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For further reducing the fuel consumption of the series hybrid loaders (SHLs) with input disturbances, this paper develops a robust heuristic dynamic programming (HDP) online strategy based on model predictive control (MPC). The dynamic model of SHLs with disturbances is built by employing recurrent neural network (RNN), to efficiently represent its actual operating process. Then, the MPC-based robust HDP (MPC-RHDP) local optimal control algorithm is presented, and the convergence of proposed algorithm and the stability of control system are proved. Furthermore, a MPC-RHDP online strategy is proposed to execute the energy management controller. At last, the RNN dynamic model and designed MPC-RHDP strategy are verified and compared on the hardware in loop platform. After analyzing the experiment results, in comparison with analytic model, the RNN model in higher precision is capable of more effectively describing the real process for SHLs with disturbances. In addition, under sand and stone scenarios, the proposed MPC-RHDP strategy can separately achieve average fuel-saving rate by 24.07% and 10.27% in contrast to rule-based strategy and equivalent consumption minimization strategy, providing a novel method for real-time energy optimization of the SHLs with disturbances.

A model predictive control-based heuristic dynamic programming (HDP-MPC) online strategy is proposed in this paper, to further improve the fuel economy for the series hybrid loaders. The back propagation neural network (BPNN) is utilized to establish the dynamics model for series hybrid loader, to effectively characterize its real working process. Next, a HDP-MPC approximate optimal algorithm is proposed. And its convergence and the controlled system stability are separately proved. Besides, an online energy management strategy based on HDP-MPC is designed to implement the energy management controller. Finally, the BPNN dynamics model and online HDP-MPC strategy are simulated and validated. Through the experimental results analysis, contrasted with the analytic model, the established dynamics model can more effectively reflect the actual motion process of the series hybrid loader with higher accuracy. Moreover, compared with the fuzzy rule-based and adaptive equivalent consumption minimization strategy strategies, the designed HDP-MPC strategy can respectively achieve fuel-savings of 28.65% and 10.38%, supplying a new idea for online energy management of the series hybrid loaders.

In Internet applications, there are more and more demands for geographic information expression technology that integrates video surveillance information, but there are generally problems such as camera position, direction, field of view and other parameters that are difficult to calculate accurately, texture occlusion, etc., which seriously affect the fusion expression of 3D scenes and video information Effect. To address the above problems, in this study, movable objects and trees are removed from the model surface, and an enhanced representation framework is designed for the fusion of video and 3D WebGIS under weak constraints. At the same time, the proposed method is verified and analyzed from three aspects: feature point matching, line coincidence degree and occlusion effect. The experimental results show the following. 1) In terms of the feature point matching ability, the feature point matching ability of the proposed method is 22.22% higher than that under strong constraints. 2) In terms of the occlusion effect, the proposed method achieves a 74.12% reduction in the area of the occlusion area, which is 1.63% higher than the occlusion area reduction rate after video projection under strong constraints. 3) In terms of the straight line coincidence, the experimental results of the proposed method are significantly better than those obtained using the strong constraints method. Both the length difference of the straight line with the same name and the relative offset angle are smaller than the result of the method using the strong constraint conditions. Also, the FPS is stabilized at about 30f/s during system operation with high rendering efficiency, which further proves the feasibility and stability of this system. Experiments show that using the proposed method can reflect real geospatial information, better enhance geographic information expression and be easy to implement, providing a valuable reference for the construction of applications such as real-world 3D models and smart cities.

The entrances of tunnels are generally considered to be accident-prone locations, and accidents related to this area mainly include rear-end and sideswipe crashes, which are also associated with lane-changing behaviours. Despite the extensive literature on the safety of lane-changing, little attention has been given to lane-changing behaviours in front of the tunnel entrance. To this end, this study investigates the contributing factors to the potential risk status of lane-changing, taking into account lane-changing scenarios and the distance to tunnel entrances. Vehicular trajectory data on adjacent sections of tunnel entrances was collected by naturalistic driving tests, and 615 lane-changing scenarios were extracted for analysis. Furthermore, lane-changing risk margin (LCRM) is proposed to estimate whether there is at potential risk during lane-changing. To verify the influence of risk factors on the safety of lane-changing on adjacent sections of tunnel entrances, a mixed logit model is established for different lane-changing scenarios and distance levels to tunnel entrances. The model estimation results indicate that the leading vehicles in the current lane and target lane (defined as CLV and TLV, respectively) significantly affect the risk of lane-changing, with the presence of CLV increasing the probability of performing risky lane-changing as approaching the tunnel entrance, while the presence of TLV instead reduces the probability of performing risky lane-changing behaviour. The distance to the tunnel entrance also has a significant impact on the risk of lane-changing, and the probability of the risk of lane-changing is higher at relatively close distances than at relatively long distances. Moreover, on adjacent sections of tunnel entrances, drivers mainly change lanes to the left, which is likely to be related to safer driving on the left. This implies that drivers approaching the tunnel may try to achieve better driving conditions before entering the tunnel by changing lanes in view of the complex driving conditions inside the tunnel.

Plug-in hybrid electric vehicles (PHEVs) have been validated as a preferable solution to transportation due to its great advantages in fuel economy promotion, harmful emission reduction and mileage anxiety mitigation. While, designing an effective energy management strategy to allocate the power between battery and engine is critical to improve the performance of powertrain in PHEVs. To this end, a real-time energy management strategy is proposed via incorporating double-delay Q-Learning and model predictive control (MPC). First, the energy management for PHEV is transformed into a nonlinear optimal control problem, and the vehicle speed predictor based on convolutional neural network is proposed to forecast vehicle speed in MPC. Then, based on the predicted vehicle speed, the double-delay Q-Learning algorithm is implemented to solve the receding horizon optimal problem in the MPC module. The simulation is conducted to verify the performance of the proposed strategy, and the results showcase that incorporating the double-delay Q-Learning into MPC can effectively improve the adaptability of energy management to dynamic environment, and meanwhile achieve a similar fuel consumption of the offline stochastic dynamic programming-based strategy. In addition, the single-step computation time of the proposed strategy is less than 23 milliseconds, highlighting its significant potential in online implementation.

In this paper, an improved online particle swarm optimization (PSO) is proposed to optimize the traditional search controller for improving the operating efficiency of the permanent magnet synchronous motor (PMSM). This algorithm combines the advantages of the attraction and repulsion PSO and the distributed PSO that can help the search controller to find the optimal d - axis air gap current quickly and accurately under non-stationary operating conditions, thereby minimizing the air gap flux and then improving the motor efficiency. To verify the effectiveness and stability of this proposed algorithm, the operating efficiency of PMSM as using this proposed algorithm is compared with that of traditional search controller under non-stationary operating conditions. The results show that the proposed algorithm can improve the operating efficiency of PMSM by 6.03% on average under non-stationary operation conditions. This indicates that the search controller based on the improved PSO has a better adaptation to the variation of external operating conditions, and can improve the operation efficiency of PMSM under non-stationary condition.

In this paper, we analyze the impact of caching on the performance of a cache enabled system with heterogeneous traffic where one of the users need to be served with confidential data. In this setup, a wireless helper system always serves a dedicated user and it can also serve a user requesting cached content. A cellular network access point is also available to serve the latter user if it cannot retrieve the requested data from the helper’s cache. The impact of caching and secrecy on throughput and delay performance for each user is then examined when the access point can deploy superposition coding to serve both users simultaneously. Two decoding schemes are considered in this work. The first decoding scheme treats interference from parallel transmissions as noise while the second one utilizes the parallel transmission to apply successive decoding for the intended data. Furthermore, network and cache related factors are identified and their impact on the overall performance of the system are analyzed. In order to find the optimal transmission power allocations, two distinct optimization problems are set in this context comparing the two decoding schemes. This will assist to identify the benefits of the considered decoding schemes for each user satisfying the secrecy requirements of the dedicated user and reducing its impact on the overall performance of the system.

Polygon shape is an important factor affecting the efficiency of vector polygon overlay analysis. Aiming at the problems that polygon shape is difficult to accurately measure and different algorithms have different sensitivities to the same polygon in vector polygon overlay analysis, 27 shape variables are selected from literature to establish the shape complexity models for Vatti, Greiner-Hormann and Martinez algorithms by multiple linear regression analysis in this paper. These three algorithms are suitable for arbitrary polygon clipping and include intersection, union, difference and symmetric difference operators. The experimental results show that the calculated performances of the intersection, union, difference and symmetric difference operators belonging to the same algorithm are similar to their sensitivity to the same polygon shape. The fitting correlation coefficient R2 of the shape complexity model based on the Greiner-Hormann algorithm is the highest among the three algorithms, which can explain 99% of the performance of this algorithm.

Driving cycle prediction plays a key role in energy management strategy (EMS) for hybrid electric vehicles (HEVs). This paper studies a driving cycle prediction method based on convolutional neural network (CNN). Firstly, the k-shape clustering method is used to group the driving cycle data into six different types. Moreover, this method is compared with the k-means algorithm which is often used for clustering driving cycles. Secondly, CNN is adopted to predict the different types of the driving cycles based on the results of k-Shape clustering. Some basic features are selected to construct the input of the networks with no assistance of human experience. In the process of training neural networks, some high-level features which can describe the information of a driving cycle more accurately are extracted, and the deep neural networks are built, which are different from traditional experience-based driving cycle prediction methods. And then, the better performance of the proposed method is illustrated by making a comparison with the traditional machine learning method. Finally, an adaptive energy management strategy for plug-in hybrid electric buses (PHEB) based on deep learning is given, and simulation results prove the effectiveness of the proposed method.

Precise estimation of state of health (SOH) are of great importance for proper operation of lithium-ion batteries equipped in electric vehicles. For real applications, it is however difficult to estimate battery SOH due to stochastic operation, which in turn speeds up aging process of the battery. To attain the precise SOH estimation, an efficient estimation manner based on machine learning is proposed in this study. Firstly, the voltage profile during charging and discharging process and incremental capacity variation are acquired through the cycle life test, and the healthy features correlating to battery degradation are extracted. Secondly, the grey relation analysis and entropy weight method are employed to analyze the healthy features. Finally, the long short-term memory is established to achieve the SOH estimation of battery. The experimental results highlight that the proposed method can effectively predict the battery SOH with preferable accuracy, stability and robustness.

Fault detection module is one of the most important components in modern industrial systems. In this paper, we propose a novel fault detection framework which makes use of both normal and faulty measurement signals at the same time. In this framework, the multivariate time series (MTS) pieces which are extracted from measurement signals in a time interval are used as the training and testing samples, and a K-nearest neighbour rule of MTS pieces is applied for fault detection. Moreover, a Mahalanobis distance based dynamic time warping method is used to measure the divergence among MTS pieces, and a one-class metric learning algorithm is proposed to learn the appropriate Mahalanobis distance. Experimental results on the Tennessee Eastman process demonstrate that the proposed method has improved fault detection performance compared with classical approaches on certain kinds of faults.

In this study, an implicit proportional-integral-based generalized predictive controller (PIGPC) is proposed to effectively control temperatures of industrial systems with time-varying delay. The controller is designed to optimize the target function with the proportional-integral structure for improving the controlling performance of implicit PIGPC. Meanwhile, the recursive least square method is leveraged to directly identify the parameters of controller. Compared with the conventional implicit generalized predictive controller, the process of parameter identification converges faster. The Lagrange multiplier method is introduced to solve the optimal control law of implicit PIGPC by considering the input constraints of system. An approximate decoupling controller for multivariable systems is proposed to eliminate the coupling effects. Simulation and experimental results manifest the effectiveness and feasibility of the proposed controller in temperature control systems.

Unmanned surface vessel(USV) has been applied in the maritime security inspection and resources exploration to execute complex works with its advantages in automation and intelligence. While the nonlinear USV working in the complex ocean environment, the good trajectory tracking performance is an important capacity. However, the nonlinearity, modeling uncertainties (e.g., modeling error and parameter variations) and external disturbance (wind, wave, current, etc) are the main difficulties, which deteriorates the control performance. To solve this issue, most existing algorithms for USV's tracking have been developed based on the linearization of the USV's nonlinear dynamic model at specific equilibrium point. However, the integrated effect of nonlinearities, modeling uncertainties and external disturbance has not been well considered, which can degrade the USV's tracking performance. Therefore, to achieve the good tracking performance for USV, a nonlinear dynamic model is strictly derived in this paper with the integrate consideration of abovementioned issues, and an adaptive sliding mode control design using RBFNN(Radial Basis Function Neural Network) and disturbance-observer is subsequently developed, where a RBFNN approximator is designed to approximate and compensate modeling uncertainties, and a disturbance-observer is designed to estimate and compensate the effect of the external disturbance. Furthermore, the global stability of the overall closed-loop system of USV are strictly guaranteed. The comparative simulation is carried out to validate the fast response, better transient performance and robustness of our proposed control design via comparing with the existing methods.

The mobility and resource limitation of nodes are the critical factors that affect the performance of Mobile AD hoc network (MANET). The mobility of nodes will affect the stability of links, and the limitation of node resources will lead to congestion, so it is very difficult to design a routing protocol that supports quality of service (QoS) in MANET. Especially in the scenario of high-speed node movement, frequent link interruption will damages QoS performance, so it is necessary to design MANET routing protocol that can adapt to network topology changes to support QoS. In this paper, we propose a Topological change Adaptive Ad hoc On-demand Multipath Distance Vector (TA-AOMDV) routing protocol, which can adapt to high-speed node movement to support QoS. In this protocol, a stable path selection algorithm is designed, which not only takes node resources (residual energy, available bandwidth and queue length) as the path selection parameters, but also considers the link stability probability between nodes. Furthermore, in order to adapt to the rapid change of topology, link interrupt prediction mechanism is integrated into the protocol, which updates the routing strategy based on periodic probabilistic estimates of link stability. Different scenarios with node speed in the range of 10-50m/s, data rate in the range of 4-40kbps and number of nodes in the range of 10-100 are simulated on NS2 platform. Our results show that the QoS metrics (packet delivery rate, end-to-end delay, and throughput) of the proposed protocol are significantly improved when the node speed is higher than 30m/s although it is slightly better when the node speed is lower than 30m/s. Our on-demand multipath routing protocol demonstrates high potential to support QoS for high-speed MANET.

Capacity prediction of lithium-ion batteries represents an important function of battery management systems. Conventional machine learning-based methods for capacity prediction are inefficient to learn long-term dependencies during capacity degradations. This paper investigates the deep learning method for lithium-ion battery's capacity prediction based on long short-term memory recurrent neural network, which is employed to capture the latent long-term dependence of degraded capacity. The neural network is adaptively optimized by the Adam optimization algorithm, and the dropout technique is exploited to prevent overfitting. Based on the offline cycling aging data of batteries, the capacity prediction performance is validated and evaluated. The experimental results demonstrate that the proposed algorithm can accurately track the nonlinear degradation trend of capacity within the whole lifespan with a maximum error of only 2.84%.

Various complex mechatronic systems are widely applied in industries such as robotics, micro-electro-mechanical systems (MEMS), and motor or hydraulic-driven equipment, and control technology is one of the key issues for mechatronic systems to achieve the desired high performance. However, for those complex mechatronic systems, mechanism nonlinearities and uncertainties (e.g., internal uncertainties, external unstructured environments, and undesired disturbances) are much more obvious and bring significant negative effects. Thus, effective modeling, identification, and dynamic analysis are necessary for complex mechatronic systems with nonlinearity and uncertainty. Model-based advanced control designs, such as adaptive control, robust control, sliding-mode control, backstepping control, and H-infinite control, are the corresponding solutions to improve system performance. Advanced modeling and control is one of the key points in industries for those complex mechatronic systems, where nonlinearity and uncertainty become more and more challenging to pursue higher system performance. The topic of this Special Section in IEEE Access is mainly focused on the categories of control systems, robotics, and automation and also covers other categories such as systems, man, and cybernetics, industrial electronics, and mechanical engineering. We received a total of 104 submissions, and after a rigorous review process, 29 articles have been selected for publication, which are briefly discussed next.

Network embedding has been an effective tool to analyze heterogeneous networks (HNs) by representing nodes in a low-dimensional space. Although many recent methods have been proposed for representation learning of HNs, there is still much room for improvement. Random walks based methods are currently popular methods to learn network embedding; however, they are random and limited by the length of sampled walks, and have difficulty capturing network structural information. Some recent researches proposed using meta paths to express the sample relationship in HNs. Another popular graph learning model, the graph convolutional network (GCN) is known to be capable of better exploitation of network topology, but the current design of GCN is intended for homogenous networks. This paper proposes a novel combination of meta-graph and graph convolution, the meta-graph based graph convolutional networks (MGCN). To fully capture the complex long semantic information, MGCN utilizes different meta-graphs in HNs. As different meta-graphs express different semantic relationships, MGCN learns the weights of different meta-graphs to make up for the loss of semantics when applying GCN. In addition, we improve the current convolution design by adding node self-significance. To validate our model in learning feature representation, we present comprehensive experiments on four real-world datasets and two representation tasks: classification and link prediction. WMGCN’s representations can improve accuracy scores by up to around 10% in comparison to other popular representation learning models. What’s more, WMGCN’feature learning outperforms other popular baselines. The experimental results clearly show our model is superior over other state-of-the-art representation learning algorithms.

The bilateral teleoperation technique has drawn much attention with its attractive superiority to implement the tasks in hazardous environments. Transmission delays and uncertainties are the two main challenges in the nonlinear bilateral teleoperation system to guarantee stability and achieve good transparency performance (including position tracking and force feedback) simultaneously. In this paper, a radial basis function neural network (RBFNN)-based adaptive sliding mode control design is developed for the nonlinear bilateral teleoperation system with transmission delays and uncertainties. For details, the reference trajectory producer is designed in both the master and slave sides to produce the passive reference trajectories for the tracking of master/slave manipulators. The RBFNN-based adaptive sliding mode controller is designed separately for the master and slave to achieve the good tracking performance under system uncertainties. To mitigate the negative effect of transmission delays on the system's stability, a projection mapping by saturation function is applied in the master side to guarantee the boundedness of the delayed environmental torque. Thus, the global stability and the good transparency performance with both position tracking and force feedback can be simultaneously achieved for our proposed method. The comparative experiment is carried out, and the results show the significant performance improvement with our proposed control design.

Autonomous driving is a crucial issue of the automobile industry, and research on lane change is its significant part. Previous works on the autonomous vehicle lane change mainly focused on lane change path planning and path tracking, but autonomous vehicle lane change decision making is rarely mentioned. Therefore, this paper establishes an autonomous lane change decision-making model based on benefit, safety, and tolerance by analyzing the factors of the autonomous vehicle lane change. Then, because of the multi-parameter and non-linearity of the autonomous lane change decision-making process, a support vector machine (SVM) algorithm with the Bayesian parameters optimization is adopted to solve this problem. Finally, we compare a lane change model based on rules with the proposed SVM model in the test set, and results illustrate that the SVM model performs better than the rule-based lane change model. Moreover, the real car experiment is carried out to verify the effectiveness of the decision model.

We investigate the effect of bursty traffic in a long term evolution (LTE) and Wi-Fi aggregation (LWA)-enabled network. The LTE base station routes packets of the same IP flow through the LTE and Wi-Fi links independently. We motivate the use of superposition coding at the LWA-mode Wi-Fi access point (AP) so that it can serve LWA users and Wi-Fi users simultaneously. A random access protocol is applied in such system, which allows the native-mode AP to access the channel with probabilities that depend on the queue size of the LWA-mode AP to avoid impeding the performance of the LWA-enabled network. We analyze the throughput of the native Wi-Fi network and the delay experienced by the LWA users, accounting for the native-mode AP access probability, the traffic flow splitting between LTE and Wi-Fi, and the operating mode of the LWA user with both LTE and Wi-Fi interfaces. Our results show some fundamental tradeoffs in the throughput and delay behavior of LWA-enabled networks, which provide meaningful insight into the operation of such aggregated systems.

The vehicle-environment cooperative (VEC) control has shown a great potential to improve vehicle performance. Consequently, it is desirable to further investigate the incorporation of the VEC control. In this context, a novel method is proposed to predict the velocity profile; meanwhile, the potential of the proposed method is exploited to improve energy management performance of plug-in hybrid electric vehicles (PHEVs). In particular, a specific VEC control framework is first introduced based on the mobile edge computation (MEC). On this basis, a compound velocity profile prediction (CVPP) algorithm is developed, which merges the cloud server (CS), MEC servers, and on-board vehicle control unit (VCU), and provides more accurate and reasonable prediction results. Finally, a case study is conducted that applies the proposed CVPP method to energy management of PHEVs. The simulation results manifest that the performance of the proposed energy management strategy (EMS) is dramatically improved after incorporating the forecasted velocity profile information given by the proposed CVPP method.

Unmanned surface vesssel (USV) has been widely applied due to its advantages in the military reconnaissance and resources exploration. Path planning is one of the critical issues for USV applications, which usually includes global and local path planning methods. However, individual global path planning algorithms may not be easy to detect the dynamic obstacles in the environment, and individual local path planning algorithms may not always guarantee the existence of the feasible solution for the complex environment. Therefore, a hybrid algorithm which effectively combines global and local path planning is proposed in this paper to overcome these drawbacks. The A* algorithm is used in the global path planning to generate a global path for USV to reach the target point. The dynamic window algorithm (DWA) is used in the local path planning to avoid the dynamic obstacles and track the global path by following the local target point which is the intersection of the global and local path planning. The weight coefficient considering sea state is added in the objective function of DWA, where the security of USV can be guaranteed by reducing the weight of velocity and increasing the weight of distance when the sea state level becomes high. Thus, USV can get a global optimal path and reach the target point in complex environment with dynamic obstacles and ocean currents via the proposed hybrid algorithm, and the comparative simulation is carried out to verify the effectiveness and advantage of the proposed method.

Identification of driving intentions has increasingly attracted wide attention since it can be a valuable reference input of vehicle intelligent control systems. In this study, the long short-term memory (LSTM) is employed to identify the longitudinal intention online with high precision. To this end, the driving intentions when the vehicle runs on a straight and flat road are divided into five categories. The vehicle driving states such as the vehicle speed and acceleration are pre-processed to label the road test data. Subsequently, a LSTM classification model is established to identify the driving intention with inputs of opening degree of the accelerator pedal, vehicle speed and brake pedal force. Identification results reveal that the highest accuracy of the proposed algorithm attains 95.36%, which is around 20% higher than that of the traditional back propagation neural network. Finally, a driving intention-perceptive gear shifting strategy is developed with the help of the built recognition algorithm, and simulation results highlight that the strategy can effectively reduce the number of shifts and achieve better fuel economy.

Energy management strategies can directly determine the dynamic performance and fuel economy of plug-in hybrid electric vehicles (PHEVs). In this paper, an adaptive equivalent consumption minimization strategy (A-ECMS) is proposed based on the energy balance principle of the hybrid powertrain of the target vehicle, by which a pair of boundary equivalent factors can be determined according to the future transportation information. Then, the equivalent factor is calculated in real time based on the energy variation in the powertrain system during the operation. Consequently, the torque distribution between the engine and the motor can be determined by solving the Hamilton function according to the dynamically adjusted equivalent factor, and thus, the energy management control is adaptively realized. The simulations were conducted considering three typical driving conditions, different battery aging statuses, and inaccurate road information. The results manifest that the proposed algorithm is feasible to improve the fuel economy with attainable adaptivity and robustness compared with the typical ECMS.

Accurate classification and the effective recognition of driving styles are critical for improving control performance of the vehicle powertrain. In this research, a set of driving style classification and recognition methods is built based on the feature engineering. First, a specified road test is conducted considering the influence factors, and meanwhile, the corresponding driving data is collected, followed by a detailed evaluation of the driving styles. Then, the information entropy is applied to discretize the driving data, including the speed, acceleration, and opening degree of the accelerator pedal, and 44 feature quantities are extracted to characterize the driving style. By analyzing strong correlation and redundancy among the constructed feature quantities, the principal component analysis (PCA) is employed to reduce the dimension, and the fuzzy c-means (FCM) clustering algorithm is leveraged to classify the driving style. The successful classification rate reaches 92.16%, which is improved by 9.81% in comparison with traditional features. Finally, a parameter identification algorithm based on the support vector machine (SVM) is applied to identify the classified driving style, and the recognition accuracy reaches 92.86%, which is improved by 7.15% in comparison with traditional features, proving the feasibility of the proposed algorithm.

Unmanned surface vessel (USV) has been widely applied due to its advantages in the maritime security inspection and resources exploration. Good trajectory tracking performance is a critical issue in the control design of USV. However, most of the existing controllers are designed based on linear dynamic model or do not well consider the integrated effect nonlinearities, various uncertainties (e.g., modeling error and parameter variations) and external disturbance (wind, wave, current, etc). In this paper, a nonlinear dynamic model is established for USV with the integrate consideration of these issues, and a disturbance-observer-based sliding mode control design is subsequently proposed to achieve the good tracking performance, where the observer is to estimate and compensate the modeling uncertainties and external disturbance. Theoretically, the stability of the observer and overall closed-loop system of USV are guaranteed via the Lyapunov theorem. The comparative simulation is carried out, and the results show the fast response, better transient performance and robustness of the proposed control design.

Good transparency performance (e.g., position tracking and force feedback) is an important issue in the control design of teleoperation systems. The four-channel architecture has advantages in the achievement of good transparency performance for teleoperation systems. However, most of the existing four-channel architectures are used in the linear teleoperation system, which cannot be well applied to execute tasks in the increasingly complicated operation environments. Thus, designing the four-channel architecture to achieve good transparency performance for nonlinear teleoperation systems with uncertainties is still a challenging issue. In this paper, the disturbance-observer-based sliding mode control design with a novel four-channel architecture is developed for the nonlinear bilateral teleoperation system to achieve a good transparency performance in the consideration of uncertainties and external disturbance, where the operating torque, master position, slave position, and the environment torque signals are transmitted through the communication channel. The reference tracking planner is designed in both the master and slave sides to produce the passive reference trajectories, and the disturbance-observer-based sliding mode controller is subsequently designed for the master and slave robots to achieve the good position tracking performance. The good force feedback performance can be achieved by the proper selection of plan parameters in the master reference tracking planner. Thus, the good transparency performance with both position tracking and force feedback can be achieved for the nonlinear bilateral teleoperation system with this novel four-channel architecture. The comparative simulation and experiment are implemented, and the results demonstrate good transparency performance achievement with the proposed control design.

In recent years, the popularity of depth sensors and 3D scanners has led to a rapid development of 3D point clouds. Semantic segmentation of point cloud, as a key step in understanding 3D scenes, has attracted extensive attention of researchers. Recent advances in this topic are dominantly led by deep learning-based methods. In this paper, we provide a survey covering various aspects ranging from indirect segmentation to direct segmentation. Firstly, we review methods of indirect segmentation based on multi-views and voxel grids, as well as direct segmentation methods from different perspectives including point ordering, multi-scale, feature fusion and fusion of graph convolutional neural network (GCNN). Then, the common datasets for point cloud segmentation are exposed to help researchers choose which one is the most suitable for their tasks. Following that, we devote a part of the paper to analyze the quantitative results of these methods. Finally, the development trend of point cloud semantic segmentation technology is prospected.

This paper proposes a fusion model based on the autoregressive moving average (ARMA) model and Elman neural network (NN) to achieve accurate prediction for the state of health (SOH) of lithium-ion batteries. First, the voltage and capacity degradation variation of the battery are acquired through the battery lifecycle data, and the health factor related to the battery aging is selected according to the variation of the voltage profile. Second, the empirical mode decomposition (EMD) is employed to process the capacity degradation data and eliminate the phenomenon of tiny capacity recovery, and multiple data sequences, as well as the related residue, are extracted, then the grey relational analysis (GRA) between sub-sequences and health factor are discussed. Furthermore, the ARMA model and Elman NN model are respectively built by training the subsequent time series data and residue data. Finally, all the individual predictions are combined to generate the estimated SOH sequences. The experimental validation is performed to manifest that the addressed fusion method performs the SOH prediction with satisfactory accuracy, compared with the single ARMA method and Elman NN model.

Energy management strategies play a critical role in performance optimization of plug-in hybrid electric vehicles (PHEVs). In order to attain effective energy distribution of PHEVs, a predictive energy management strategy is proposed in this study based on real-time traffic information. First, an exponentially varied model for the velocity prediction is established, of which the tunable decay coefficient is regulated by the supported vector machine (SVM). In this manner, the prediction precision is improved. Then, by properly simplifying the powertrain model, the state of charge (SOC) reference trajectory is generated based on the fast dynamic programming (DP) with fast calculation speed and consideration of the traffic information. Moreover, the typical DP algorithm is leveraged to solve the nonlinear rolling optimization problem for minimizing the operating cost in a receding horizon. Simulation results demonstrate that the proposed algorithm can reach 92.83% operating savings, compared with that of the traditional DP; and save 6.18% cost compared with the MPC algorithm only with reference of the trip duration.

Hydraulic exoskeleton is a kind of the human robot interaction system, which can augment the human performance in the application of heavy load carrying. Due to the existence of complicated multijoint nonlinear dynamics and various uncertainties, traditional robust control of these systems is hard to be realized in most of the practical research. In this paper, an adaptive robust cascade force controller is proposed for 3-DOF hydraulic leg exoskeleton to achieve accurate tracking of human motion. Specifically, the control strategy includes two levels. The desired joint positions, which can be assumed as the human motion intent as well, are generated in the high-level by attenuating the integral of human-machine interaction force. And in the low-level, an observer-based MIMO motion controller is developed for 3-DOF dynamics to track the generated human motion intent accurately. Adaptive robust control algorithms are developed in both control levels to address the strongly coupled high-order dynamics under parametric uncertainties and uncertain disturbances. Comparative simulations show that the human-machine interaction force can be attenuated exactly and robust performance to various uncertainties can be guaranteed, validating the effectiveness of the proposed scheme.

Good transparency performance (including position tracking and force feedback) is a significant issue for teleoperation control design. The four-channel control approach, which is based on the impedance matching between the master and slave, is an effective method to achieve the perfect transparency performance of teleoperation system. However, the obvious drawback of this design is that the time delay existing in the communication channel is neglected and thus the stability of this design under time delay cannot be guaranteed. Some passivity based control approaches such as wave-variable are developed to handle the stability problem of teleoperation system under time delay, and have been tried to add into four-channel control design. Though the stability under time delay is achieved for this kind of design, the transparency performance may largely decrease since the traditional wave-variable may essentially suffer some performance limitations (e.g., wave reflection). Thus, simultaneously realizing the stability and good transparency performance for four channel teleoperation system is still a challenging issue. In this paper, an improved wave-variable based four-channel control design is developed for bilateral teleoperation system to guarantee stability and further improve transparency performance. Under four channel control architecture, the passivity based time-delay compensator by the modified wave transform and the local force feedback are provided to compensate the distortion by reducing wave reflection and further enhance the stability and transparency of teleoperation system. The experiment is carried out, and the results show the great improvement of the proposed method to achieve the good transparency performance compared to the previous four-channel control designs.

In this paper, we investigate the effect of bursty traffic and random availability of caching helpers in a wireless caching system. More explicitly, we consider a general system consisting of a caching helper with its dedicated user in proximity and another non-dedicated user requesting for content. Both the non-dedicated user and the helper have limited storage capabilities. When the user is not able to locate the requested content in its own cache, then its request shall be served either by the caching helper or by a large data center. Assuming bursty request arrivals at the caching helper from its dedicated destination, its availability to serve other users is affected by the request rate, which will further affect the system throughput and the delay experienced by the non-dedicated user. We characterize the maximum weighted throughput and the average delay per packet of the considered system, taking into account the request arrival rate of the caching helper, the request probability of the user, and the availability of the data center. Our results provide fundamental insights in the throughput and delay behavior of such systems, which are essential for further investigation in larger topologies.

This paper proposes a modular teleoperation software platform based on the robot operating system (ROS), which is flexible and portable for different master and slave devices. The main issues for the modularity are to develop its control modules in MATLAB/Simulink and access ROS by the robotics system toolbox, which will facilitate the design, analysis, and adjustment of the control algorithm in teleoperation systems greatly. In addition, a case study for this kind of modular teleoperation system is carried out, which is composed of a Geomagic Touch as the master device and a robotic arm of the Rethink Baxter robot as the slave device. Due to the asymmetric structure property between the master and slave devices, the proper workspace mapping which can cover the whole workspace of the slave robot and simultaneously achieve an excellent mapping accuracy becomes a challenging issue. Different from the traditional methods, a hybrid workspace mapping algorithm is proposed, which uses the joint space mapping to cover the whole workspace of the slave device and the operating space mapping to conduct the elaborate manipulation. A smoothing switch law between the joint space mapping and the operating space mapping is also designed. Comparative experiments are carried out, and the results verify the effectiveness and excellent performance of the proposed design methodology.

Multilateral teleoperation system, which is regarded as an extension of bilateral teleoperation system, is a significant issue and becomes a hotspot in recent years to improve the ability of multiple robots to coordinate and perform efficiently and accurately in the complicated, dangerous and remote working environment. In this paper, a four-channel multilateral teleoperation control problem is discussed, where n masters control the n slaves to handle dangerous, unknown, and complicated tasks remotely under time varying delays in the communication channel. A novel multilateral teleoperation system combining the time-delay compensator by the novel wave transform and four-channel architecture is built to meet the demands of the communication among multiple masters and slaves, where the weighing coefficients are provided to perform different weighed commands. The two design parameters are provided separately in the time-delay compensator to optimize the signal transmission and guarantee the stability in the four-channel architecture, and the ideal transparency conditions are theoretically derived to simultaneously achieve the stability and good transparency performance for multilateral teleoperation system. The practical experiments are carried out on a 2-master-2-slave teleoperation system to vilify the effectiveness of the proposed control scheme.

In this paper, a hierarchical energy management strategy is proposed to achieve optimal energy distribution in plug-in hybrid electric vehicles by dividing the energy management algorithm into two layers. Between two control layers, a novel velocity-prediction method based on wavelet transformation and a radial basis function neural network is introduced to realize accurate vehicle speed prediction. To simplify the problem, a quadratic optimization method is employed to find the optimal state-of-charge (SOC) trajectory in the upper layer and the calculation time can be minimized to be within 400 ms. Model predictive control is established simultaneously in the bottom layer to achieve fast, and local energy management based on the predicted velocity and the planned SOC trajectory. Simulation results show that the proposed method can increase the accuracy of the velocity prediction and improve the fuel economy with a fast calculation speed.

Image-to-image translation usually refers to the task of translating an input image from the source domain to the target domain while preserving the structure in the source domain. Recently, generative adversarial networks (GANs) using paired images for this task have made great progress. However, paired training data will not be available for many tasks. In this paper, a GAN-based unsupervised transformation network (UTN-GAN) is proposed for image-to-image translation. Importantly, UTN-GAN employs hierarchical representations and weight-sharing mechanism to translate images from the source domain to the target domain without paired images. We employ two groups of unsupervised GANs to generate the images in different domains first, and then discriminate them. In UTN-GAN, an auto-encoder reconstruction network is designed to extract the hierarchical representations of the images in the source domain by minimizing the reconstruction loss. In particular, the high-level representations (semantics) are shared with a translation network to guarantee that the input image and the output image are paired up in the different domains. All network structures are trained together by using a joint loss function. The experimental studies in qualitative and quantitative aspects on several image translation tasks show that the proposed algorithm is effective and competitive compared with some state-of-the-art algorithms.

Four-wheel independently driven skid-steered mobile robots are widely used in the fields of industrial automation and outdoor exploration. In most of existing controllers of skid-steered mobile robots, the wheel velocities are controlled independently to track the desired velocities from the high-level kinematic controller. However, this kind of control method may lead to chattering phenomenon of skidsteered mobile robots in practice, when the desired velocity commands of four wheels are not matched under different ground conditions. In this paper, the coordinated control problem is investigated for the four-wheel independently driven skid steer mobile robots, so as to solve the chattering phenomenon and also achieve good control performance under different ground conditions. Since the mobile robots are overactuated and lack of suspension systems, a coordinated adaptive robust control scheme integrated with torque allocation technique is proposed. First, an adaptive robust control law is developed to attenuate the negative effects of load variations and uncertainties. Second, instead of directly giving the desired velocity commands, a torque control and allocation algorithm is developed to regulate the driving torque of each wheel motor. A coordinated control law with considering the wheel slip compensation is also proposed. Comparative experiments are carried out, and the results show the proposed scheme can avoid the chattering problem and achieve the excellent performance under different ground conditions.

The scheduling of smart home appliance electricity tasks can effectively distribute power loads and enhance the operational efficiency and stability of power grids. Additionally, effectively shifting the working hours of household appliances to off-peak hours with lower electricity prices could reduce the cost of household electricity consumption. Therefore, we aim to study a bi-objective household appliance scheduling problem under time-of-use (TOU) and threshold-based pricing tariffs, where the objectives are to simultaneously minimize the total electricity usage cost and peak load within a day. A bi-objective mathematical model is first presented for this problem. To the best of our knowledge, this is the first study to propose a novel bi-objective chemical reaction optimization (CRO) algorithm with problem-specific encoding and decoding schemes, as well as molecular reaction operators to solve the studied problem. Finally, the computational results of well-known multi-objective optimization benchmark instances and case study were reported and analyzed. The results indicate that our proposed approach achieves a better balance between exploitation and exploration, yields high-quality trade-offs between reducing the electricity cost and peak load, and outperforms existing algorithms in terms of solution quality and diversity.