Search Results (409)

With the advancement of energy market reform, integrated energy systems (IESs) have achieved rapid development. Considering insufficient research on an electricity–heat coupled master–slave game and the local optimum defect of traditional algorithms, this paper proposes a Stackelberg game optimization strategy for IES considering integrated demand response (IDR), with microgrid operator (MGO) as the leader and load aggregator (LA) as the follower. Firstly, an IDR model containing rigid, shiftable electric loads and reducible thermal loads is established, and a bi-level game model is built: the upper MGO optimizes electricity and heat pricing to maximize profit, while the lower LA adjusts flexible loads for maximum consumer surplus. Secondly, an improved snake optimizer (ISO) is constructed via Hammersley sequence initialization, Lévy flight and random perturbation and combined with quadratic programming (QP) to form the ISO-QP hybrid solving method. Benchmark function and CEC2017 tests verify the superior convergence and stability of ISO against multiple classical intelligent algorithms. Case simulation obtains the Stackelberg equilibrium result, and repeated experiments and parameter sensitivity analysis verify model robustness. Results show that the proposed method smooths load fluctuations via price guidance and synchronously improves MGO revenue and LA consumer surplus on the premise of guaranteed user satisfaction. Full article

With the increasing penetration of renewable energy generation (REG) in novel distribution systems, active distribution networks (ADNs) integrated with microgrids (MGs) play a crucial role in enhancing the flexibility of regulation resources and promoting the accommodation of REG. To meet the operational requirements for efficient collaboration between ADNs and MGs under different dispatch time scales, this paper proposes a collaborative optimal dispatch strategy for multi-microgrid active distribution systems based on a hybrid game and variable time scales. Firstly, a transaction operation framework is constructed for the distribution network operator (DNO) and a multi-microgrid alliance (MMA), considering the peer-to-peer (P2P) transaction mode. On this basis, a day-ahead hybrid game model with a two-layer structure is constructed, the upper layer is a master–slave game with the DNO as the leader and the MMA as the follower, while the lower layer is a cooperative game for MGs within the MMA. An asymmetric Nash bargaining strategy based on contribution degree in P2P transactions is introduced to ensure equitable benefit allocation among cooperative MGs. Secondly, an intra-day rolling optimization model for reactive power and voltage based on variable time scales is proposed, which enhances the system’s responsiveness to real-time source–load power fluctuations by dynamically adjusting the dispatch time scale. Finally, the alternating direction method of multipliers (ADMM), integrated with a strategy separation mechanism, is adopted to efficiently solve the hybrid game model involving numerous 0–1 variables. The case study results indicate that, under the proposed strategy, the MMA’s power purchase cost from the DNO and ESS operational cost are decreased by 9.7% and 11.6%, respectively, while the system’s average deviation rate of node voltage decreases by 0.82%. Therefore, the proposed collaborative dispatch strategy can not only effectively reduce the system’s operational cost and ensure voltage stability but also significantly promote the accommodation of REG. Full article

This paper introduces the research progress of path planning, trajectory tracking control, and multi-machine collaborative operation systems for agricultural robots. It summarizes the development laws of 3D terrain modeling and adaptive path planning algorithms for complex agricultural environments such as hills and mountains, and analyzes the dynamic disturbance characteristics of agricultural machinery under slip, sideslip, and dynamic load changes. Through comprehensive analysis, it is found that traditional kinematic control models have limitations in complex and unstructured environments. Combining soil mechanics mechanisms, variable load identification, and robust control strategies is key to improving trajectory tracking stability and operational quality. In terms of multi-machine collaboration, this paper discusses master–slave collaboration, distributed control, and task allocation modes. It further identifies that the stability of collaboration and interoperability standards between devices in weak network environments are currently the main bottlenecks limiting the large-scale application of this technology. Finally, this paper provides prospects for future research directions and suggests strengthening the closed-loop integration of perception, decision-making, and dynamic models, establishing industry unified standards, and enhancing the safety of the entire lifecycle of operations, providing suggestions for the unmanned application of agricultural robots. Full article

This paper investigates the synchronization problem for a class of proportional Caputo fractional-order neural networks with respect to another function. A master–slave framework is formulated, and a linear state-feedback controller is proposed for the response system. Under a standard Lipschitz condition on the activation functions, sufficient conditions ensuring the convergence of the synchronization error to zero are established. The analysis is based on an explicit integral representation of the error system, a generalized Gronwall-type inequality, and asymptotic properties of the Mittag–Leffler function. The obtained criterion explicitly reveals the roles of the fractional order, the proportional parameter, the control gain, and the network interconnection matrix. Numerical experiments based on a benchmark fractional Hopfield neural network illustrate the effectiveness of the proposed approach. In particular, a scaled benchmark satisfying all theoretical assumptions provides a strict validation of the main theorem, while the original benchmark highlights the conservative nature of the derived sufficient conditions. Full article

The optimal reconfiguration of electrical distribution feeders is a fundamental strategy for reducing active power losses and improving voltage profiles, yet it remains a challenging mixed-integer nonlinear programming (MINLP) problem due to the combinatorial explosion of radial topologies and the nonlinearities introduced by power flow equations. This paper proposes a novel master–slave methodology that integrates a Chu and Beasley genetic algorithm (CBGA) with a minimum spanning tree (MST)-based repair mechanism to address these challenges. In the master stage, the CBGA explores the binary space of switching decisions via steady-state population management, duplicate elimination, and stagnation restart policies. A key contribution lies in the MST-based repair procedure, which ensures that every individual generated by crossover and mutation is projected onto a feasible radial and connected configuration, effectively confining the search to the constrained solution space without recourse to penalty functions. A systematic weight-design rule preserves the Hamming distance between infeasible offspring and repaired solutions, minimizing the distortion of genetic information. The slave stage evaluates each candidate topology using a successive approximations power flow solver, assessing electrical feasibility and computing active power losses. The proposed methodology is validated on multiple test feeders, ranging from small 9- and 24-bus networks to large-scale benchmarks including 33-, 69-, 84-, 136-, and 415-bus systems. A comparison against the deterministic sequential switch opening method (SSOM) and a specialized tabu search demonstrates that the CBGA-MST consistently matches the best-known optima in the literature, achieving loss reductions of up to 9.63% compared to SSOM on the 415-bus system. A statistical analysis over 100 independent runs confirms the algorithm’s robustness, with zero standard deviation for networks of up to 69 buses and a standard deviation of only 2.99 kW (0.51%) for the 415-bus system. The findings confirm that the proposed approach offers superior scalability, robustness, and solution quality, positioning it as a practical and effective tool for distribution system operators seeking to enhance network efficiency under peak load conditions. Full article

Currently, mining material transportation in warehouses relies heavily on manual operations, which pose safety hazards and suffer from low standardization and automation. Existing automated attempts using single-sensor perception or single-arm manipulators lack robustness and adaptability in harsh mine environments. To address these gaps, this paper proposes an intelligent loading system for standardized mining material transportation based on multimodal perception and multi-arm collaboration. First, the overall architecture of the transportation and loading system is introduced, comprising five modules: a standardized carrier platform and modular transport boxes, a box locking and spreader module, a multi-sensor recognition and positioning module, a multi-manipulator collaborative loading/unloading module, and a perception feedback and (human-controlled) overhead crane module. Next, a standardized hardware system is designed, focusing on the standardization of the separable and easily detachable carrier platform and the modularization of transport boxes, along with the locking mechanism between them, establishing the hardware foundation for the system. Subsequently, a multimodal perception data fusion and recognition positioning technology based on multiple depth cameras, UWB, and IMU is investigated to provide perceptual feedback for automated loading/unloading. Following this, a multi-manipulator collaborative control technology based on multi-agent error consensus is developed, designing a “two-master, two-slave” structure and a collaborative control algorithm to achieve automated loading/unloading of transport boxes. An information-based interactive monitoring software is then designed to monitor system perception data in real time and control the system’s operational status, ensuring safety and controllability. Finally, the feasibility and effectiveness of the system are validated through simulations and prototype experiments. This work provides a foundation for standardized transportation and storage of mining materials and outlines a practical system-level approach. Full article

To address the speed oscillation and stability degradation caused by load imbalance in a single−inverter dual−permanent magnet synchronous motor (PMSM) parallel system, this paper proposes an adaptive control strategy based on a sliding mode observer. The proposed method preserves the hardware simplicity of the single−inverter topology while improving control performance under load disturbances. First, a sliding mode observer is designed to estimate the load torque difference between the two motors in real time, thereby enabling dynamic perception of load variations. Then, an adaptive controller is introduced to switch the control mode according to the estimated load imbalance. When the load difference is small, master−slave vector control without fixed role distinction is adopted. When the load difference exceeds a predefined threshold, an improved finite−set model predictive torque control (FCS−MPTC) is activated. In the predictive control mode, unnecessary full−time predictive optimization is avoided and a d−axis current suppression term is incorporated into the cost function to improve current waveform quality. Simulation results show that the proposed strategy reduces speed overshoot during load transients and improves the three−phase current waveform compared with conventional predictive torque control. Therefore, the proposed method provides an effective control solution for single−inverter dual−motor drive systems under load disturbance. Full article

Structural health monitoring (SHM) is a critical research topic in civil engineering for assessing the integrity of constructed facilities, yet its widespread deployment is often hindered by the high cost of commercial equipment. This study introduces an accessible, vibration-based SHM system consisting of a slave unit for data acquisition via an MPU6050 sensor and a master unit for long-range wireless transmission using the LoRa protocol. To overcome the inherent noise levels of inexpensive MEMS sensors, we propose a robust modal identification framework that utilizes the Levenberg–Marquardt optimization method combined with a sliding window strategy to accurately estimate damped natural frequencies. Experimental validation conducted on a steel beam demonstrates the technical viability of this event-triggered IoT architecture. The designed system achieved a relative error of only 6.38% in natural frequency identification compared to a high-precision commercial reference system. Ultimately, this framework provides a technically sound, resource-efficient solution for structural assessment. Full article

Frequency synthesis is an important aspect of an atomic clock. It is also imperative that the synthesized frequency exhibits good short term stability or, in other words, exhibits good phase noise. Conventionally single-PLL-system-based approaches have been made for realizing the frequency synthesizers required for hydrogen maser atomic clocks. In this article, a novel approach involving a master–slave-based phase-locked loop (MSPLL) method is presented for frequency synthesis in a hydrogen maser atomic clock. The novelty of this paper lies in the fact that the way two phase-locked loops are coupled to obtain advantage in improving the master oscillator’s stability to match maser physics subsystem stability and at the same time achieving lower jitter by the design. The design involves the usage of a master and a slave phase-locked loop with coupled custom designed direct digital synthesizers for ensuring that the hydrogen maser’s frequency stability is transferred to the master oscillator. The slave PLL (SPLL) generates a low jitter clock for the master PLL (MPLL), thereby guaranteeing reliable tracking of the input reference of 10 MHz, obtained by down-converting the maser physics subsystem frequency of ∼1.4 GHz. A novel mathematical model was derived for the proposed MSPLL design which aids in determination of the settling time of phase, which in turn, leads to the investigation of jitter variance in time domain. A detailed study and analysis of the settling time, phase noise in frequency domain, phase jitter in time domain. and stability performance is presented. The results were validated by the experimental data. The realized frequency synthesizer deduced a settling time of phase that can be adjusted between 689 $\mathsf{\mu }$s to 811 $\mathsf{\mu }$s. The synthesized frequency’s phase noise is ≤−114 dBc/Hz at 1 Hz offset, and it was observed that this design induces a very low phase noise to the output signal with respect to the physics subsystem. The achieved short-term stability of the output signal at 1 s is approximately (7.66 × ${10}^{-12}$) ${\tau }^{-1/2}$, which is very close to the physics subsystem stability. In terms of stability degradation factor, the proposed MSPLL design exhibits an excellent short-term stability that is one order better than that of the existing methods. Full article

This paper investigates the synchronization problem for a class of master–slave neural networks with state-dependent impulses. Different from fixed-time impulsive systems, the impulsive instants considered here depend on the current states of the neural networks, which makes the synchronization analysis more complicated. In particular, when both the master and slave systems possess their own state-dependent impulses, the corresponding impulsive instants are generally asynchronous, so the synchronization error evolves over an impulsive interval rather than undergoing only a single instantaneous jump. To address this difficulty, two easily verifiable conditions are first proposed to guarantee that each trajectory intersects every impulsive surface exactly once, thereby excluding the beating phenomenon. Then, an interval–impulse differential inequality is established to characterize the error evolution on non-impulsive subintervals and to handle the mismatch between the impulsive times of the master and slave systems. Based on this inequality, an intermittent controller activated only outside the impulsive interval is designed so that the controller does not destroy the intrinsic state-dependent impulsive rhythm of the master system. By combining Lyapunov analysis with matrix inequality techniques, verifiable criteria are derived for local exponential synchronization-like behavior of the considered neural networks. Here, synchronization-like refers to exponential decay of the synchronization error on the non-mismatched time intervals since the master and slave systems generally possess asynchronous state-dependent impulsive instants. Finally, numerical examples are presented to illustrate the effectiveness of the proposed conditions and control strategy. The simulation results show that the designed controller can effectively suppress synchronization error and that increasing the control gain can significantly accelerate the convergence process. Full article

This paper presents a Digital Twin (DT)-based framework for the control, monitoring, and intelligent optimization of an Assembly/Disassembly/Repair Mechatronic Production Line (A/D/R MPL), developed as a laboratory platform aligned with Industry/Education 4.0/5.0 paradigms. The A/D/R MPL is assisted by two complementary cyber–physical robotic systems: an Assembly/Disassembly/Replacement Cyber–Physical Robotic System (A/D/R CPRS), and a Mobile Cyber–Physical Robotic System (MCPRS), enabling both fixed and mobile intelligent operations. The CPRS is equipped with an industrial robotic manipulator (IRM) responsible for A/D/R tasks, while the A/D Mechatronic Line (A/D ML) consists of seven interconnected workstations (WS1–WS7) dedicated to storage, transport, quality control, and final product handling. MCPRS includes a wheeled mobile robot (WMR), carrying a robotic manipulator (RM) and Mobile Visual Servoing System (MVSS). Each workstation is connected to a local slave programmable logic controller (PLC), which communicates via PROFIBUS with a master PLC located at the CPRS level. Additional communication infrastructures include LAN PROFINET and LAN Ethernet for local integration, and WAN Ethernet connectivity enabled through open platform Communication-Unified Architecture (OPC-UA), ensuring interoperability, scalability, and remote accessibility. Also, MODBUS TCP as serial industrial communication is used between the master PLC and the MCPRS. Virtual environment supports task planning through Augmented Reality (AR) and real-time monitoring through Virtual Reality (VR). The system behaviour is modelled with synchronized hybrid Petri Nets (SHPNs) which describe the discrete and hybrid dynamics of A/D/R processes. Artificial intelligence (AI) techniques are integrated into the DT framework for optimal task scheduling and adaptive decision-making. As a laboratory-scale implementation, the proposed system provides a comprehensive platform for experimentation, validation, and education. It supports Education 4.0/5.0 objectives by facilitating hands-on learning, human–machine interaction, and the integration of emerging technologies such as AI, Digital Twins, AR/VR, and cyber–physical systems. At the same time, it embodies Industry 4.0/5.0 principles, including interoperability, decentralization, sustainability, robustness, and human-centric design. Full article

The integration of grid-forming energy storage systems (GFM-ESSs) provides essential support for the stable operation of grid-connected converters in renewable energy systems. However, GFM-ESSs may exhibit low-frequency oscillations in response to grid state variations, posing a threat to power system stability. To address this challenge, this paper proposes a fast continuous optimization method for the active power–frequency control loop of multi-VSG-based GFM-ESSs. First, a parameter coupling model for multiple VSGs is established, and an internal parameter decoupling control strategy is proposed. Subsequently, an iterative optimization model based on a gradient-based master–slave game is developed, in which the minimization of converter frequency deviation serves as the leader’s objective, while the minimization of system frequency deviation acts as the follower’s objective. Frequency fluctuations are further mitigated through tracking differentiator-based active power compensation. The effectiveness of the proposed method is validated through simulation with six GFM-ESS units integrated into a modified IEEE 33-node system featuring six renewable energy stations. Simulation results demonstrate that the proposed approach significantly suppresses frequency fluctuations while also reducing the response time and the rate of frequency change under grid disturbance conditions. Full article

This paper introduces a novel memristor-based hyperchaotic system in which the integer-order derivatives are replaced by a variable-order fractal-fractional operator. The dynamical properties of the system, including equilibrium points, Lyapunov exponents, bifurcation diagrams with respect to the variable orders, and the Kaplan–Yorke dimension, are analyzed. A synchronization scheme based on active control is designed for the master–slave configuration, and global Mittag–Leffler stability of the error dynamics is established using a suitable variable-order Lyapunov function. The synchronized states are then applied to an image encryption algorithm. Numerical simulations, security analyses, and NIST randomness tests demonstrate the effectiveness and enhanced performance of the proposed framework compared to existing fixed-order and classical fractional-order methods. Full article

Robotic technologies are increasingly investigated for craniofacial and dental surgical procedures where sub-millimeter positional accuracy and stable instrument trajectories are essential. This structured review evaluates the current landscape of robotic systems applied to craniofacial surgical interventions and analyzes their technical architectures, validation approaches, and reported surgical accuracy. A structured literature search of PubMed and IEEE Xplore identified 27 studies published between 2015 and 2025 that met predefined inclusion criteria. The included systems were analyzed with respect to robotic control architecture, surgical application domain, validation model, and quantitative performance metrics. To facilitate cross-study interpretation, the review introduces a unified engineering classification framework linking robotic control paradigms, mechanical configurations, and clinical application domains. Most platforms employed master–slave teleoperation, image-guided hybrid control, task-autonomous execution, or cooperative haptic-guided architectures designed to stabilize surgical trajectories and reduce surgeon-dependent variability. Across representative investigations, robotic systems demonstrated entry-point deviations typically ranging from approximately 0.6–1.5 mm and angular deviations between 1.2° and 3.5°, indicating improved reproducibility compared with conventional freehand techniques. Dental implant robotics currently represents the most clinically mature application, whereas sinus, skull base, and microsurgical systems remain largely in experimental or early translational stages. Overall, craniofacial surgical robotics demonstrates substantial potential to enhance surgical precision and procedural standardization; however, broader clinical validation and improved workflow integration remain necessary for widespread clinical adoption. Full article

Given the energy-intensive operations and considerable carbon emissions of offshore oil and gas platforms (OOGPs) in deep-sea regions, adopting floating offshore wind farms (FOWFs) as power sources offers substantial benefits. However, the expenses associated with dynamic submarine cables constitute a substantial portion of the capital expenditure (CAPEX) for this hybrid system, highlighting the crucial need for optimization in the power collection system design. In this study, we present a mixed-integer quadratic programming (MIQP) model designed to reduce both the costs of investment and power losses associated with dynamic submarine cables, taking into account the influence of the wake effect in local wind conditions. Due to the complexity of this problem, we employ the Benders’ decomposition method to reformulate it into a master problem and a slave problem. Additionally, two valid inequalities are specifically incorporated into the master problem to accelerate the solution process. These constraints are derived from a heuristic combination of various cable connection configurations and a greedy-based spanning tree structure. Through multiple case studies, we first demonstrate the accuracy and rapid convergence of our method. Furthermore, we reveal that as the wind farm grows in size, the influence of the wake effect becomes increasingly pronounced. Full article