel injection strategy has been recognized as an effective technology in creating an advanced spray and mixture formation and improving combustion efficiency indirectly. However, the detailed combustion and emission behaviors under flexible fuel injection are still unknown. Therefore, this paper aims to investigate the combustion and emission characteristics under flexible fuel injection and explore an optimal injection strategy for high-efficiency combustion. A numerical simulation method is conducted by coupling the large-eddy simulation (LES) model and the SAGE combustion model. Then, the spray mixing, combustion flame propagation and emissions formation under various multiple-injection strategies are investigated. Results reveal that initial an ultrahigh injection pressure has a significant influence on the spray’s axial penetration while dwell time mainly affects the spray’s radial expansion. Under an initial ultrahigh injection pressure, the turbulence kinetic energy (TKE) becomes larger, and the vortex motions are stronger, contributing to a better spray turbulent mixing. Meanwhile, a snatchier flame structure with a favorable level of equivalence ratio and a homogeneous temperature distribution is obtained. In this way, the peak heat release rate (HRR) could increase by 46.7% with a 16.7% reduction in soot formation and a 31.4% reduction in NO_x formation. Full article

One-dimensional (1D) aerodynamic performance predictions are very often conducted by researchers and designers during the preliminary design of centrifugal compressors. This paper focuses on a 1D prediction method for centrifugal compressors with splitter blades, which is rarely seen in the open literature. One-dimensional prediction of aerodynamic overall performance is made for centrifugal compressors with different technical design specifications. However, the aerodynamic overall prediction accuracy relies on the accuracy of the 1D-loss-models used. Therefore, an optimum combination of loss models is proposed by summarizing a variety of loss models presented in the public literature. In addition, an optimization method is utilized to optimize some coefficients involved in loss models in order to improve the generality of the combined model. The modified models obtained in this study are proved to have good predictive accuracy. Full article

Three-dimensional (3D) object detection has a vital effect on the environmental awareness task of autonomous driving scenarios. At present, the accuracy of 3D object detection has significant improvement potential. In addition, a 3D point cloud is not uniformly distributed on a regular grid because of its disorder, dispersion, and sparseness. The strategy of the convolution neural networks (CNNs) for 3D point cloud feature extraction has the limitations of potential information loss and empty operation. Therefore, we propose a graph neural network (GNN) detector based on neighbor feature alignment mechanism for 3D object detection in LiDAR point clouds. This method exploits the structural information of graphs, and it aggregates the neighbor and edge features to update the state of vertices during the iteration process. This method enables the reduction of the offset error of the vertices, and ensures the invariance of the point cloud in the spatial domain. For experiments performed on the KITTI public benchmark, the results demonstrate that the proposed method achieves competitive experimental results. Full article

Fault location is one of the main challenges in the distribution network due to its expanse and complexity. Today, with the advent of phasor measurement units (PMU), various techniques for fault location using these devices have been proposed. In this research, distribution network fault location is defined as an optimization problem, and the network fault location is determined by solving it. This is done by combining PMU data before and after the fault with the power system status estimation (PSSE) problem. Two new objective functions are designed to identify the faulty section and fault location based on calculating the voltage difference between the two ends of the grid lines. In the proposed algorithm, the purpose of combining the PMU in the PSSE problem is to estimate the voltage and current quantities at the branch point and the total network nodes after the fault occurs. Branch point quantities are calculated using the PMU and the governing equations of the π line model for each network section, and the faulty section is identified based on a comparison of the resulting values. The advantages of the proposed algorithm include simplicity, step-by-step implementation, efficiency in conditions of different branch specifications, application for various types of faults including short-circuit and series, and its optimal accuracy compared to other methods. Finally, the proposed algorithm has been implemented on the IEEE 123-node distribution feeder and its performance has been evaluated for changes in various factors including fault resistance, type of fault, angle of occurrence of a fault, uncertainty in loading states, and PMU measurement error. The results show the appropriate accuracy of the proposed algorithm showing that it was able to determine the location of the fault with a maximum error of 1.21% at a maximum time of 23.87 s. Full article

This paper develops an inner stator current controller based on an enhanced reaching-law-based discrete-time terminal sliding mode. The problem of tracking stator currents with high accuracy while ensuring the robustness of a six-phase induction motor in the presence of uncertain electrical parameters and unmeasurable states is tackled. The unknown dynamics are approximated by using a time delay estimation method. Then, an enhanced power-reaching law is used to make each stage of the convergence faster. A stability analysis and the system controller’s finite-time convergence are demonstrated in detail. Practical work was conducted on an asymmetrical six-phase induction machine to illustrate the developed discrete approach’s robustness and effectiveness. Full article

This paper investigates the path planning problem of an unmanned aerial vehicle (UAV) for completing a raid mission through ultra-low altitude flight in complex environments. The UAV needs to avoid radar detection areas, low-altitude static obstacles, and low-altitude dynamic obstacles during the flight process. Due to the uncertainty of low-altitude dynamic obstacle movement, this can slow down the convergence of existing algorithm models and also reduce the mission success rate of UAVs. In order to solve this problem, this paper designs a state detection method to encode the environmental state of the UAV’s direction of travel and compress the environmental state space. In considering the continuity of the state space and action space, the SD-TD3 algorithm is proposed in combination with the double-delayed deep deterministic policy gradient algorithm (TD3), which can accelerate the training convergence speed and improve the obstacle avoidance capability of the algorithm model. Further, to address the sparse reward problem of traditional reinforcement learning, a heuristic dynamic reward function is designed to give real-time rewards and guide the UAV to complete the task. The simulation results show that the training results of the SD-TD3 algorithm converge faster than the TD3 algorithm, and the actual results of the converged model are better. Full article

In 4D printing, structures with gradients in physical properties are 3D printed in order to dramatically increase deformation. For example, printing bilayer structures with passive and active layers has been proposed, however, these methods have the disadvantages that the material of each layer is mixed, and the modeling process is complicated. Herein, we present a method of creating gradient gels with different degrees of polymerization on the UV-exposed side and the other side using a single material by simply increasing the amount of initiator. This gel is the first example in which the differential swelling ratio between two sides causes the gradient to curl inward toward the UV-exposed side. The mechanical properties (swelling ratio and Young’s modulus) were measured at different material concentrations and structures, and the effects of each on deformation were analyzed and simulated. The results show that adding an initiator concentration of 0.2 (mol/L) or more causes deformation, that increasing the crosslinker concentration by a factor of three or more increases deformation, and that adding a hinge structure limits the gradient gel to deformation up to 90°. Thus, it was found that the maximum deformation can be predicted to some extent by simulation. In the future, we will be able to create complex structures while utilizing simulation. Full article

This study presents a new flexible dynamic model for drive systems comprising lubricated bearings operating under conditions representative of electrified vehicle powertrains. The multi-physics approach importantly accounts for the tribological phenomena at the roller–race conjunction and models their effect on shaft-bearing system dynamics. This is achieved by embedding a non-linear lubricated bearing model within a flexible system level model; this is something which has not, to the authors’ knowledge, been reported on hitherto. The elastohydrodynamic (EHL) film is shown to increase contact deflection, leading to increased contact forces and total bearing stiffness as rotational speeds increase. Results show that for a 68 Nm hub motor operating up to 21,000 rpm, the input bearing EHL film reaches a thickness of 4.15 $\mathsf{\mu }$m. The lubricant entrainment increases the roller–race contact deflection, causing the contact stiffness to increase non-linearly with speed. The contribution of the lubricant film leads to a 16.6% greater bearing stiffness at 21,000 rpm when compared to conventional dry-bearing modelling methods used in current multi-body dynamic software. This new methodology leads to more accurate dynamic response of high-speed systems necessary for the next generation of electrified vehicles. Full article

The change towards a clean electric generation system is essential to achieve the economy decarbonization goal. The Canary Islands Archipelago confronts social, environmental, and economic challenges to overcome the profound change from a fossil fuel-dependent economy to a fully sustainable renewable economy. This document analyzes a scenario with a totally renewable generation system and with total electrification of the economy for the Canary Islands by 2040. In addition, it also shows the significant reduction in this fully renewable system when an optimized interconnection among islands is considered. This scenario consists of a solar PV system of 11 GWp, a wind system of only 0.39 GWp, a pumped storage system of 16.64 GWh (2065 MW), and a lithium-ion battery system of 34.672 GWh (3500 MW), having a system LCOE of 10.1 cEUR/kWh. These results show the certainty of being able to use an autonomous, reliable, and fully renewable system to generate and store the energy needed to dispense with fossil fuels, thus, resulting in a system free of greenhouse gas emissions in the electricity market. In addition, the proposed system has low energy wastage (less than 20%) for a fully renewable, stand-alone, and off-grid system. Full article

Compared with pneumatic tires, non-pneumatic tires have incomparable performance, in terms of load bearing and safety. In this paper, the static load characteristics and pure longitudinal slip characteristics of the non-pneumatic tire are studied by combining experiments and simulations. The test results show that the radial stiffness of the original structure is nonlinear, the pure longitudinal sliding characteristics are seriously inconsistent, the brakes are very sensitive, and the driving is slightly soft. A series of designs have been carried out from the aspects of load-bearing mode and anti-symmetry of the structure, and numerical simulations have been carried out. The results show that the radial secant stiffness of the optimized structure II is increased by 58.8%, and the radial tangent stiffness is increased by 2.96 times, under the premise of ensuring the mass reduction. Additionally, the R square is 0.9932, and the linearity of the radial stiffness curve is greatly improved. The braking and driving conditions under pure longitudinal sliding characteristics are more antisymmetric, which greatly improves the braking sensitivity, but the driving performance is not as good as the original structure. In addition, this paper establishes the evaluation index of the non-pneumatic tire carrying mode, which lays the foundation for further exploration of the non-pneumatic tire carrying mechanism. Full article

With improvements in lubrication and material strength, the power transmitted by plastic gears has increased significantly. To develop high-performance transmission systems, it is necessary to gain deep insights into the dynamic characteristics of plastic gears. However, because plastics are viscoelastic materials, they do not obey Hooke’s law, which is the basis of traditional gear dynamic models. In this study, a refined dynamic model for an epoxy gear pair considering material viscoelasticity and extended tooth contact is established, and the differences in the dynamic responses between an epoxy and a steel gear pair are compared with respect to the dynamic meshing force and dynamic transmission error. The results show that: (1) the plastic gear can restrain the meshing impact, it has a generally lower dynamic meshing force than steel gear pair; (2) the position accuracy is the weak point of plastic gears, and this is significantly affected by the rotation speed; (3) the way to indirectly evaluate the dynamic meshing force by measuring the dynamic transmission error, which is often used for metal gears and is less effective for plastic gears. Full article

In public use of a large display, it is a usual phenomenon that multiple users individually participate in respective tasks on a common device. Previous studies have categorized such activity as independent interaction that involves little group engagement. However, by investigating how users approach, participate in, and interact with large displays, we found that parallel use is affected by group factors such as group size and between-user relationship. To gain a thorough understanding of individual and group behaviors, as well as parallel interaction task performance, one 70-inch display-based information searching task and experiment was conducted, in which a mobile eye movement tracking headset and a motion sensing RGB-depth sensor were simultaneously applied. The results showed that (1) a larger group size had a negative influence on the group users’ concentration on the task, perceived usability, and user experience; (2) a close relationship between users contributed to occasional collaborations, which was found to improve the users’ task completion time efficiency and their satisfaction on the large display user experience. This study proves that an integrated application of eye movement tracking and motion sensing is capable of understanding individual and group users’ behaviors simultaneously, and thus is a valid and reliable scheme in monitoring public activities that can be widely used in public large display systems. Full article

Weak magnetic flux leak detection is one of the most important non-destructive testing and measurement methods for pipelines. Since different defects cause different damage, it is necessary to classify the different types of defects. Traditional machine learning methods of defect type identification mainly use feature analysis methods and rely on expert a priori knowledge and the ability of designers. These methods have the following weaknesses: a priori knowledge needs to be designed iteratively, and a priori knowledge design relies on expert experience. In recent years, the rapid development of deep learning methods in the field of machine vision has led to the development of defect analysis in the industry. However, most deep learning methods lack the ability to analyze both detailed information and the overall structure. In this paper, we propose graph convolution networks for splitting and fusing multiple graphs of detail graphs and a root graph. Detail information (detail graphs) provides detailed information for the detection of WMFLs. The structure information (root graph) provides structural information for the detection of WMFLs. This paper uses simulation data and experimental data to verify that the proposed method can identify stress defects and corrosion defects well. The paper explains the experimental results in detail to demonstrate the superiority of the method in industrial methods. Full article

A parallel continuum manipulator (PCM) is a mechanism of closed-loop morphology with flexible elements such that their deformation contributes to its mobility. Flexible hexapods are six-degrees-of-freedom (DoF) fully parallel continuum mechanisms already presented in the literature. Devices of reduced mobility, i.e., lower mobility than six DoF, have not been studied so far. An essential characteristic of lower mobility mechanisms is that reduced mobility is due to kinematic constraints generated by mechanical arrangements and passive joints. In rigid-link parallel manipulators, those constraints are expressed as a set of equations relating to the parameters representing the end effector’s pose. As a consequence, independent output pose variables are controllable with the position equations, while dependent output variables undergo parasitic motions. In this paper, the performance of a tripod-type parallel continuum manipulator, $3P\underline{F}S$, is compared with the operation of its rigid counterpart $3\underline{P}RS$. We will show that in PCMs there are no such geometric constraints expressible with algebraic equations, but it is difficult to perform some types of motion in the end effector with the input torques. Another goal of this paper is to evaluate such limitation of motion in a tripod-like PCM and compare it with the constraints of the rigid $3\underline{P}RS$. Finally, the paper shows that there are strong similarities in the reduced mobility of both mechanisms. Full article

This paper introduces a procedure for controlling autonomous vehicles entering roundabouts. The aim of the centralized controller is to define the velocity profile of each autonomous vehicle by which collisions can be avoided and traveling times can be minimized. To achieve these performances, a model predictive control is introduced based on the solution of an analytical calculation of traveling times spent in the roundabout and designing the autonomous vehicles’ velocity profiles in order to avoid conflict situations while ensuring a time-optimal solution. By the application of the proposed procedure, safety of autonomous vehicles can be enhanced and the possibility of a forming congestion can be minimized. The operation of the proposed method is demonstrated by a few simulation examples in the CarSim simulation environment. Full article

To address the problem of multiple working conditions and complex requirements in magnetorheological fluid (MRF) mounts, a high-precision damping characteristic optimization method is explored. Based on the parallel plate model, the equation of fluid motion in the inertial channel was established according to the Navier–Stokes equation, and the MRF mount damping characteristics were analyzed. Considering the fluid model to be suitable in the steady-state, the model was experimentally verified, and the extended equation was fitted. Multi-objective optimization design was carried out by considering the large damping force and adjustable coefficient as the optimization goal and external geometric dimensions as variables. According to results, under the radial-channel MRF mount structure, the magnet core depth has the least influence on the damping force; furthermore, the damping performance can be quickly improved by changing the height of the inertial channel. The addition of the extended equations further improved the accuracy of the fluid model. The multi-objective optimization design can improve the strength and uniformity of the flux density of the MRF mount damping gap. After optimization, the damping force is increased by 44.64%; moreover, when the current is increased from 1.5 to 1.8 A, the controllable force increases by only 2.26%, and the damping performance is fully exerted. Full article

Three-phase grid-connected inverters have been widely used in the distributed generation system, and the current sensor has been applied in closed-loop control in inverters. When the current sensor offset faults occurs, partial fault features of multiple current sensors disappear from the closed-loop control grid-connected system, which leads to difficulties for fault diagnostics and fault-tolerant control. This paper proposes a fault tolerance method based on average current compensation mode to eliminate these adverse effects of fault features. The average current compensation mode compensates the average of the three-phase current to the αβ axis current to realize the fault feature reconstruction of the current sensor. The mode does not affect the normal condition of the system. Then, the data-driven method is used for fault diagnosis, and the corresponding fault tolerant control model is selected according to the diagnosis results. Finally, the experimental results show that the proposed strategy has a good fault tolerance control performance and can improve the fault feature discrimination and diagnostic accuracy. Full article

Laser directed energy deposition (LDED) can be deployed for depositing high-performance materials for various engineering applications. Alumina-forming steel is a high-performance material that possesses excellent corrosion and oxidation resistance, finding application in the power generation sector. In the present work, LDED using powder feeding (LDED-PF) was used to deposit Fe20Cr5.5AlY alloy using single-track, multi-track, and multi-layer deposition on SS 316L substrate. Response surface methodology (RSM)-based optimization was used to optimize the single-track deposition. The relationship between the track geometry parameters and the build rate with the LDED-PF processing parameters was studied. Further, the nonlinear relationship among the major process parameters was developed and an analysis of variance (ANOVA) was utilized to find significant parameters. The multi-track deposition yielded densely clad layers with a columnar grain structure. The presence of complex oxide slag of Y, Al, and Zr on the clad layer was detected. A micro-hardness of 240–285 HV was observed in the clad layer, with a hardness of 1088–1276 HV at the slag layer. The multi-layered structures showed a relative density of 99.7% with columnar growth and an average microhardness of 242 HV. The study paves the way for the deposition of dense alumina-forming steel structures for building components for power generation applications. Full article

For autonomous vehicles, obstacle detection results using 3D lidar are in the form of point clouds, and are unevenly distributed in space. Clustering is a common means for point cloud processing; however, improper selection of clustering thresholds can lead to under-segmentation or over-segmentation of point clouds, resulting in false detection or missed detection of obstacles. In order to solve these problems, a new obstacle detection method was required. Firstly, we applied a distance-based filter and a ground segmentation algorithm, to pre-process the original 3D point cloud. Secondly, we proposed an adaptive neighborhood search radius clustering algorithm, based on the analysis of the relationship between the clustering radius and point cloud spatial distribution, adopting the point cloud pitch angle and the horizontal angle resolution of the lidar, to determine the clustering threshold. Finally, an autonomous vehicle platform and the offline autonomous driving KITTI dataset were used to conduct multi-scene comparative experiments between the proposed method and a Euclidean clustering method. The multi-scene real vehicle experimental results showed that our method improved clustering accuracy by 6.94%, and the KITTI dataset experimental results showed that the F1 score increased by 0.0629. Full article

The wind tunnel facility at the Centro de Tecnologías Aeronáuticas was used to perform a set of experiments to study the effect of intentional mistuning on the forced response behavior of an aerodynamically unstable low-pressure turbine rotor. The intentional mistuning patterns were implemented by adding a small extra mass to some of the blades. The forced response of the rotor was therefore expected to show two resonance peaks with similar amplitudes, corresponding, respectively, to the vibration frequencies of the blades with and without added mass. However, on the post-processing of the measurements, some anomalous behavior was observed. Near resonance, the system response was synchronous with the forcing, and the frequency sweeps exhibited two resonance peaks, but it was found that the two peaks were clearly different, with the peak at lower frequency showing a much higher vibration amplitude than the high-frequency peak, and with some blades responding at both frequencies with a similar amplitude. In order to give a correct interpretation of the experimental results, a reduced-order model is derived that takes into account only the traveling wave modes coupled by the mistuning. This model, although extremely simple, is capable of reproducing the unexpected behavior of the experiments, and gives a clean explanation of the system response. It is shown that the relative size of the mistuning with respect to the frequency difference of the involved traveling-wave modes is the key parameter for the appearance of this phenomenon. Full article

Accurate trajectory tracking is a paramount objective when a mobile robot must perform complicated tasks. In high-speed movements, time delays appear when reaching the desired position and orientation, as well as overshoots in the changes of orientation, which prevent the execution of some tasks. One of the aspects that most influences the tracking performance is the control system of the actuators of the robot wheels. It usually implements PID controllers that, in the case of low-cost robots, do not yield a good tracking performance owing to friction nonlinearity, hardware time delay and saturation. We propose to overcome these problems by designing an advanced process control system composed of a PID controller plus a prefilter combined with a Smith predictor, an anti-windup scheme and a Coulomb friction compensator. The contribution of this article is the motor control scheme and the method to tune the parameters of the controllers. It has been implemented in a well-known low-cost small mobile robot and experiments have been carried out that demonstrate the improvement achieved in the performance by using this control system. Full article

This study aims to identify parametric models to estimate track irregularities in high-speed railways with simple acceleration measurements. The primary contribution of current research is the development of effective parametric models with smaller parameters. These parameters are derived from the measured data via a specialized track geometry inspection system. An adaptive Kalman filter algorithm, using the displacement estimated from the acceleration signals as the input and measured track irregularities as the output, is applied to obtain the model’s unknown parameters. These models are applied to acceleration measured from high-speed rail vehicles in operation, and track irregularities are estimated in spatial and wavelength domains. The estimated irregularities are compared to the track geometry inspection system’s results. Full article

The 6-axis external fixation mechanism with Gough-Stewart configuration has been widely applied to the correction of long bone deformities in orthopedics. Pose recognition of the mechanism is essential for trajectory planning of bone correction, but is usually implemented by the surgeons’ experience, resulting in a relatively low level of correction accuracy. This paper proposes a pose recognition method based on novel image markers, and implements accuracy analysis. Firstly, a pose description of the mechanism is established with several freely installed markers, and the layout of the markers is also parametrically described. Then, a pose recognition method is presented by identifying the orientation and position parameters using the markers. The recognition method is general in that it encompasses all possible marker layouts, and the recognition accuracy is investigated by analyzing variations in the marker layout. On this basis, layout principles for markers that achieve a desired recognition accuracy are established, and an error compensation strategy for precision improvement is provided. Finally, experiments were conducted. The results show that volume errors of pose recognition were 0.368 ± 0.130 mm and 0.151 ± 0.045°, and the correction accuracy of the fracture model after taking compensation was 0.214 ± 0.573 mm and −0.031 ± 0.161°, validating the feasibility and accuracy of the proposed methods. Full article

Generally, tire deflation results in a decrease in both handling performance and tire lifetime, and in fuel consumption increment. Therefore, the real-time knowledge of the pressure is important. Direct approaches via pressure sensors mounted on the rim of each tire are not practical, due to technical and economic reasons. Cost-effective solutions with real-time estimation of tire pressure are generally less accurate and reliable than direct ones. Dynamical estimators based on a suspension model need road surface topology information to compute disturbances on the suspension system as an input, which is typically unknown. This paper proposes an innovative approach to estimate tire pressure indirectly, without actual road surface roughness information. A vertical suspension dynamic model is used to build several unscented Kalman filters, parametrised around different road surface topologies. These estimators are combined following the Interacting Multiple Model approach, which gives an acceptable estimation of tire stiffness through a weighted average obtained from a probabilistic model. A known linear static relationship between the tire stiffness and inflation pressure is utilized to indirectly estimate the tire inflation pressure. A Monte Carlo analysis has been performed on a wide range of driving scenarios and vehicle manoeuvres. The results of the estimation have been compared to those of a single unscented Kalman filter, in order to validate the effectiveness of the proposed solution and to highlight the improved performances in monitoring tire pressure. Full article

This study aims to maintain the frequency stability of the power system penetrated by wind power. Hence, a battery energy storage system (BESS) is applied to smooth the wind power output in power systems and to enhance their load frequency control (LFC) capacity. A novel comprehensive control framework is proposed for power systems integrated with wind farms and BESS based on an adaptive fuzzy super-twisting sliding mode control (AF-SSMC) method. Firstly, the sliding functions and control laws of subsystems are designed according to different relative degrees. Then, the super-twisting algorithm is applied to suppress the chattering of the sliding mode control law. Furthermore, an adaptive fuzzy control method is used to adjust the control gains online for better control performance of the controllers. The Lyapunov stability theory is employed to prove the asymptotic stability of the subsystems. The model of an interconnected thermal power system with wind and BESS penetration is also constructed for simulation analyses. The results indicate that the AF-SSMC method effectively reduces the chattering, and the proposed framework stabilizes the frequency of the power system under system uncertainties and external disturbances. Moreover, the wind farm and BESS combined system accurately tracks a reference power to reduce wind power fluctuations. Full article

Autonomous driving vehicles (ADVs) are sleeping giant intelligent machines that perceive their environment and make driving decisions. Most existing ADSs are built as hand-engineered perception-planning-control pipelines. However, designing generalized handcrafted rules for autonomous driving in an urban environment is complex. An alternative approach is imitation learning (IL) from human driving demonstrations. However, most previous studies on IL for autonomous driving face several critical challenges: (1) poor generalization ability toward the unseen environment due to distribution shift problems such as changes in driving views and weather conditions; (2) lack of interpretability; and (3) mostly trained to learn the single driving task. To address these challenges, we propose a view-invariant spatiotemporal attentive planning and control network for autonomous vehicles. The proposed method first extracts spatiotemporal representations from images of a front and top driving view sequence through attentive Siamese 3DResNet. Then, the maximum mean discrepancy loss (MMD) is employed to minimize spatiotemporal discrepancies between these driving views and produce an invariant spatiotemporal representation, which reduces domain shift due to view change. Finally, the multitasking learning (MTL) method is employed to jointly train trajectory planning and high-level control tasks based on learned representations and previous motions. Results of extensive experimental evaluations on a large autonomous driving dataset with various weather/lighting conditions verified that the proposed method is effective for feasible motion planning and control in autonomous vehicles. Full article

Wind power has become an important source of electricity for both production and domestic use. However, because wind turbines often operate in harsh environments, they are prone to cracks, blisters, and corrosion of the blade surface. If these defects cannot be repaired in time, the cracks evolve into larger fractures, which can lead to blade rupture. As such, in this study, we developed a remote non-contact online health monitoring and warning system for wind turbine blades based on acoustic features and artificial neural networks. Collecting a large number of wind turbine blade defect signals was challenging. To address this issue, we designed an acoustic detection method based on a small sample size. We employed the octave to extract defect information, and we used an artificial neural network based on model-agnostic meta-learning (MAML-ANN) for classification. We analyzed the influence of locations and compared the performance of MAML-ANN with that of traditional ANN. The experimental results showed that the accuracy of our method reached 94.1% when each class contained only 50 data; traditional ANN achieved an accuracy of only 85%. With MAML-ANN, the training is fast and the global optimal solution is automatic searched, and it can be expanded to situations with a large sample size. Full article

High-power tractors are regarded as effective operation tools in agriculture, and plugin hybrid tractors have shown potential as agricultural machinery, due to their wide application in energy conservation. However, the allocation of the output power of the motors and engine is a challenging task, given that the energy management strategy (EMS) is nonlinearly constrained. On the other hand, the structure of the continuous variable transmission (CVT) system is complicated, and affects the price of tractors. In this paper, a variable configuration of a tractor that could have the same performance as a complex CVT system is proposed. To address the EMS issues that have shown poor performance in real time, where the programming runs online, firstly a demand power prediction algorithm is proposed in a rotary tillage operation mode. Secondly, an equivalent fuel consumption minimization strategy (ECMS) is used to optimize the power distribution between the engine and the motors. In addition, the equivalent factor is optimized with an offline genetic algorithm. Thirdly, the equivalent factor is converted into a lookup table, and is used for an online power distribution with different driving mileages and state-of-charge (SOC). The simulation results indicate that the equivalent fuel consumption is reduced by 8.4% and extends the operating mileage of pure electric power. Furthermore, the error between the actual and forecasted demand power is less than 1%. The online EMS could improve the mileage of the tractor working cycle with a more feasible fuel economy based on demand power predictions. Full article