Search Results (135)

The realization of hand function reengineering using a manipulator is a research hotspot in the field of robotics. In this paper, we propose a multimodal perception and control method for a robotic hand to assist the disabled. The movement of the human hand can be divided into two parts: the coordination of the posture of the fingers, and the coordination of the timing of grasping and releasing objects. Therefore, we first used a pinhole camera to construct a visual device suitable for finger mounting, and preclassified the shape of the object based on YOLOv8; then, a filtering process using multi-frame synthesized point cloud data from miniature 2D Lidar, and DBSCAN algorithm clustering objects and the DTW algorithm, was proposed to further identify the cross-sectional shape and size of the grasped part of the object and realize control of the robot’s grasping gesture; finally, a multimodal perception and control method for prosthetic hands was proposed. To control the grasping attitude, a fusion algorithm based on information of upper limb motion state, hand position, and lesser toe haptics was proposed to realize control of the robotic grasping process with a human in the ring. The device designed in this paper does not contact the human skin, does not produce discomfort, and the completion rate of the grasping process experiment reached 91.63%, which indicates that the proposed control method has feasibility and applicability. Full article

With the rapid increase in vehicle ownership and increasingly stringent emission regulations, addressing the energy consumption of and emissions from commercial vehicles have become critical challenges. This study introduces a multi-objective cooperative adaptive cruise control (CACC) strategy, designed for intelligent connected commercial vehicle platoons, operating in event-triggered conditions. A hierarchical control framework is utilized: the upper layer handles reference speed planning based on vehicle dynamics and constraints, while the lower layer uses distributed model predictive control (DMPC) to manage vehicle following. DMPC is chosen for its ability to manage distributed platoons by enabling vehicles to make local decisions, while maintaining system-wide coordination. Additionally, adaptive particle swarm optimization (APSO) is employed during the optimization process to solve the optimal problem efficiently. APSO is employed for its computational efficiency and adaptability, ensuring quick convergence to optimal solutions with reduced overheads. An event-triggering mechanism is integrated to further reduce the computational demands. The simulation results show that the proposed approach reduces fuel consumption by 8.05% and NO_x emissions by 10.15%, while ensuring stable platoon operation during dynamic driving conditions. The effectiveness of the control strategy is validated through extensive simulations, highlighting superior performance compared to conventional methods. Full article

As the penetration of renewable energy increases, the distribution grid faces great challenges in integrating large amounts of distributed energy sources and dealing with their output uncertainty. To address this, a multi-time scale optimal dispatch method based on model predictive control is proposed, including a day-ahead stage and an intra-day rolling stage. In the day-ahead stage, to fully utilize the flexibility of variable speed pumped storage hydropower, the generating/pumping phase modulation condition is considered, not just generating or pumping. Day-ahead optimal dispatch is established with the objective of minimizing the operation economy and node voltage deviation of the distribution network. In the intra-day rolling stage, model predictive control with finite time domain rolling optimal dispatch is used to replace the traditional single-time section optimal dispatch, considering the forecast data of wind, photovoltaic (PV), and load within the finite time domain, so that can respond in advance to smooth the generator output. At the same time, the uncertainty problem of the distribution network is solved effectively by rolling optimization and feedback correction of model predictive control. In order to consider the daily operating capacity balance of energy storage in the intra-day stage, the capacity imbalance penalty is added to the intra-day rolling optimization objective function, so that the energy storage capacity tries to track the results of the day-ahead optimization, achieving the long-term development of energy storage. Simulation analysis proves the feasibility and effectiveness of the proposed method. The proposed method enhances the generation–load–storage coordinated dispatching ability, effectively improving the distribution network’s capability to respond to fluctuations of renewable energy. Full article

Arterial green wave control can effectively reduce the delay time and number of stops of the coordinated traffic flows. However, existing arterial green wave control methods mostly focus on motor vehicles and provide them with green wave bands, neglecting the electric bicycles that are widespread on the roads. In fact, electric bicycles have become an important tool for short-to-medium trips among urban residents because they are convenient, low-cost, and eco-friendly. To tackle this, an arterial multi-path green wave control model that considers both motor vehicles(cars and buses) and electric bicycles is presented in this paper. The presented model is formulated as a mixed integer linear programming problem. The optimization objective of the model is to maximize the sum of the green wave bandwidths for all coordinated paths of each traffic mode on all road segments. The key constraints of the presented model can be addressed by analyzing the relationships among the green wave bandwidth, coordinated path, common cycle time, offset, phase sequence, etc., to utilize the time–space diagram. The results of the numerical example show that compared with the traditional model for through motor vehicles (cars and buses), the total green wave bandwidths of cars, buses, and electric bicycles generated by the presented model at the entire arterial level has been increased by 36.8%, 47.9%, and 19.3%, respectively. Full article

In order to improve the separation control effect of an oscillating jet, the external flow field of the actuators and the wing wake are obtained via hot-wire measurements to optimize the actuator and achieve wing separation flow control. The optimization objectives are to improve the sweeping uniformity and range of the jet. In the present study, the PCA method is used for the modal decomposition of the velocity distribution. The modal-based actuator evaluation parameters are proposed, and the kriging surrogate models of the modal coefficients (principal components) on the actuator parameters are established. The multi-objective genetic algorithm was utilized to complete the optimization of the actuator, and the effect of flow separation control on the wing was verified. The results show that three patterns exist in the time-averaged velocity distribution of the external flow field: unimodal, broad and bimodal, from unimodal to bimodal, the degree of the jet sweeping uniformity gradually decreases, and the sweeping range gradually increases. The pattern of the velocity distribution modals affects the degree of jet sweeping uniformity, while the distance of the modal peaks affects the jet sweeping range. The two evaluation parameters are negatively correlated: insufficient sweeping range or poor sweeping uniformity of the jet are not conducive to wing separation flow control, and the two must be coordinated to achieve the optimal control effect. Full article

Ship welding is a crucial part of ship building, requiring higher levels of robot coordination and working efficiency than ever before. To this end, this paper studies the coordinated ship-welding task, which involves multi-robot welding of multiple weld lines consisting of synchronous ones to be executed by a pair of robots and normal ones that can be executed by one robot. To evaluate working efficiency, the objectives of optimal lazy robot ratio and energy consumption were considered, which are tackled by the proposed dynamic Kuhn–Munkres-based model-free policy gradient (DKM-MFPG) reinforcement learning algorithm. In DKM-MFPG, a dynamic Kuhn–Munkres (DKM) dispatcher is designed based on weld line and co-welding robot position information obtained by the wireless sensors, such that robots always have dispatched weld lines in real-time and the lazy robot ratio is 0. Simultaneously, a model-free policy gradient (MFPG) based on reinforcement learning is designed to achieve the energy-optimal motion control for all robots. The optimal lazy robot ratio of the DKM dispatcher and the network convergence of MFPG are theoretically analyzed. Furthermore, the performance of DKM-MFPG is simulated with variant settings of welding scenarios and compared with baseline optimization methods. Compared to the four baselines, DKM-MFPG owns a slight performance advantage within 1% on energy consumption and reduces the average lazy robot ratio by 11.30%, 10.99%, 8.27%, and 10.39%. Full article

Direct yaw moment control (DYC) and differential drive-assist steering (DDAS) for distributed-drive vehicles are both realized by allocating the in-wheel motor torque. To address the interference caused by overlapping control objectives, this paper proposes a multilayer control strategy that integrates DYC and DDAS, consisting of an upper controller, a coordinated decision layer, and a torque distribution layer. The upper controller, designed based on the vehicle’s dynamic characteristics, incorporates an adaptive fuzzy control DYC system and a dual PID control DDAS system. The coordinated decision layer is developed utilizing a phase-plane dynamic weighting method, delineating region boundaries by applying the double-line and limit cycle methods. The torque distribution strategy is formulated considering motor peak torque and road adhesion conditions. Multi-condition joint simulation experiments indicate that the proposed multilayer control strategy, integrating the advantages of DYC and DDAS, reduces peak steering wheel torque by approximately 10%, peak yaw rate by around 25%, peak sideslip angle by roughly 29%, and peak sideslip angle rate by about 19%, significantly improving driving stability and maneuvering flexibility. Full article

To reduce production costs, environmental effects, and crop losses, tomato leaf disease recognition must be accurate and fast. Early diagnosis and treatment are necessary to cure and control illnesses and ensure tomato output and quality. The YOLOv5m was improved by using C3NN modules and Bidirectional Feature Pyramid Network (BiFPN) architecture. The C3NN modules were designed by integrating several soft attention modules into the C3 module: the Convolutional Block Attention Module (CBAM), Squeeze and Excitation Network (SE), Efficient Channel Attention (ECA), and Coordinate Attention (CA). The C3 modules in the Backbone and Head of YOLOv5 model were replaced with the C3NN to improve feature representation and object detection accuracy. The BiFPN architecture was implemented in the Neck of the YOLOv5 model to effectively merge multi-scale features and improve the accuracy of object detection. Among the various combinations for the improved YOLOv5m model, the C3ECA-BiFPN-C3ECA-YOLOv5m achieved a precision (P) of 87.764%, a recall (R) of 87.201%, an F1 of 87.482, an mAP.5 of 90.401%, and an mAP.5:.95 of 68.803%. In comparison with the YOLOv5m and Faster-RCNN models, the improved models showed improvement in P by 1.36% and 7.80%, R by 4.99% and 5.51%, F1 by 3.18% and 6.86%, mAP.5 by 1.74% and 2.90%, and mAP.5:.95 by 3.26% and 4.84%, respectively. These results demonstrate that the improved models have effective tomato leaf disease recognition capabilities and are expected to contribute significantly to the development of plant disease detection technology. Full article

The existing trajectory planning research mainly considers the safety of the obstacle avoidance process rather than the anti-rollover requirements of heavy vehicles. When there are driving risks such as rollover and collision, how to coordinate the game relationship between the two is the key technical problem to realizing the anti-rollover trajectory planning under the condition of driving risk triggering. Given the above problems, this paper studies the non-cooperative game model construction method of the obstacle avoidance process that integrates the vehicle driving risk in a complex traffic environment. Then it obtains the obstacle avoidance area that satisfies both the collision and rollover profit requirements based on the Nash equilibrium. A Kmeans-SMOTE risk clustering fusion is proposed in this paper, in which more sampling points are supplemented by the SMOTE oversampling method, and then the ideal obstacle avoidance area is obtained through clustering algorithm fusion to determine the optimal feasible area for obstacle avoidance trajectory planning. On this basis, to solve the convergence problems of the existing multi-objective particle swarm optimization algorithm and analyze the influence of weight parameters and the diversity of the optimization process, this paper proposes an anti-rollover trajectory planning method based on the improved cosine variable weight factor MOPSO algorithm. The simulation results show that the trajectory obtained based on the method proposed in this paper can effectively improve the anti-rollover performance of the controlled vehicle while avoiding obstacles. Full article

This paper presents a collaborative manufacturing cell implemented in a laboratory setting, focusing on developing learning-based interaction abilities to enhance versatility and ease of use. The key components of the system include 3D real-time volumetric monitoring for safety, visual recognition of hand gestures for human-to-robot communication, classification of physical-contact-based interaction primitives during handover operations, and detection of hand–object interactions to anticipate human intentions. Due to the nature and complexity of perception, deep-learning-based techniques were used to enhance robustness and adaptability. The main components are integrated in a system containing multiple functionalities, coordinated through a dedicated state machine. This ensures appropriate actions and reactions based on events, enabling the execution of specific modules to complete a given multi-step task. An ROS-based architecture supports the software infrastructure among sensor interfacing, data processing, and robot and gripper controllers nodes. The result is demonstrated by a functional use case that involves multiple tasks and behaviors, paving the way for the deployment of more advanced collaborative cells in manufacturing contexts. Full article

The growing popularity of plug-in hybrid electric vehicles (PHEVs) is due to their environmental advantages. But uncoordinated charging of a large number of PHEVs can lead to a significant surge in peak loads and higher charging costs for PHEV owners. To end this, this paper introduces an innovative approach to address the issue by proposing a multi-objective weighting control for coordinated charging of PHEVs in a future smart grid, which aims to find an economically optimal solution while also considering load stabilization with large-scale PHEV penetration. Technical constraints related to the owner’s demand and power limitations are considered. In the proposed approach, the charging behavior of PHEV owners is modeled by a normal distribution. It is observed that owners typically start charging their vehicles when they arrive home and stop charging when they go to their workplace. The charging cost is then calculated based on the tiered electricity price and charging power. By adjusting the cost weighting factor and the load stability weighting factor in the multi-objective function, the grid allows for flexible weight selection between the two objectives. This approach effectively encourages owners to actively participate in coordinated charging scheduling, which sets it apart from existing works. The algorithm offers better robustness and adaptability for large-scale PHEV penetration, making it highly relevant for the future smart grid. Finally, numerical simulations are presented to demonstrate the desirable performance of theory and simulation. Full article

In recent years, the development of neural networks has significantly advanced their application in Synthetic Aperture Radar (SAR) ship target detection for maritime traffic control and ship management. However, traditional neural network architectures are often complex and resource intensive, making them unsuitable for deployment on artificial satellites. To address this issue, this paper proposes a lightweight neural network: the Multi-Scale SAR Ship Detection Network (MSSD-Net). Initially, the MobileOne network module is employed to construct the backbone network for feature extraction from SAR images. Subsequently, a Multi-Scale Coordinate Attention (MSCA) module is designed to enhance the network’s capability to process contextual information. This is followed by the integration of features across different scales using an FPN + PAN structure. Lastly, an Anchor-Free approach is utilized for the rapid detection of ship targets. To evaluate the performance of MSSD-Net, we conducted extensive experiments on the Synthetic Aperture Radar Ship Detection Dataset (SSDD) and SAR-Ship-Dataset. Our experimental results demonstrate that MSSD-Net achieves a mean average precision (mAP) of 98.02% on the SSDD while maintaining a compact model size of only 1.635 million parameters. This indicates that MSSD-Net effectively reduces model complexity without compromising its ability to achieve high accuracy in object detection tasks. Full article

In the field of electrical engineering, there is an increasing concern among managers and operators about the secure and cost-efficient operation of smart power systems in response to disturbances caused by physical cyber attacks and natural disasters. This paper introduces an innovative framework for the hybrid, coordinated control of Unified Power Flow Controllers (UPFCs) and Power System Stabilizers (PSSs) within a power system. The primary objective of this framework is to enhance the system’s security metrics, including stability and resilience, while also considering the operational costs associated with defending against cyber-physical attacks. The main novelty of this paper lies in the introduction of a real-time online framework that optimally coordinates a power system stabilizer, power oscillation damper, and unified power flow controller to enhance the power system’s resilience against transient disturbances caused by cyber-physical attacks. The proposed approach considers technical performance indicators of power systems, such as voltage fluctuations and losses, in addition to economic objectives, when determining the optimal dynamic coordination of UPFCs and PSSs—aspects that have been neglected in previous modern research. To address the optimization problem, a novel multi-objective search algorithm inspired by Harris hawks, known as the Multi-Objective Harris Hawks (MOHH) algorithm, was developed. This algorithm is crucial in identifying the optimal controller coefficient settings. The proposed methodology was tested using standard IEEE9-bus and IEEE39-bus test systems. Simulation results demonstrate the effectiveness and efficiency of this approach in achieving optimal system recovery, both technically and economically, in the face of cyber-physical attacks. Full article

The symmetry principle has significant guiding value in vehicle dynamics modeling and motion control. In complex driving scenarios, there are problems of low accuracy and large time delay in the trajectory tracking control of unmanned ground vehicles. In order to solve this problem and improve the motion control of unmanned ground vehicles, a vehicle coordination control method based on chaotic particle swarm optimization (CPSO) and model predictive control (MPC) algorithms is proposed. To achieve coordinated control of vehicle trajectory tracking and yaw stability, a model predictive controller was designed with the objective of minimizing trajectory tracking errors and yaw stability tracking errors. The required front wheel angle and yaw torque control variables were obtained by solving nonlinear constraint optimization. At the same time, considering the problems of low computational efficiency, high solving time, and local optimization in model predictive control, a chaotic particle swarm optimization algorithm is introduced to solve the optimization constraint problem within model predictive control, thereby effectively improving the computational efficiency and accuracy of the model predictive trajectory tracking controller. The results show that compared with MPC, the multi-objective function optimization solution time and vehicle lane changing time of CPSOMPC improved by 24.51% and 7.21%, respectively, which indicates the coordinated control method that combines the CPSO and MPC algorithms can effectively improve trajectory tracking performance while ensuring vehicle lateral stability. Full article

Equivalent combustion natural gas engines typically utilize exhaust gas recirculation (EGR) systems to tackle their high thermal burden and NOx emissions. Variable nozzle turbochargers (VNT) can increase the engine intake and EGR rate simultaneously, resulting in NOx reduction while ensuring robust power performance. Using a VNT along with engine bench testing, the impact of VNT- and EGR-coordinated control on the performance and emissions of equivalent combustion natural gas engines was investigated under different operating conditions. Subsequently, multi-objective optimization was performed using a support vector machine. The results demonstrated that the use of VNTs in equivalent combustion natural gas engines could bolster the capacity to introduce EGR under several operative conditions and extend the scope of EGR regulation, thereby decreasing the engine’s thermal burden, improving fuel efficiency, and curbing emissions. Owing to the implementation of a multi-objective optimization method based on a support vector regression model and NSGA-II genetic algorithm, VNT and EGR control parameters could be optimized to slightly improve the economy and significantly reduce NOx emissions while maintaining the original engine power performance. At 20 operating points optimized for validation, brake-specific fuel consumption (BSFC) and NOx decreased by 0.94% and 47.0%, respectively, and CH₄ increased by 3.7%, on average. Full article