Communication Load Research Articles

A Method for micro-vibration test analysis of small satellites with laser loading

Based on the parameters of the structure configuration and equipment layout of a small laser satellite in the design stage, the in-orbit micro-vibration simulation analysis of the satellite is carried out by finite element method, which mainly includes [1-2]: 1) modal analysis obtaining the main vibration frequency and modal shape of the satellite in orbit; 2) micro-vibration analysis of the satellite in orbit with the micro-vibration disturbance of the moment gyroscope as the excitation condition. Through the in-orbit micro-vibration simulation analysis of the satellite, we can obtain the vibration transmission relationship from the moment gyroscope to the laser communication load and the micro-vibration state of the laser communication load.

State Estimation for Recurrent Neural Networks With Intermittent Transmission.

This work addresses the state estimation problem for recurrent neural networks over capacity-constrained communication channels. The intermittent transmission protocol is used to reduce the communication load, where a stochastic variable with a given distribution is used to describe the transmission interval. A corresponding transmission interval-dependent estimator is designed, and an estimation error system based on it is also derived, whose mean-square stability is proved by constructing an interval-dependent function. By analyzing the performance in each transmission interval, sufficient conditions of the mean-square stability and the strict (Q,S,R) - γ -dissipativity are established for the estimation error system. Finally, the correctness and the superiority of the developed result are illustrated by a numerical example.

Matrix-Network Modeling of Translation Communication from Perspective of Eco-Translatology

The article presents the results of a search study conducted based on ecosystemic and network approaches in translation studies within the framework of the concept of “human — network environment”. The relevance is determined by the importance of considering the socio-communicative aspect in translation activities and viewing the translator as a functionally integrated element of the network environment. Communicative-network interaction of participants in a translation project is described. The research methodology is based on the potential of matrix and network modeling methods for processing and visualizing data of translation communication. The analysis is carried out using two models: a responsibility distribution matrix and an undirected graph compiled using a social network visualizer program as a hypothetical projection of a more complex model, verified in practice. Examples of analyzing project interaction between written and oral translations based on a protocol of pilot interviews with English language translators about their experience working in a translation department at an industrial enterprise in the Chelyabinsk region are used as materials. The comprehensive methodology of matrix-network modeling allows for explicitating the specifics of work in a translation project at an enterprise, identifying control-network nodes that accumulate the main communicative load, and determining strategies to enhance communication efficiency.

Application of SSA-KNN Algorithm in Fault Analysis of Power Metering Automation System

In view of the great pressure of operation and maintenance of power energy data collection terminal after putting into operation, an intelligent fault diagnosis system for power equipment terminal based on computer was developed. This paper proposes an Agent based on autonomy, promotility, cooperation and interactivity. It also provides a flexible interactive communication interface and load balancing optimization method. It can meet a variety of data transmission specifications and a large number of data service transmission requirements. The SA-KNN algorithm is used to analyze the power quality. Methods The operation data of maintenance electricity were collected. Learning and cluster analysis are carried out by using relevant data, and the calculation model of automatic check device failure is established. At the same time, the outlier points in the collected data are used to accurately locate the fault points of the power grid. This method can realize the comprehensive fault diagnosis of the power grid by using the interaction between the power grid and each component.

Distributed Event-Triggered Quantized Fault-Tolerant Control of Linear Multiagent Systems With External Disturbances and Parameter Uncertainties.

In this article, the issue of fault-tolerant leader-following consensus under a distributed dynamic event-triggered mechanism is addressed for linear multiagent systems (MASs) in the presence of unknown parameter uncertainties, external disturbances, and actuator faults, including loss of effectiveness and bias, in which the mechanism is with quantized state measurements. Due to the fact that information is transmitted via a bandwidth-limited communication network, a quantized control scheme with a uniform quantizer is introduced for leader-following consensus. In order to decrease the communication load and save the limited communication network resources, a distributed event-triggered mechanism is studied for leader-following consensus problem of linear MASs with quantized state measurements. In the presence of actuator faults, external disturbances, and unknown parameter uncertainties, an adaptive coupling gain for the controller is presented. Based on the Lyapunov function approach, the stability of the closed-loop system and the convergence of consensus errors are proved. Furthermore, the Zeno behavior is excluded for the triggering time sequences. Finally, simulation studies are given to verify the effectiveness of the proposed event-triggered fault-tolerant control scheme.

Command filter-based event-triggered control for stochastic MEMS gyroscopes with finite-time prescribed performance

This paper proposes an adaptive neural control strategy for stochastic microelectromechanical system (MEMS) gyroscopes, aiming to achieve a prescribed performance in a finite time. The radial basis function neural network is introduced to address the system’s unknown nonlinear dynamics and stochastic disturbances. Then, the technology of finite-time prescribed performance function, along with the method of command-filtered backstepping design, is utilized to ensure both transient and steady-state performance and simultaneously solve the problem of "explosion of complexity." Moreover, a switching threshold event-triggered control law is proposed to cut down on communication resources and eliminate corresponding parametric inequality restrictions. The proposed adaptive state feedback control strategy is able to guarantee that the output tracking error converges to a prescribed, arbitrarily small residual set. Additionally, the closed-loop system’s signals can be semi-globally ultimately uniformly bounded in probability. Finally, numerical simulations demonstrate the effectiveness and superiority of the proposed strategy.

Confronting combined cyber-attacks on collaborative DC microgrids using a communication protocols-based distributed unscented Kalman filter

The functioning of microgrids (MGs), which represent a contemporary arrangement of distribution networks featuring local control over generation and consumption, necessitates telecommunication infrastructure for transmitting and receiving measurement data and control commands. This reliance on telecommunication infrastructure renders MGs vulnerable to the threat of cyber-attacks. The existing methods for identifying cyber-attacks have been restricted solely to MG modeling. However, identifying and restoring a cyber-attack lack dependability and feasibility in a practical application without the concurrent modeling of MGs and their communication protocols. Therefore, this study aims to identify hybrid cyber-attacks (simultaneous false data injection and time delay attacks) to flow sensors and cyber links in direct flow MGs by considering the communication protocols between them, as well as extracting and reconstructing real measurement data. In order to achieve this objective, a generalized unscented Kalman filter (UKF) is introduced in conjunction with communication protocols incorporating an augmented state space to estimate the power of nonlinear loads. For this purpose, two measurement models have been developed to prevent data collisions and reduce the communication load. These models are based on the round-robin scheduling protocol and the try-once-discard weighted protocol. They aim to establish a specific order for data transfer from sensors to the filter. The article concludes by presenting a stability analysis of the protocol-based generalized UKF, which aims to ensure the convergence and limitation of the estimation error. The study presents simulation results on the resilient reconstruction of hybrid cyber-attacks involving unknown constant power loads and noise. The proposed approach utilizes the protocol-based generalized UKF and exhibits superior performance.

Dynamic triggered‐based tracking approach for connected vehicle system via extended observer

SummaryThis paper investigates the tracking control problem of connected vehicle system suffer from disturbance and communication load. A new dynamic triggered‐based tracking approach was presented. First, an extended state observer is designed to estimate the equivalent disturbance, and the convergence of observed error is proved. Then, a tracking approach based on backstepping control is proposed. Furthermore, in order to reduce the consumption of network resources, a novel tracking method on dynamic event‐triggering strategy is developed, and all signals in connected vehicle system are ultimately uniformly bounded without Zeno behavior is verified. Finally, the effectiveness of the proposed control strategy is testified by simulation results.

Event-triggered adaptive secure tracking control for nonlinear cyber–physical systems against unknown deception attacks

This article presents an event-triggered adaptive neural networks secure tracking control method for a class of nonlinear cyber–physical systems under unknown sensor and actuator deception attacks. To obtain the desired system performance, dynamic surface technique is applied to design controller and radial basis function neural networks are introduced to deal with unknown nonlinear and actuator attacks. By skillfully combining compensation signals with the attack compensators, the unknown deception attacks are effectively mitigated. To reduce the transmission communication load, a novel event-driven control scheme is developed by applying relative threshold triggered mechanism. On this basis, all the signals of closed-loop system are bounded under deception attacks by using Lyapunov stability analysis. Additionally, the presented secure tracking control strategy can ensure the tracking error converges to a small neighborhood of origin and the Zeno behavior is ruled out. Finally, a practical example is employed to verify the feasibility and effectiveness of the designed control algorithm.

Distributed Sparse Precision Matrix Estimation via Alternating Block-Based Gradient Descent

Precision matrices can efficiently exhibit the correlation between variables and they have received much attention in recent years. When one encounters large datasets stored in different locations and when data sharing is not allowed, the implementation of high-dimensional precision matrix estimation can be numerically challenging or even infeasible. In this work, we studied distributed sparse precision matrix estimation via an alternating block-based gradient descent method. We obtained a global model by aggregating each machine’s information via a communication-efficient surrogate penalized likelihood. The procedure chooses the block coordinates using the local gradient, to guide the global gradient updates, which can efficiently accelerate precision estimation and lessen communication loads on sensors. The proposed method can efficiently achieve the correct selection of non-zero elements of a sparse precision matrix. Under mild conditions, we show that the proposed estimator achieved a near-oracle convergence rate, as if the estimation had been conducted with a consolidated dataset on a single computer. The promising performance of the method was supported by both simulated and real data examples.

Standarisasi Prosedur Koordinasi Antara Unit APP dan Tower dalam Menggunakan Sistem Freetext di Perum LPPNPI Cabang Pontianak

In the activity of providing air traffic services, the Pontianak Approach Control Surveillance unit has implemented ATC automation. One of them is the coordination carried out by ATC personnel between the APP unit and the Tower unit. The coordination uses a system called free text. The free text system can facilitate the ATC coordination process, especially between ATC personnel from the APP unit and the Pontianak Branch of the Perum LPPNPI Tower unit, because it can reduce load of communication. However, the SOP of Perum LPPNPI Pontianak Branch has not yet found a procedure regarding standardization of words to coordinate using this free text system. This study aims to form a standardization of writing words in the free text system between Pontianak Approach Control Surveillance and Pontianak Tower. The method used in this research is descriptive qualitative. The results of this study suggest that there is a need for standardization of words to coordinate using the free text system between Tower units and APP units at Perum LPPNPI Pontianak Branch.

Performance recovery-based fuzzy robust control of networked nonlinear systems against actuator fault: A deferred actuator-switching method

This paper concentrates on the performance recovery-based fuzzy robust control of networked nonlinear systems via a deferred actuator-switching approach. The main design procedure of this paper is divided into three parts: 1) nominal controller design; 2) monitoring function and shifting function design; 3) reconfigurable controller design. With the aid of low-computation method and fuzzy logic systems, a structurally and computationally efficient nominal controller is designed for fault-free. Then, an actuator-switching rule is established based on monitoring the stabilization error. Furthermore, a new error transformation is constructed via designing the shifting function, by which tangent barrier Lyapunov functions are effective at the replacement instant. Finally, the stabilization error recovers into the prescribed performance within a specified period of time, and a reconfigurable controller is designed for the backup actuator. By employing the event-triggering mechanism with the relative threshold strategy, communication loads from controllers to actuators are reduced. Simulation examples are given to confirm the effectiveness of the proposed control approach.

BDBM: A distributed network simplification method for solving task allocation problems

Existing task allocation algorithms often fail to consider communication load and do not meet the scalability requirements of large-scale systems in practical applications. Furthermore, many general network simplification algorithms are centralized and not designed for task allocation scenarios, thus losing their superiority when applied to solve task allocation problems in distributed systems. Inspired by recent network simplification algorithms, this paper introduces a novel network simplification algorithm called bid-based distributed broken-motifs (BDBM), which simplifies the communication network by reducing the number of closed-loop triangles. The BDBM algorithm keeps those communication edges that facilitate the deconfliction based on the initial bids, thus avoiding the surge in the number of convergence iterations caused by the simplification. In addition, CBBA is distributed and requires only two iterations to complete the communication network simplification. Theoretical analysis confirms that the simplified network of BDBM remains connected and can be used in combination with improved task allocation algorithms based on consensus-based bundle algorithm (CBBA). In terms of experiments, we conducted comprehensive experiments in different settings and found that the proposed algorithm does not lead to worse allocation solutions. The statistical results also show that BDBM outperforms state-of-the-art network simplification algorithms in terms of solving efficiency and scalability in assisting CBBA solve large-scale complex task allocation problems.

Enhanced Coordination in the PV–HESS Microgrids Cluster: Introducing a New Distributed Event Consensus Algorithm

To ensure reliable power delivery to customers under potential disturbances, the coordination of a microgrid cluster (MGC) is essential. Various control strategies—centralized, decentralized, distributed, and hierarchical—have been explored in the literature to achieve this goal. The hierarchical control method, with three distinct levels, has proven effective in fostering coordination among microgrids (MGs) within the cluster. The third control level, utilizing a time-triggering consensus protocol, relies on a continuous and reliable communication network for data exchange among MGs, leading to resource-intensive operations and potential data congestion. Moreover, uncertainties introduced by renewable energy sources (RESs) can adversely impact cluster performance. In response to these challenges, this paper introduces a new distributed event-triggered consensus algorithm (DETC) to enhance the efficiency in handling the aforementioned issues. The proposed algorithm significantly reduces communication burdens, addressing resource usage concerns. The performance of this approach is evaluated through simulations of a cluster comprising four DC MGs, in each of which were PV and a hybrid Battery-Super capacitor in the MATLAB environment. The key findings indicate that the proposed DETC algorithm achieves commendable results in terms of voltage regulation, precise power sharing among sources, and a reduction in triggering instants. Based on these results, this method can be deemed as a good development in MGC management, providing a more efficient and reliable means of coordination, particularly in scenarios with dynamic loads and renewable energy integration. It is also a viable option for current microgrid systems, due to its ability to decrease communication loads while retaining excellent performance.

Multi-objective cooperative optimization of communication base station and active distribution grid under dual carbon targets

To achieve “carbon peaking” and “carbon neutralization”, access to large-scale 5G communication base stations brings new challenges to the optimal operation of new power systems, but also provides new opportunities for the low-carbon development of distribution networks. This paper develops a method to consider the multi-objective cooperative optimization operation of 5G communication base stations and Active Distribution Network (ADN) and constructs a description model for the operational flexibility of 5G communication base stations. Based on this, a multi-objective cooperative optimization 5G communication base station operating model and active distribution network considering the system operation economy and minimum carbon emissions as the optimization objectives are established. In the above model, by encouraging 5G communication base stations to engage in Demand Response (DR), the Renewable Energy Sources (RES), and 5G communication base stations in ADN are concurrently scheduled, and the uncertainty of RES and communication load is described by using interval optimization method. Finally, the problem is solved by combining the equivalent transformation based on interval analysis and the non-dominated sorting genetic algorithm. The analysis results of the example show that participation in grid-side dispatching through the flexible response capability of 5G communication base stations can enhance the power system’s renewable energy consumption and usage efficiency, resulting in significant low-carbon benefits.

TEAM: A Layered-Cooperation Topology Evolution Algorithm for Multi-Sink Internet of Things

Numerous sensor nodes deployed in the Internet of Things (IoT) can form a large heterogeneous network. The increased energy consumption of sensor nodes and the unbalanced communication load on multiple sink nodes reduce the energy efficiency of the network. Moreover, frequent network attacks also pose severe challenges to topology robustness. Optimizing the network topology to achieve the balance between energy efficiency and robustness is a complex problem. Multi-objective heuristic algorithms based on genetic evolution are commonly used to solve joint optimization problems. However, due to the lack of global search ability caused by the loss of genetic diversity, genetic operations are prone to premature convergence during multi-objective evolution. Therefore, this paper introduces multi-population cooperation into the multi-objective evolution process and proposes a novel layered-cooperation Topology Evolution Algorithm for Multi-sink IoT (TEAM). In TEAM, information entropy is used to measure the effectiveness of load balancing on multiple sink nodes. The crossover and mutation probabilities of different populations are dynamically adjusted to ensure genetic diversity. A layered-cooperation mechanism is designed to avoid premature convergence. Extensive experiments confirm that TEAM can effectively improve the energy efficiency and robustness of network topology while balancing the communication load on multi-sink nodes.

Asynchronous Event-Triggered Distributed Predictive Control for Multiagent Systems With Parameterized Synchronization Constraints

This paper proposes an asynchronous event-triggered distributed predictive control method for the cooperation of multi-agent systems with parameterized synchronization constraints. A novel event-triggering condition related to the parameterized information received from neighboring agents is derived for each agent based on the input-to-state stability (ISS) condition. In such a framework, the distributed optimization problem is solved and the parameterized information is exchanged between neighboring agents, only when the triggering condition is satisfied. In addition, the dual mode control is adopted in this work and no information will be exchanged between the agent and its neighbors when the agent enters the invariant set, reducing the computation and communication load. Moreover, the recursive feasibility of the proposed algorithm is proved, and a sufficient condition is developed to ensure the stability of the closed-loop system. Finally, numerical simulation is given to verify the effectiveness of the proposed method.

Practical generalized finite-time synchronization of duplex networks with quantized and delayed couplings via intermittent control

<abstract><p>This paper investigates the practical generalized finite-time synchronization (PGFETS) of duplex networks with quantized and delayed couplings. Given that continuous transmission of signals will increase the load and cost of communication, we introduce quantized couplings in the model. Then, via the theorem of finite-time stability, the PGFETS is proposed based on the fact that PGFETS is much more extensive and practical than classical finite-time synchronization. Some sufficient criteria are formulated to achieve the goal of synchronization by utilizing quantized intermittent control schemes. Lastly, the validity of the theoretical results is illustrated by numerical simulations.</p></abstract>

Exponential Asynchronous Stabilization for Delayed Semi-Markovian Neural Networks via DAEIC.

The exponential asynchronous stabilization (EAS) issue for a category of neural networks (NNs) with semi-Markov jump (SMJ) parameters and additive time-varying delays (ATDs) is addressed in this article. Here, the SMJ parameters in the controller gain are supposed to be distinct from those in the system structure, which is more consistent with the actual situation. To further relieve the communication load of the network, a new discrete adaptive event-triggered impulsive control (DAEIC) scheme is proposed, where the impulsive moments are the sampling instants satisfying event-triggered constraints, and the triggering threshold can be dynamically adjusted by an adaptive update rule (AUR) related to the current sampling state and the last triggered state. A more flexible looped Lyapunov-Krasovski functional (LLKF) is constructed to commendably capture the available information about impulsive instants, triggering state, sampling interval, ATDs, and heterogeneous SMJ parameters. Combined with the LLKF, DAEIC scheme, and other inequality analysis approaches, some novel results guaranteeing the EAS of the underlying systems are exported. Finally, three explanatory examples are presented to check the validity of our results.

Event-triggered data-driven load frequency control for multi-area power system with wind penetration under deregulation environment

In order to solve the problem of power exchange between areas of multi-area power systems under the power market, this paper designs a power trading contract based on the generation participation matrix, so as to simulate the specific process of power change in each area. With the integration of wind power into the large-scale power system, it is difficult to model the multi-area power system. For this situation, this paper designs a data-driven model-free adaptive load frequency control algorithm based on collecting input and output data, which gets rid of the dependence of the power system on the model. Along with the frequent transmission of input and output data in each area, the communication load of the power system also increases. Aiming at saving communication resources, this paper designs an event-triggered mechanism to reduce the communication bandwidth. The stability of the control algorithm is demonstrated theoretically. Finally, a three-area power system with wind penetration is used as an example to simulate and verify the effectiveness of the proposed algorithm in this paper.