Master Slave Research Articles

A Master–Slave Game-Based Strategy for Trading and Allocation of Virtual Power Plants in the Electricity Spot Market

With the transformation of the energy structure, the integration of numerous small-scale, widely distributed renewable energy sources into the power grid has introduced operational safety challenges. To enhance the operational competitiveness, the virtual power plant (VPP) has emerged to aggregate and manage these distributed energy resources (DERs). However, current research on the VPP’s frequency modulation performance and bidding strategy remains insufficient in the joint market of electrical energy and frequency modulation (FM) ancillary services, with inadequate coordination of internally distributed resources. To fully leverage the flexibility of VPPs and incentivize their participation in electricity market operations, this paper investigates game-based bidding strategies and internal distributed resources allocation methods for VPPs in the joint market for electrical energy and frequency ancillary services. Firstly, the regulatory performance indicators of VPPs participating in the joint market and develops the corresponding market-clearing model. Secondly, to address the competition among distributed resources within VPPs, a master-slave game approach is innovatively employed to optimize the VPP’s trading strategies. This method ensures the rational allocation of electricity consumption among distributed energy resources within the VPP and derives the optimized bidding prices and quantities for both the VPP and its internal members. Finally, the case study shows that the proposed trading strategy provides effective bidding strategies for distributed energy resources participating in the joint market for energy and frequency regulation ancillary services. It enhances the regulatory performance of VPPs in the energy-frequency regulation market, ensures the profitability of distributed energy resources, and contributes to the economically stable operation of the market.

Research on linewidth narrowing and frequency control technology of quantum cascade lasers based on optical feedback and optical phase-locked loops

The mid-infrared (MIR) spectral region, which corresponds to molecular vibrational and rotational energy level transitions, contains a wealth of molecular energy level information. By employing techniques such as cavity ring-down spectroscopy (CRDS), the MIR spectra can be precisely measured, thereby validating fundamental physical laws, the inversion of fundamental physical constants, and the detection of trace gases. However, technical noise from temperature fluctuations, mechanical vibrations, and current noise causes free-running quantum cascade laser (QCL) to suffer high-frequency noise, typically broadening the linewidth to the MHz range, thus reducing spectral resolution. Moreover, long-term drift in the laser frequency due to temperature and current fluctuations hinders high-precision spectroscopy, particularly for narrow-linewidth nonlinear spectroscopy, such as saturated absorption and multiphoton absorption spectroscopy. This work presents a method of combining optical feedback with an optical phase-locked loop (OPLL) for offset frequency locking, aiming to generate a mid-infrared (MIR) laser with excellent frequency characteristics. Strong optical feedback is employed to narrow the linewidth of the quantum cascade laser (QCL) acting as a slave laser, thereby alleviating the challenges associated with phase locking. The OPLL uses frequency-offset to lock the slave laser to the ultra-narrow laser. By adjusting the offset frequency, fine control of the slave laser is achieved. To ensure tight phase locking, the OPLL is based on the ADF4007, and combines a phase lead circuit to compensate for phase lag, effectively expanding the loop bandwidth of the system. In this work, the fundamental principles of the optical phase-locked loop are theoretically analyzed, and a basic model is established. The influence of loop bandwidth on locking performance is also investigated. Upon achieving phase locking using the combined optical feedback and OPLL system, the magnitude of the beat note of the two lasers is improved by 66 dBm, with phase noise suppressed to -81 dBc/Hz@2 kHz in the low-frequency region and -101 dBc/Hz@2MHz in the high-frequency region.The frequency noise power spectral density of both the master laser and slave laser is obtained via the error signal in the closed-loop system. Significant suppression of frequency noise is observed for the slave laser across both low- and high-frequency region, with suppression ratio reaching 86 dB at 100 Hz and 55 dB at 400 kHz. The frequency noise of the slave laser in the low-frequency domain is found to be comparable to that of the master laser. Based on the white noise response region in the frequency noise spectrum (from 200 Hz to 400 kHz), the locked slave laser linewidth is determined to be approximately 3 Hz, narrowing the initial MHz-level linewidth to match the Hz-level linewidth of the master laser. Finally, the locked laser is used to conduct cavity ring-down spectroscopy, achieving an improvement factor of 5 in the signal-to-noise ratio of the ringdown signal. This frequency-stabilized laser will be applied to high-precision spectroscopy for detecting radiocarbon isotopes in future.

Low‐Carbon Optimal Scheduling of Integrated Energy System Based on the Master–Slave Game

ABSTRACTWith the integration of distributed power generation into the grid, the economic incentive trading market mechanism becomes an effective method to promote carbon emission reduction in microgrids. In this paper, the carbon flow of the integrated energy system is calculated, the carbon emission model and the carbon flow tracing model of the integrated energy system are established, and the optimization model aiming at low‐carbon operation of the integrated energy system is constructed based on the master–slave game model. For the user side of the energy system, the dynamic carbon price and electricity price established by the model play a good role in peaking and valley filling for the load part, improving the operation stability of the power system, realizing the optimal scheduling of the power grid system under the background of electric‐carbon coupling trading, and encouraging each microgrid entity to participate in the electric‐carbon coupling trading actively. The calculation results show that electric‐carbon coupling trading facilitates the flexible operation of power grid systems and improves economic benefits. When combined with carbon emission flow in operation, it can promote the low‐carbon and clean power system, encourage distributed renewable resources to connect to the power grid, reduce the carbon content of the power system, enable users to actively participate in low‐carbon demand response, and promote the effective carbon emission reduction of a multi‐microgrid system.

Design and Realization of an Isomorphic Master-Slave Robotic System for Precise Sub-Micrometer Manipulations in Ophthalmic Surgery

Abstract Intra-ocular surgery requires precise submicrometer manipulations within the confined ocular space. Implementing a master-slave robotic system is a potential solution. The development of master manipulators impacts the overall performance of the robotic system. A master–slave isomorphic mapping method is used to design a master manipulator prototype. Kinematic and dynamic models of the master manipulator are established, and dynamic parameters and friction forces at each joint identified. Gravity compensation is applied to the master manipulator based on motor torque, and its efficacy is validated through experiments. The isomorphic master manipulator adapts to the required degrees-of-freedom (DOF) for intra-ocular surgery. The gravity compensates algorithm, based on torque, enables stable hovering of the master manipulator within the workspace and reduces the operating force by 71.4%. The proposed master manipulator can feasibly be applied in master–slave surgical robot systems.

A note on the topological synchronization of unimodal maps

Abstract In this note we complete the analysis carried on in Caby et al (2023 Nonlinearity 36 3603–21) about the topological synchronisation of unimodal maps of the interval coupled in a master–slave configuration, by answering to the questions raised in that Paper. Namely, we compute the weak limits of the invariant measure of the coupled system as the coupling strength k ∈ ( 0 , 1 ) tends to 0 and to 1 and discuss the uniqueness of the invariant measure of its random dynamical system counterpart, proving that the convergence of the associated Markov chain to its unique stationary measure is geometric.

Master-Slave Deep Architecture for Top- K Multiarmed Bandits With Nonlinear Bandit Feedback and Diversity Constraints.

We propose a novel master-slave architecture to solve the top- K combinatorial multiarmed bandits (CMABs) problem with nonlinear bandit feedback and diversity constraints, which, to the best of our knowledge, is the first combinatorial bandits setting considering diversity constraints under bandit feedback. Specifically, to efficiently explore the combinatorial and constrained action space, we introduce six slave models with distinguished merits to generate diversified samples well balancing rewards and constraints as well as efficiency. Moreover, we propose teacher learning-based optimization and the policy cotraining technique to boost the performance of the multiple slave models. The master model then collects the elite samples provided by the slave models and selects the best sample estimated by a neural contextual UCB-based network (NeuralUCB) to decide on a tradeoff between exploration and exploitation. Thanks to the elaborate design of slave models, the cotraining mechanism among slave models, and the novel interactions between the master and slave models, our approach significantly surpasses existing state-of-the-art algorithms in both synthetic and real datasets for recommendation tasks. The code is available at https://github.com/huanghanchi/Master-slave-Algorithm-for-Top-K-Bandits.

Master–slave strategy based on fuzzy control for SOC recovery in the BESS FM process

Master–slave strategy based on fuzzy control for SOC recovery in the BESS FM process

Two-Stage, Three-Layer Stochastic Robust Model and Solution for Multi-Energy Access System Based on Hybrid Game Theory

This paper proposes a two-stage, three-layer stochastic robust model and its solution method for a multi-energy access system (MEAS) considering different weather scenarios which are described through scenario probabilities and output uncertainties. In the first stage, based on the principle of the master–slave game, the master–slave relationship between the grid dispatch department (GDD) and the MEAS is constructed and the master–slave game transaction mechanism is analyzed. The GDD establishes a stochastic pricing model that takes into account the uncertainty of wind power scenario probabilities. In the second stage, considering the impacts of wind power and photovoltaic scenario probability uncertainties and output uncertainties, a max–max–min three-layer structured stochastic robust model for the MEAS is established and its cooperation model is constructed based on the Nash bargaining principle. A variable alternating iteration algorithm combining Karush–Kuhn–Tucker conditions (KKT) is proposed to solve the stochastic robust model of the MEAS. The alternating direction method of multipliers (ADMM) is used to solve the cooperation model of the MEAS and a particle swarm algorithm (PSO) is employed to solve the non-convex two-stage model. Finally, the effectiveness of the proposed model and method is verified through case studies.

A Five-Axis Toolpath Corner-Smoothing Method Based on the Space of Master–Slave Movement

The smoothing of linear toolpaths plays is critical in improving machining quality and efficiency in five-axis CNC machining. Existing corner-smoothing methods often overlook the impact of spline curvature fluctuations, which may lead to acceleration variations, hindering surface quality improvements. The paper presents a five-axis toolpath corner-smoothing method based on the space of master–slave movement (SMM), aiming to minimize curvature fluctuations in five-axis machining and improve surface quality. The concept of movement space in master–slave cooperative motion is introduced, where the tool tip position and tool orientation are decoupled into a main motion trajectory and two master–slave movement space trajectories. By deriving the curvature monotony conditions of a dual Bézier spline, a G2-continuous tool tip corner-smoothing curve with minimal curvature fluctuations is constructed in real-time. Subsequently, using the SMM and the asymmetric dual Bézier spline, a high-order continuous synchronization relationship between the tool tip position and tool orientation is established. Simulation tests and machining experiments show that with our smoothing algorithm, maximum acceleration values for each axis were reduced by 21.05%, while jerk was lowered by 22.31%. These results indicate that trajectory smoothing significantly reduces mechanical vibrations and improves surface quality.

Master–Slave Game Optimization Scheduling of Multi-Microgrid Integrated Energy System Considering Comprehensive Demand Response and Wind and Storage Combination

This paper addresses the critical challenge of scheduling optimization in regional integrated energy systems, characterized by the coupling of multiple physical energy streams (electricity, heat, and cooling) and the participation of various stakeholders. To tackle this, a novel multi-load and multi-type integrated demand response model is proposed, which fully accounts for the heterogeneous characteristics of energy demands in different campus environments. A leader–follower two-layer game equilibrium model is introduced, where the system operator acts as the leader, and campus load aggregators, energy storage plants, and wind farm operators serve as followers. The layer employs an enhanced particle swarm optimization (PSO) algorithm to iteratively adjust energy sales prices and response compensation unit prices, influencing the user response plan through the demand response model. In the lower layer, the charging and discharging schedules of energy storage plants, wind farm energy supply, and outputs of energy conversion devices are optimized to guide system operation. The novelty of this approach lies in the integration of a game-theoretic framework with advanced optimization techniques to balance the interests of all participants and enhance system coordination. A case study is conducted to evaluate the effectiveness of the proposed strategy, demonstrating significant economic benefits. The results show that the model encourages stakeholders to invest in energy infrastructure and actively participate in coordinated dispatch, leading to improved overall system efficiency and comprehensive revenue enhancement for the multi-agent energy system.

The slow clock is the master

AbstractWe investigate the synchronization and stability of mutually coupled oscillators through small impacts and explore both master–master and master–slave synchrony. The synchronization behaviour between two oscillators, one operating at a frequency $$ \omega $$ ω and the other at a frequency near a multiple of the first one, $$ n \omega $$ n ω where $$ n $$ n is an integer greater than 1, is investigated. It is observed that such systems tend to exhibit a consistent pattern of master–slave relationship. This phenomenon is geometrically elucidated by considering the interaction dynamics: while the multiple bursts of the faster oscillator tend to cancel each other out upon impacting the slower oscillator, the single burst of the slower oscillator on the faster has a secular effect. We propose that this synchronization pattern, arising from perturbative impacts, is prevalent across various physical systems. Further experimental validation of these findings could contribute to a deeper understanding of synchronization phenomena and their implications in natural and technological domains.

Security-based control design for synchronization of switched reaction diffusion neural networks with hybrid attacks

This study delves into exploring dissipative synchronization for a class of switched neural networks with external disturbances featuring reaction–diffusion terms under the master–slave scheme. Precisely, the addressed network model comprises a hybrid attack model which entails both deception and denial-of-service attacks. Moreover, security-based control is designed to achieve the intended results, wherein in the realm of control design, the likelihood of cyber attacks is dictated by two separate and independent stochastic Bernoulli distributed factors. Meanwhile, the dissipative theory is employed to effectively curb the external disturbances within the network model. Subsequently, by leveraging the Lyapunov stability theory and linear matrix inequality approach, adequate conditions are acquired for ensuring the mean square exponential synchronization and strict (Γ1,Γ2,Γ3)-θ dissipativity of the examined system. Furthermore, the relation for deriving the control gain matrices is set forth in accordance with the acquired criteria. At the end, a numerical example accompanied by simulation results is supplied to vividly demonstrate the efficacy and significance of the acquired theoretical insights.

FPGA Realization of an Image Encryption System Using a 16-CPSK Modulation Technique

Nowadays, M-Quadrature Amplitude Modulation (M-QAM) techniques are widely used to modulate information by bit packets due to their ability to increase transfer rates. These techniques require more power when increasing the modulation index M to avoid interference between symbols. This article proposes a technique that does not suffer from interference between symbols, but instead uses memory elements to store the modulation symbols. In addition, the aim of this paper is to implement a four-dimensional reconfigurable chaotic oscillator that generates 16-Chaotic Phase Shift Keying (16-CPSK) modulation–demodulation carriers. An encryption and modulation transmitter module, a reception module, and a master–slave Hamiltonian synchronization module make up the system. A 16-CPSK modulation scheme implemented in Field Programmable Gate Array (FPGA) and applied to a red-green-blue (RGB) and grayscale image encryption system are the main contributions of this work. Matlab and Vivado were used to verify the modulation–demodulation scheme and synchronization. This proposal achieved excellent correlation coefficients according to various investigations, the lowest being −15.9×10−6 and 0.13×10−3 for RGB and grayscale format images, respectively. The FPGA implementation of the 16-CPSK modulation–demodulation system was carried out using a manufacturer’s card, Xilinx’s Artix-7 AC701 (XC7A200TFBG676-2).

A Tri-Level Transaction Method for Microgrid Clusters Considering Uncertainties and Dynamic Hydrogen Prices

The advancement of hydrogen technology and rising environmental concerns have shifted research toward renewable energy for green hydrogen production. This study introduces a novel tri-level transaction methodology for microgrid clusters, addressing uncertainties and price fluctuations in hydrogen. We establish a comprehensive microgrid topology with distributed power generation and hydrogen production facilities. A polygonal uncertainty set method quantifies wind and solar energy uncertainties, while an enhanced interval optimization technique refines the model. We integrate a sophisticated demand response model for hydrogen loading, capturing users’ behavior in response to price changes, thereby improving renewable energy utilization and supporting economically viable management practices. Additionally, we propose a tri-level game-theoretic framework for analyzing stakeholder interactions in microgrid clusters, incorporating supply–demand dynamics and a master–slave structure for microgrids and users. A distributed algorithm, “KKT & supply-demand ratio”, solves large-scale optimization problems by integrating Karush–Kuhn–Tucker conditions with a heuristic approach. Our simulations validate the methodology, demonstrating that accounting for uncertainties and dynamic hydrogen prices enhances renewable energy use and economic efficiency, optimizing social welfare for operators and economic benefits for microgrids and users.

Supervised Reinforcement Learning-Based Collaborative Master–Slave Harvest Control Study in Wheat

Aiming at the difficulty of controlling the longitudinal relative position of agricultural machines during the agricultural master–slave navigation cooperative operation and the weak adaptability of the unitary traditional control method in the face of the working conditions of complex farmland environments, this paper proposes a supervised reinforcement learning (SRL)-based longitudinal stable and safe control method applicable to master–slave navigation harvesting and unloading operations. Firstly, to improve the algorithm training success rate, a supervisor trained on actual driving data is introduced into the actor–critic reinforcement learning method. Secondly, in order to improve the effect of agricultural machine operation, considering the actual grain unloading operation scene and combining the smoothness of operation and the safety of unloading, a new reward function in the supervised reinforcement learning algorithm is designed. Finally, the performance of the proposed SRL control strategy is verified by simulation and agricultural machines following grain unloading tests. The results of field operation show that, when the harvester speed is 1.2 m/s, the average absolute deviation of the actual distance between the two trucks is 0.048 m, and the maximum deviation of the steady state is 0.26 m. In the variable speed test, when the harvester speed is 0.4 m/s and 1.2 m/s, the average absolute deviation of the actual distance between the two trucks is 0.079 m and 0.091 m, and the maximum deviation of the steady state is 0.20 m and 0.21 m, and the cooperative accuracy can fulfill the operational demands of harvesting cooperative unloading. The study’s results can serve as a technological reference for autonomous harvesting operations in the field.

Attitude synchronization of chaotic satellites with unknown dynamics using a neural network based fixed time sliding mode controller

This study investigates the synchronization and anti-synchronization of both identical and non-identical chaotic satellite systems. A fixed-time sliding mode control framework, based on a radial basis function (RBF) neural network, has been developed to synchronize the chaotic dynamics of master–slave satellite configurations. The proposed control scheme operates under the assumption that the dynamics of the satellites are not entirely known. The proposed control method guarantees that system errors will converge to negligible levels within a fixed time. Furthermore, the controller exhibits robustness in the presence of parametric uncertainties and external disturbances. The stability of the controlled systems is rigorously validated through Lyapunov analysis, and the controller’s effectiveness is confirmed through extensive simulation studies. These simulations are conducted on both identical and non-identical satellite models, with performance comparisons made against recent findings reported in the literature.

Global Exponential Synchronization of Delayed Quaternion-Valued Neural Networks via Decomposition and Non-Decomposition Methods and Its Application to Image Encryption

With the rapid advancement of information technology, digital images such as medical images, grayscale images, and color images are widely used, stored, and transmitted. Therefore, protecting this type of information is a critical challenge. Meanwhile, quaternions enable image encryption algorithm (IEA) to be more secure by providing a higher-dimensional mathematical system. Therefore, considering the importance of IEA and quaternions, this paper explores the global exponential synchronization (GES) problem for a class of quaternion-valued neural networks (QVNNs) with discrete time-varying delays. By using Hamilton’s multiplication rules, we first decompose the original QVNNs into equivalent four real-valued neural networks (RVNNs), which avoids non-commutativity difficulties of quaternions. This decomposition method allows the original QVNNs to be studied using their equivalent RVNNs. Then, by utilizing Lyapunov functions and the matrix measure method (MMM), some new sufficient conditions for GES of QVNNs under designed control are derived. In addition, the original QVNNs are examined using the non-decomposition method, and corresponding GES criteria are derived. Furthermore, this paper presents novel results and new insights into GES of QVNNs. Finally, two numerical verifications with simulation results are given to verify the feasibility of the obtained criteria. Based on the considered master–slave QVNNs, a new IEA for color images Mandrill (256 × 256), Lion (512 × 512), Peppers (1024 × 1024) is proposed. In addition, the effectiveness of the proposed IEA is verified by various experimental analysis. The experiment results show that the algorithm has good correlation coefficients (CCs), information entropy (IE) with an average of 7.9988, number of pixels change rate (NPCR) with average of 99.6080%, and unified averaged changed intensity (UACI) with average of 33.4589%; this indicates the efficacy of the proposed IEAs.

Fractional derivative of Hermite fractal splines on the fractional-order delayed neural networks synchronization

The purpose of this research is twofold. First, the master–slave synchronization of fractional-order neural networks is explored with time delays using aperiodic intermittent control. Then we present a sufficient condition for master–slave synchronization of delayed fractional-order neural networks via average-width intermittent control technique. A numerical simulation is used to demonstrate the efficacy of the derived results. Second, a novel investigation of the Caputo-fractional derivative of Hermite fractal splines is accomplished. Moreover, its box counting dimension is estimated and related with the Caputo-fractional order. Additionally, we propose an image encryption algorithm utilizing the semi-tensor product (STP). The efficiency of the algorithm is evaluated through the application of statistical measures.

Transactive energy management for CIES considering tri-level hybrid game-theoretic approaches with multiple weight allocation factors

In city integrated energy systems, to reduce the costs of energy production and usage, it is extremely important for different entities to design proper energy management approaches since they have diverse behaviours and benefits. The hierarchy of the system is naturally divided into three layers, and hybrid game theories are employed on the basis of the vertical master–slave and horizontal equal cooperation characteristics among energy providers, aggregators, and users. First, to reflect the interactions between different levels' entities, Stackelberg game theory is adopted vertically, and the model complexity is lessened by the KKT conditions. Then, for energy providers, the asymmetric bargaining game is improved by combining multiple weight allocation factors, which can help reduce renewable energy fluctuations and encourage the consumption of clean energy to reduce the carbon emissions of the system. Also, the results show that the proposed method can enhance the utility of renewable energy generation units by approximately 64 % to 69 %. Finally, the optimality and effectiveness of the proposed game theoretic approaches are verified in case studies and in strict mathematical proofs. In addition, the impact mechanisms on the demand response from the perspectives of the user load and price sensitivity are analysed. To explore pricing methods that are more suitable for user aggregators, two pricing strategies are designed for the user aggregator and compared in terms of computation time and revenue. The results show that a unified pricing strategy can increase the aggregator's revenue by 1.24 % and reduce the computation time by 24.47 %.

Stochastic Differential Games of Carbon Emission Reduction in the Four-Tier Supply Chain System Based on Reference Low-Carbon Level

This paper takes corporate social responsibility goodwill and consumers’ reference low-carbon level as endogenous variables of joint carbon emission reduction in the “supplier–manufacturer–retailer–consumer” supply chain system. The joint carbon emission reduction strategies of this four-tier system are analyzed from a dynamic perspective by considering random factors that affect the endogenous variables. Three stochastic differential games are proposed to examine the mechanism between each player, namely the cooperative model, Nash non-cooperative model, and Stackelberg master–slave model. Compared to the Nash non-cooperative game, the manufacturer/supplier-led Stackelberg master–slave game leads to Pareto improvement in the profits of the entire supply chain system and each player. The cooperative game demonstrates the highest expected emission reduction and corporate social responsibility goodwill, but also the highest variance. More importantly, the reference low-carbon level embraces consumers’ subjective initiative in the dynamic of carbon emission reduction. This level is an internal benchmark used to compare against the observed low-carbon level. This paper provides a theoretical foundation for strategic decision-making in emission reduction, contributing to sustainable development. By addressing environmental, economic, and social sustainability, it promotes climate action through carbon reduction strategies and offers policy recommendations aligned with the Sustainable Development Goals.