Search Results (557)

Despite rapid advances in robot learning, the coordination of closed-loop manipulation in cluttered environments remains a challenging and relatively underexplored problem. We present a novel two-level hierarchical architecture for a depth vision-equipped robotic arm that integrates pushing, grasping, and high-level decision making. Central to our approach is a prioritised action–selection mechanism that facilitates efficient early-stage learning via behaviour cloning (BC), while enabling scalable exploration through reinforcement learning (RL). A high-level decision neural network (DNN) selects between grasping and pushing actions, and two low-level action neural networks (ANNs) execute the selected primitive. The DNN is trained with RL, while the ANNs follow a hybrid learning scheme combining BC and RL. Notably, we introduce an automated demonstration generator based on oriented bounding boxes, eliminating the need for manual data collection and enabling precise, reproducible BC training signals. We evaluate our method on a challenging manipulation task involving five closely packed cubic objects. Our system achieves a completion rate (CR) of 100%, an average grasping success (AGS) of 93.1% per completion, and only 7.8 average decisions taken for completion (DTC). Comparative analysis against three baselines—a grasping-only policy, a fixed grasp-then-push sequence, and a cloned demonstration policy—highlights the necessity of dynamic decision making and the efficiency of our hierarchical design. In particular, the baselines yield lower AGS (86.6%) and higher DTC (10.6 and 11.4) scores, underscoring the advantages of content-aware, closed-loop control. These results demonstrate that our architecture supports robust, adaptive manipulation and scalable learning, offering a promising direction for autonomous skill coordination in complex environments. Full article

Modern traffic management requires sophisticated approaches to address the complexities of urban road networks, which continue to grow in complexity due to increasing urbanization and vehicle usage. Traditional methods often fall short in mitigating congestion and optimizing traffic flow, inducing the exploration of innovative traffic control strategies based on advanced theoretical frameworks. In this sense, we explore different game theory-based control strategies in an eight-intersection traffic network modeled by means of hybrid systems and graph theory, using a software simulator that combines the multi-modal traffic simulation software VISSIM and MATLAB to integrate traffic network parameters and population game criteria. Across five distinct network scenarios with varying saturation conditions, we explore a fixed-time scheme of signaling by means of fictitious play dynamics and adaptive schemes, using dynamics such as Smith, replicator, Logit and Brown–Von Neumann–Nash (BNN). Results show better performance for Smith and replicator dynamics in terms of traffic parameters both for fixed and variable signaling times, with an interesting outcome of fictitious play over BNN and Logit. Full article

This study presents a hybrid adaptive control strategy that integrates genetic algorithm (GA) optimization with an adaptive neuro-fuzzy inference system (ANFIS) for precise thermal regulation of single-photon avalanche diodes (SPADs). To address the nonlinear and disturbance-sensitive dynamics of SPAD systems, a performance-oriented dataset is constructed through multi-scenario simulations using settling time, overshoot, and steady-state error as fitness metrics. The genetic algorithm (GA) facilitates broad exploration of the proportional–integral–derivative (PID) controller parameter space while ensuring control stability by discarding low-performing gain combinations. The resulting high-quality dataset is used to train the ANFIS model, enabling real-time, adaptive tuning of PID gains. Simulation results demonstrate that the proposed GA-ANFIS-PID controller significantly enhances dynamic response, robustness, and adaptability over both the conventional Ziegler–Nichols PID and GA-only PID schemes. The controller maintains stability under structural perturbations and abrupt thermal disturbances without the need for offline retuning, owing to the real-time inference capabilities of the ANFIS model. By combining global evolutionary optimization with intelligent online adaptation, this approach improves both accuracy and generalization, offering a practical and scalable solution for SPAD thermal management in demanding environments such as quantum communication, sensing, and single-photon detection platforms. Full article

This study focuses on the multi-objective coordination problem in university campus planning and design, proposing an optimized methodology integrating an improved multi-objective decision-making framework. A five-dimensional objective system—comprising energy efficiency, spatial quality, economic cost, ecological benefits, and cultural expression—was established, alongside the identification and standardization of 29 key variables to construct mapping relationships among objective functions. On the algorithmic level, an adapted NSGA-III was implemented on the MATLAB platform (version R2022b), introducing a dynamic reference point mechanism and hybrid constraint-handling strategy to enhance convergence and solution diversity. Taking the northern residential area of the western campus of Dalian University of Technology as a case study, multiple Pareto-optimal solutions were generated. Five representative alternatives were selected and evaluated through the AHP–TOPSIS method to determine the optimal scheme. The results indicated significant improvements in energy, economic, spatial, and ecological dimensions, while also achieving quantifiable control over cultural expression. On this basis, an integrated optimization strategy targeting “form–function–environment–culture” was proposed, offering data-informed support and procedural reference for systematic campus planning. This study demonstrates the effectiveness, adaptability, and practical value of the proposed approach in addressing multi-objective conflicts in university planning. Full article

Inertial confinement fusion (ICF) stands as one of the approaches to achieve controlled thermonuclear fusion, capable of supplying humans with abundant, economical, and safe energy. In this study, the high-order hybrid compact–WENO scheme is employed to simulate Rayleigh–Taylor instability (RTI) phenomena, one of the challenges hindering the realization of ICF, and to investigate the interaction of RTI phenomena in a multi-layer fluid system. To ensure a more reasonable comparison, the corresponding initial and boundary conditions for three-layer and four-layer fluids are derived based on the same Atwood number. Numerical results show that with the development of RTI phenomena, the interaction between interfaces can be gradually observed. The number of fluid layers exhibits an inhibitory effect on the development of RTI phenomena. When a pair of spike and bubble at two adjacent interfaces reach the same height, the evolution of the spike–bubble gap changes significantly. Full article

Uncoordinated electric vehicle (EV) charging can significantly complicate power system operations. In this paper, we develop a hybrid EV charging method that seamlessly integrates centralized EV charging and distributed control schemes to address EV energy demand challenges. The proposed method includes (1) a centralized day-ahead optimal scheduling mechanism and EV shifting process based on mixed-integer linear programming (MILP) and (2) a distributed control strategy based on a genetic algorithm (GA) that dynamically adjusts the charging rate in real-time grid scenarios. The MILP minimizes energy imbalance at overloaded slots by reallocating EVs based on supply–demand mismatch. By combining full and minimum charging strategies with MILP-based shifting, the method significantly reduces network stress due to EV charging. The centralized model schedules time slots using valley-filling and EV-specific constraints, and the local GA-based distributed control adjusts charging currents based on minimum energy, system availability, waiting time, and a priority index (PI). This PI enables user prioritization in both the EV shifting process and power allocation decisions. The method is validated using demand data on a radial feeder with residential and commercial load profiles. Simulation results demonstrate that the proposed hybrid EV charging framework significantly improves grid-level efficiency and user satisfaction. Compared to the baseline without EV integration, the average-to-peak demand ratio is improved from 61% to 74% at Station-A, from 64% to 80% at Station-B, and from 51% to 63% at Station-C, highlighting enhanced load balancing. The framework also ensures that all EVs receive energy above their minimum needs, achieving user satisfaction scores of 88.0% at Stations A and B and 81.6% at Station C. This study underscores the potential of hybrid charging schemes in optimizing energy utilization while maintaining system reliability and user convenience. Full article

Quadruped robots have shown significant potential in disaster relief applications, where they have to navigate complex terrains for search and rescue or reconnaissance operations. However, their deployment is hindered by limited adaptability in highly uncertain environments, especially when relying solely on vision-based sensors like cameras or LiDAR, which are susceptible to occlusions, poor lighting, and environmental interference. To address these limitations, this paper proposes a novel sensor-enhanced hierarchical switching model predictive control (MPC) framework that integrates proprioceptive sensing with a bi-level hybrid dynamic model. Unlike existing methods that either rely on handcrafted controllers or deep learning-based control pipelines, our approach introduces three core innovations: (1) a situation-aware, bi-level hybrid dynamic modeling strategy that hierarchically combines single-body rigid dynamics with distributed multi-body dynamics for modeling agility and scalability; (2) a three-layer hybrid control framework, including a terrain-aware switching MPC layer, a distributed torque controller, and a fast PD control loop for enhanced robustness during contact transitions; and (3) a multi-IMU-based proprioceptive feedback mechanism for terrain classification and adaptive gait control under sensor-occluded or GPS-denied environments. Together, these components form a unified and computationally efficient control scheme that addresses practical challenges such as limited onboard processing, unstructured terrain, and environmental uncertainty. A series of experimental results demonstrate that the proposed method outperforms existing vision- and learning-based controllers in terms of stability, adaptability, and control efficiency during high-speed locomotion over irregular terrain. Full article

The growing need for adaptable and efficient hardware-based encryption methods has led to increased interest in unconventional models such as cellular automata (CA). This study presents the hardware design and the field programmable gate array (FPGA)-based implementation of a multi-layer symmetric block encryption algorithm built on the principles of reversible cellular automata (RCA). The algorithm operates on 128-bit plaintext blocks processed over iterative rounds and integrates five RCA components, each assigned with specific transformation roles to ensure high data diffusion. A 256-bit secret key that governs the rule configuration yields a vast keyspace, significantly enhancing resistance to brute-force attacks. Key elements such as rule-based evolution, neighborhood radius, and hybrid cellular automata for random state generation are also integrated into the hardware logic. All cryptographic components, including initialization, encryption logic, and control, are built exclusively using CA, ensuring design consistency and low complexity. The cryptosystem takes advantage of the localized interactions and naturally parallel CA structure, which align with the architecture of FPGA devices, making them a suitable platform for implementing such encryption schemes. The results demonstrate the feasibility of deploying multi-layer RCA encryption schemes on reconfigurable devices and provide a viable path toward efficient and secure hardware-level encryption systems. Full article

In this paper, a modification of the classical PID controller scheme for position control is presented. The resulting controller incorporates an exponential trajectory that smoothly guides the system towards the setpoint and a hybrid mechanism to dynamically reset the exponential signal, allowing an adaptive response to discontinuous reference signals. This combination leverages the benefits of exponential trajectories to reduce overshoot and transient oscillations, while the hybrid system ensures robust performance over a wide range of operating scenarios. Among the advantages of the proposed approach, two stand out: (1) significant improvements in energy savings can be achieved in some cases, and (2) closed-loop system performance can be improved even considering poorly tuned PIDs. Full article

Due to the dynamic and harsh underwater environment, which involves a long propagation delay, high bit error rate, and limited bandwidth, it is challenging to achieve reliable communication in underwater wireless sensor networks (UWSNs) and network support applications, like environmental monitoring and natural disaster prediction, which require energy efficiency and low latency. To tackle these challenges, we introduce AC-RL-based power control (ACRLPC), a novel hybrid MAC protocol that can efficiently integrate Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA)-based MAC and Time Division Multiple Access (TDMA) with Actor–Critic Reinforcement Learning (AC-RL). The proposed framework employs adaptive strategies, utilizing adaptive power control and intelligent access methods, which adjust to fluctuating conditions on the network. Harsh and dynamic underwater environment performance evaluations of the proposed scheme confirm a significant outperformance of ACRLPC compared to the current protocols of FDU-MAC, TCH-MAC, and UW-ALOHA-QM in all major performance measures, like energy consumption, throughput, accuracy, latency, and computational complexity. The ACRLPC is an ultra-energy-efficient protocol since it provides higher-grade power efficiency by maximizing the throughput and limiting the latency. Its overcoming of computational complexity makes it an approach that greatly relaxes the processing requirement, especially in the case of large, scalable underwater deployments. The unique hybrid architecture that is proposed effectively combines the best of both worlds, leveraging TDMA for reliable access, and the flexibility of CSMA/CA serves as a robust and holistic mechanism that meets the desired enablers of the system. Full article

Hybrid beamforming plays a critical role in evaluating wireless communication technology, particularly for millimeter-wave (mmWave) multiple-input multiple-out (MIMO) communication. Several hybrid beamforming systems are investigated for millimeter-wave multiple-input multiple-output (MIMO) communication. The deployment of huge grant-free transmission in the millimeter-wave (mmWave) band is required due to the growing demands for spectrum resources in upcoming enormous machine-type communication applications. Ultra-high data speed, reduced latency, and improved connection are all promised by the development of 5G mmWave networks. Yet, due to severe route loss and directional communication requirements, there are substantial obstacles to transmission reliability and energy efficiency. To address this limitation in this research we present an intelligent transmission control scheme tailored to 5G mmWave networks. Transport control protocol (TCP) performance over mmWave links can be enhanced for network protocols by utilizing the mmWave scalable (mmS)-TCP. To ensure that users have the stronger average power, we suggest a novel method called row compression two-stage learning-based accurate multi-path processing network with received signal strength indicator-based association strategy (RCTS-AMP-RSSI-AS) for an estimate of both the direct and indirect channels. To change user scenarios and maintain effective communication constantly, we utilize the innovative method known as multi-user scenario-based MATD3 (Mu-MATD3). To improve performance, we introduce the novel method of “digital and analog beam training with long-short term memory (DAH-BT-LSTM)”. Finally, as optimizing network performance requires bottleneck-aware congestion reduction, the low-latency congestion control schemes (LLCCS) are proposed. The overall proposed method improves the performance of 5G mmWave networks. Full article

This paper reviews the research advancements and development in magnetic bearings. Firstly, from the technical principle, the design differences and application areas of active magnetic bearings, permanent magnetic bearings and hybrid structures are clarified. At the key technology level, focusing on electromagnetic design optimization, control strategy innovation and power-driven energy management, the breakthrough points of multi-physics coupling modeling, vibration suppression and energy efficiency improvement are revealed. Through the analysis of its engineering cases in the fields of high-speed motors, flywheel energy storage, aerospace and so on, the feasibility and economy of the technical scheme are verified. Further, the technical bottlenecks that need to be broken through are pointed out. For the future trend, this paper suggests that integration of interdisciplinary high-precision modeling, intelligent control algorithm and miniaturized integrated design should be deeply integrated to promote the large-scale application of magnetic bearing in frontier fields. This paper provides theoretical reference and engineering practice guidance for the technology iteration and cross-field integration of magnetic bearings. Full article

Gesture recognition based on conventional machine learning is the main control approach for advanced prosthetic hand systems. Its primary limitation is the need for feature extraction, which must meet real-time control requirements. On the other hand, deep learning models could potentially overfit when trained on small datasets. For these reasons, we propose a hybrid Linear Discriminant Analysis–convolutional neural network (LDA-CNN) framework to improve the gesture recognition performance of sEMG-based prosthetic hand control systems. Within this framework, 1D-CNN filters are trained to generate latent representation that closely approximates Fisher’s (LDA’s) discriminant subspace, constructed from handcrafted features. Under the train-one-test-all evaluation scheme, our proposed hybrid framework consistently outperformed the 1D-CNN trained with cross-entropy loss only, showing improvements from 4% to 11% across two public datasets featuring hand gestures recorded under various limb positions and arm muscle contraction levels. Furthermore, our framework exhibited advantages in terms of induced spectral regularization, which led to a state-of-the-art recognition error of 22.79% with the extended 23 feature set when tested on the multi-limb position dataset. The main novelty of our hybrid framework is that it decouples feature extraction in regard to the inference time, enabling the future incorporation of a more extensive set of features, while keeping the inference computation time minimal. Full article

Addressing the issue of efficient, economical, and reliable operation of a single lead-acid battery (LAB) black start system in complex scenarios, a hybrid energy storage system (HESS) black start scheme based on super capacitors (SCs) is proposed. The proposed solution mainly includes two aspects: an integrated structure and a control strategy. A topology structure with a direct parallel output on the AC side is adopted, and the SC is directly connected to the AC side of the LAB in the current source mode. Compared with traditional DC side access schemes, it can cope with large surge currents by a small capacity, and the economy of the HESS black start system has been effectively improved. In order to improve the dynamic characteristics of the black start control system, a self-adaptive control strategy based on the virtual synchronous generator (VSG) and model predictive control (MPC) is proposed. Based on the small signal disturbance model, the influence of the system parameters on stability was analyzed, and the control parameters are adjusted according to the angular velocity and frequency deviation. A generator recognition model at the ms level was constructed, and the set reference current according to the power level is brought into the MPC to track the reference current. Compared with existing methods, it can effectively suppress the disturbance of the black start system, and the fast responsiveness and stability of the control system is improved. Finally, real operational data is compared and analyzed. The results indicate that the proposed control strategy can accurately identify different black start scenarios, with lower configuration costs and good dynamic performance. Full article

This study examines the impact of midpoint voltage fluctuations on the performance of multilevel converters and proposes an advanced control strategy to reduce the required DC bus capacitance while maintaining system stability. The research demonstrates that active voltage imbalance control in active neutral-point-clamped (ANPC) topologies allows for stable operation with significantly reduced capacitor values. A hybrid control approach, combining fuzzy logic control and third-harmonic injection PWM (THIPWM), is developed to enhance voltage balancing, and modulation techniques are systematically optimized. Both simulation and experimental analyses confirm the efficacy of the proposed method, which achieves superior voltage regulation compared to conventional PI-based control schemes. Specifically, experimental results show a reduction in peak-to-peak DC-link voltage fluctuation from 116 V to just 4 V, and the phase current THD is reduced from 3.6% to 0.8%. The results indicate a substantial reduction in voltage fluctuations, contributing to a total harmonic distortion (THD) as low as 0.8%. Furthermore, the proposed strategy facilitates an approximate 26-fold decrease in DC bus capacitor size without compromising system stability. The reduction in capacitance not only lowers the overall system costs and hardware complexity but also improves reliability. The inverter was tested at a rated power of 62.5 kW using 0.3 mF capacitors instead of the theoretically required 7.8 mF. This work advances power electronics by presenting an efficient voltage balancing methodology, offering a cost-effective and robust solution for multilevel converter applications. The findings are validated through comprehensive simulations and experimental tests, ensuring practical applicability. Full article