Limited Communication Bandwidth Research Articles

Event-Triggered Model Predictive Control for Multiagent Systems With Communication Constraints

This article is concerned with the problem of distributed model predictive control (DMPC) for second-order multiagent systems under event-triggered technique and logarithm quantized communication for a directed topological graph. Considering the limitation of communication bandwidth, a new bounded logarithm quantized communication strategy is proposed to preprocess the information before its transmission, thus reducing the influence of quantization error on the final convergence state. In order to decrease the frequency of control law update and reduce the power consumption, a distributed event-triggered rule is designed to decide when to transmit the information and when to optimize the model predictive control, in which trigger function synthesizes three factors, namely, predictive step, saturation of quantizer, and event-triggered error related with quantized error. The optimal control sequence of DMPC guides the update of controller between two triggering instants. The relationship among the quantization level, event-triggered parameters, and Laplacian matrix is established. Conditions are presented to ensure that all leaders asymptotically converge to a designed formation configuration, while all followers reach to the convex hull of them. Finally, an example is given to illustrate the effectiveness of the proposed methods.

Event-based asynchronous and resilient filtering for singular Markov jump LPV systems against deception attacks

This article addresses the issue of dissipative asynchronous and resilient filter design for singular Markov jump linear parameter-varying (SMJLPV) systems against deception attacks under the dynamic event-triggered transmission protocol. Firstly, an improved dynamic event-triggered transmission protocol is provided to further relieve the channel congestion caused by the bandwidth limited communication network. Then, the deception attack, which can potentially destroy the integrity of the system, is modelled as a random variable satisfying Bernoulli distribution. Since the filter cannot identify the original system mode accurately, the hidden-Markov-model (HMM) is established to describe the phenomenon that two modes are out of synchronization. By augmenting the states of the original system and the filter, the filtering error systems are converted into SMJLPV systems with partially known transition rates (TRs). Based on the parameter-dependent linear matrix inequalities (PDLMIs), a cooperative design technique for the asynchronous filter and the weighting matrix of the dynamic event-triggered transmission protocol is proposed. Lastly, a numerical instance and a resistance-inductance-capacitance (RLC) switch circuit system are employed to verify the effectiveness of the theoretical results.

Event-Triggered Adaptive Higher-Order Sliding Mode Tracking Control for Steer-by-Wire Systems

Abstract The model uncertainty of the steer-by-wire (SbW) system and the limitation of communication bandwidth will have a negative effect on its control performance. For this reason, this paper proposes an event-triggered high-order sliding mode control for uncertain SbW systems. First, to save communication and computing resources, an event-triggering mechanism that depends on the system state is proposed for the SbW system, such that both communication and computing resources can be saved. Second, an event-triggered adaptive higher-order sliding mode (ET-AHOSM) control is proposed for the closed-loop SbW system. The assumptions about the global Lipschitz of nonlinearity and the a priori bounds of the disturbance are no longer required in the control design. Much importantly, the control input continuity can be guaranteed even there is the event-triggering communication in the controller-to-actuator channel. Theoretical analysis shows that the global practical finite-time stability of the closed-loop SbW system can be obtained while avoiding Zeno behavior of the event-triggered control system. Finally, numerical simulation and experiments show that the designed control method can reduce more than 1/2 of the calculation and communication resources while ensuring satisfactory tracking accuracy.

Input-Dynamic Distributed Algorithms for Communication Networks

Consider a distributed task where the communication network is fixed but the local inputs given to the nodes of the distributed system may change over time. In this work, we explore the following question: if some of the local inputs change, can an existing solution be updated efficiently, in a dynamic and distributed manner? To address this question, we define the batch dynamic \congest model in which we are given a bandwidth-limited communication network and a dynamic edge labelling defines the problem input. The task is to maintain a solution to a graph problem on the labeled graph under batch changes. We investigate, when a batch of α edge label changes arrive, \beginitemize \item how much time as a function of α we need to update an existing solution, and \item how much information the nodes have to keep in local memory between batches in order to update the solution quickly. \enditemize Our work lays the foundations for the theory of input-dynamic distributed network algorithms. We give a general picture of the complexity landscape in this model, design both universal algorithms and algorithms for concrete problems, and present a general framework for lower bounds. In particular, we derive non-trivial upper bounds for two selected, contrasting problems: maintaining a minimum spanning tree and detecting cliques.

C4ISR Service Deployment Based on an Improved Quantum Evolutionary Algorithm

To overcome the limited communication bandwidth in the deployment of command, control, communications, computers, intelligence, surveillance, and reconnaissance (C4ISR) services, a service deployment platform was extended from the cloud to a terminal platform based on the concept of fog computing. Additionally, computing and memory resources on the terminal platform were used to alleviate the communication pressure due to the flow of combat information. A corresponding C4ISR service deployment model was constructed, and the quantum evolutionary algorithm, the shortest path algorithm, and the pairwise exchange method were combined to create a two-level search algorithm. This method can plan the operational information flow path and service deployment simultaneously, thereby achieving the optimal deployment of services even under the constraints of limited communication bandwidth.

FedPSO: Federated Learning Using Particle Swarm Optimization to Reduce Communication Costs.

Federated learning is a learning method that collects only learned models on a server to ensure data privacy. This method does not collect data on the server but instead proceeds with data directly from distributed clients. Because federated learning clients often have limited communication bandwidth, communication between servers and clients should be optimized to improve performance. Federated learning clients often use Wi-Fi and have to communicate in unstable network environments. However, as existing federated learning aggregation algorithms transmit and receive a large amount of weights, accuracy is significantly reduced in unstable network environments. In this study, we propose the algorithm using particle swarm optimization algorithm instead of FedAvg, which updates the global model by collecting weights of learned models that were mainly used in federated learning. The algorithm is named as federated particle swarm optimization (FedPSO), and we increase its robustness in unstable network environments by transmitting score values rather than large weights. Thus, we propose a FedPSO, a global model update algorithm with improved network communication performance, by changing the form of the data that clients transmit to servers. This study showed that applying FedPSO significantly reduced the amount of data used in network communication and improved the accuracy of the global model by an average of 9.47%. Moreover, it showed an improvement in loss of accuracy by approximately 4% in experiments on an unstable network.

Network-Based $$H_\infty $$ Filtering for Descriptor Markovian Jump Systems with a Novel Neural Network Event-Triggered Scheme

This paper studies network-based $$H_\infty $$ filtering problem for descriptor Markovian jump systems with a novel neural network event-triggered scheme. Firstly, to save more limited communication bandwidth, a novel neural network event-triggered scheme is introduced to dynamically adjust communication bandwidth based on desired filtering performance. Secondly, an event-triggered mode-dependent $$H_\infty $$ filter is designed for descriptor Markovian jump system. By considering the network-induced delay and the event-triggered scheme, a delay system method is used to build a novel filtering error system model. By using Lyapunov function technology and free weighting method, the criteria are obtained in terms of LMIs which guarantee the filtering error system to be regular, impulse free and stochastically stable with the $$H_\infty $$ performance. Then, a co-design method is proposed for the designed filter parameters. Finally, a numerical simulation example is employed to illustrate the effectiveness, and by a compared example, we show that the number of transmitted data produced by the proposed neural network event-triggered scheme is less than those produced by traditional event-triggered scheme.

Synchronization control of neutral-type neural networks with sampled-data via adaptive event-triggered communication scheme

This paper addresses the problem of synchronization control of neutral-type neural networks with sampled-data, where sampled data will be over a communication network before received by controller. Generally, the communication network is with a bandwidth-limited communication channel. To reduce network burden, an event-triggered scheme is designed between the sampler and communication network. A weak synchronization conditions are derived by using our proposed integral inequality. Finally, a numerical example is given to illustrate the effectiveness and advantage of the proposed results.

TWINBOT: Autonomous Underwater Cooperative Transportation

Underwater Inspection, Maintenance, and Repair operations are nowadays performed using Remotely Operated Vehicles (ROV) deployed from dynamic-positioning vessels, having high daily operational costs. During the last twenty years, the research community has been making an effort to design new Intervention Autonomous Underwater Vehicles (I-AUV), which could, in the near future, replace the ROVs, significantly decreasing these costs. Until now, the experimental work using I-AUVs has been limited to a few single-vehicle interventions, including object search and recovery, valve turning, and hot stab operations. More complex scenarios usually require the cooperation of multiple agents, i.e., the transportation of large and heavy objects. Moreover, using small, autonomous vehicles requires consideration of their limited load capacity and limited manipulation force/torque capabilities. Following the idea of multi-agent systems, in this paper we propose a possible solution: using a group of cooperating I-AUVs, thus sharing the load and optimizing the stress exerted on the manipulators. Specifically, we tackle the problem of transporting a long pipe. The presented ideas are based on a decentralized Task-Priority kinematic control algorithm adapted for the highly limited communication bandwidth available underwater. The aforementioned pipe is transported following a sequence of poses. A path-following algorithm computes the desired velocities for the robots' end-effectors, and the on-board controllers ensure tracking of these setpoints, taking into account the geometry of the pipe and the vehicles' limitations. The utilized algorithms and their practical implementation are discussed in detail and validated through extensive simulations and experimental trials performed in a test tank using two 8 DOF I-AUVs.

Open-Source Heterogeneous Constrained Edge-Computing Platform for Smart Grid Measurements

This article presents a low-cost, open-source, heterogeneous, resource-constrained hardware platform called “Parallella” as a measurement device for edge-computing applications research in smart grid. The unique hardware architecture of the Parallella provides a multitude of edge-computing resources in the form of a Zynq SoC (dual-core ARM + FPGA) and a 16-core co-processor called Epiphany. A multifunctional intelligent electronic device (IED) design is demonstrated to showcase the capabilities of the platform. A custom I/O board has been developed for the desktop and embedded versions of Parallella, which can be interfaced with external daughter boards and peripherals for measurements. One such daughter board is an analog sensing board, which can measure voltages of all the three phases and four line currents using a 16-bit synchronous ADC set at 32 kHz. The ADC samples are synchronized to the PPS time clock of a GPS unit for providing global time reference. These captured seven-channel raw waveform data are sent to a cloud server over a bandwidth-limited communication channel using a custom anomaly-aware data compression algorithm implemented on the ARM. A phasor measurement algorithm using the Teager energy operator (TEO) is implemented on the field-programmable gate array (FPGA). A parallel power quality (PQ) measurement algorithm is implemented on the Epiphany. The obtained measurements are found to be comparable to a commercial power analyzer.

Distributed Vehicle Tracking in Wireless Sensor Network: A Fully Decentralized Multiagent Reinforcement Learning Approach

Vehicle tracking is one of the important applications of the wireless sensor network (WSN), and sensor scheduling is essential in WSN for achieving an efficient tracking process. Traditional centralized sensor scheduling frameworks cannot meet real-time requirements of vehicle tracking in WSN, because of task overloading caused by limited resource consumption and communication bandwidth. To solve this problem, this letter proposes a multiagent distributed sensor scheduling framework in WSN. This letter first proposes a preactivation-based vehicle tracking model to preactive some sensors in order to reduce unnecessary resource consumption. Then, this letter develops a fully decentralized multiagent reinforcement learning (FDMARL) algorithm to design our multiagent sensor scheduling framework. The simulation results show the convergence and the superiority of our proposed FDMARL-aided sensor scheduling algorithm.

A Heuristic Lowest Unknown-Degree Target Search Strategy Under Non-Structured Environment for Multi-Agent Systems

Multi-target searching is a hotspot and foundation topic in multi-agent systems research. However, most of the research is based on simple environment or known environment, which greatly limits the application of target search. In the non-structured environment, the searching result can be greatly affected by the complex terrain constraints and detectability of targets especially when we have no prior knowledge about the environment. In the paper, a novel search strategy combining maximum visibility and particle swarm optimization is proposed for the target search problem in a completely unknown and non-structural environment. The strategy utilizes the concept of visibility to describe how well the agent detects the map, and guides the agent to perform online path planning to complete the search task. In addition, considering the limited communication distance and communication bandwidth, the strategy introduces a cooperative mechanism for each agent to improve the search efficiency. Finally, in the experimental part, the search strategy is compared with the commonly used search strategies. Compared with the methods combining advantages, the proposed strategy can still achieve similar results, which proves the feasibility and efficiency of the strategy.

Event-based secure [formula omitted] load frequency control for delayed power systems subject to deception attacks

This paper focuses on designing an H∞ load-frequency controller for power systems suffering from deception attacks based on the event-based secure control scheme. Firstly, to alleviate the occurrence of congestion phenomenon in the bandwidth-limited communication network, an event-triggered scheme is introduced into the sensor-to-controller channel. Subsequently, a mathematical model with regard to the event-triggered load frequency control is established under considering the existence of deception attacks. Then, in terms of Lyapunov stability theory and an improved inequality technique, some sufficient conditions that can ensure the mean-square asymptotic stability with a prescribed H∞ performance index of the closed-loop system are deduced. Besides, the corresponding controller gain is obtained by solving those conditions. Finally, two numerical examples are presented to verify the effectiveness of the proposed method.

Distributed Observer-Based Consensus Over Directed Networks With Limited Communication Bandwidth Constraints

In this paper, we present a distributed observer-type consensus controller design for general linear multiagent systems over directed digital networks with limited communication data rates. Considering that each agent can only exchange binary symbolic sequence with its neighbors due to bandwidth constraints, we adopt the probabilistic quantization approach in the distributed consensus controller design. Compared to the existing works, the main contribution is that we use distributed observer-type controllers to inhibit the impact of quantization noises in agent states. First, we study the observer-type protocols with total quantized communications, where the relative-state information of each agent is quantized. The quantized consensus can be achieved, and the upper bounds of steady-state errors in the mean-square sense are also characterized, which are related to the scale of networks, the control parameters, the quantization levels, and the structures of agents. Further, we extend the controller design based on total quantization to the partial quantization case where only the neighbors’ information is quantized.

Prototype of a Computer Vision-Based CubeSat Detection System for Laser Communications

Up to now, CubeSat nano-satellites have strong limitations in communication data rates ( $$\sim \hbox {100}$$ kbps) and bandwidth due to the strictness of CubeSat standard. However, if they could be endowed with optical communications (data rates up to 1 Gbps in optimal state), CubeSat applications would exponentially increase. Nonetheless, laser communications face some important drawbacks as the development of a very strict and accurate tracking mechanism. This work proposes an on-board fine pointing system to locate an optical ground station beacon using an embedded system complying with the restrictive CubeSat standard. Such on-board fine pointing system works based on computer vision. The experimental prototype is implemented in Matlab/Simulink, within a Raspberry Pi 3B. The main outcome is the usage of off-the-shelf components (COTS), obtaining an efficient tracking with low power consumption in very noisy and reflective environments. The developed system proves to be fast, stable and strong. It also satisfies the strict size and power consumption restrictions of CubeSat standard.

Communication and Computing Resource Optimization for Connected Autonomous Driving

Transportation system is facing a sharp disruption since the Connected Autonomous Vehicles (CAVs) can free people from driving and provide good driving experience with the aid of Vehicle-to-Vehicle (V2V) communications. Although CAVs bring benefits in terms of driving safety, vehicle string stability, and road traffic throughput, most existing work aims at improving only one of these performance metrics. However, these metrics may be mutually competitive, as they share the same communication and computing resource in a road segment. From the perspective of joint optimizing driving safety, vehicle string stability, and road traffic throughput, there is a big research gap to be filled on the resource management for connected autonomous driving. In this paper, we first explore the joint optimization on driving safety, vehicle string stability, and road traffic throughput by leveraging on the consensus Alternating Directions Method of Multipliers algorithm (ADMM). However, the limited communication bandwidth and on-board processing capacity incur the resource competition in CAVs. We next analyze the multiple tasks competition in the contention based medium access to attain the upper bound delay of V2V-related application offloading. An efficient sleeping multi-armed bandit tree-based algorithm is proposed to address the resource assignment problem. A series of simulation experiments are carried out to validate the performance of the proposed algorithms.

Event-based disturbance compensation control for discrete-time SPMSM with mismatched disturbances

This paper investigates the problem of event-based disturbance compensation control for discrete-time surface-mounted permanent magnet synchronous motor (SPMSM) subject to both mismatched external disturbances and the limited communication bandwidth. The mismatched external disturbances make it impossible to transform the original model into the multiple-step-ahead predictor model by using the existing predictor technology. To tackle such a challenge, a novel backstepping-based disturbance compensation control framework is firstly proposed for the discrete-time SPMSM with known system dynamics. In the proposed framework, the disturbance observer is designed to compensate for the external disturbances, and the novel variable substitution/decoupling technology is proposed to design the event-based controller for the original controlled system. The proposed controller is composed of the event-based feedback control signal and the feedforward disturbance compensation signal, thereby improving the system disturbance rejection ability and mitigating the communication resource. Subsequently, an event-based adaptive neural control scheme is proposed by combining a modified neural disturbance observer. The proposed scheme ensures that the tracking error converges to a small neighbourhood of the origin, all the signals in the closed-loop system are bounded and meanwhile the communication resources are greatly reduced. The effectiveness of the proposed controller is illustrated through the simulation results.

Finite-Time $\mathcal {L}_{2}$-$\mathcal {L}_{\infty }$ Synchronization for Semi-Markov Jump Inertial Neural Networks Using Sampled Data

This paper investigates the finite-time synchronization issue for semi-Markov jump inertial neural networks, in which the sampled-data control is employed to alleviate the burden of the limited communication bandwidth. Due to the existence of inertial item, the semi-Markov jump inertial neural networks as hybrid neural systems, are depicted with second-order derivatives for the first time, which can be turned to first-order derivatives by the variable transformation. Furthermore, by applying appropriate integral inequalities and constructing the proper Lyapunov functional, some sufficient conditions, which not only guarantee the finite-time synchronization of the resulting error system but also ensure a specified level of <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> - <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> performance, are acquired based on the optimization of integral inequality technique. A numerical example is, eventually, proposed to substantiate the validity of the developed method.

Finite-TimeH∞State Estimation for Two-Time-Scale Complex Networks Under Stochastic Communication Protocol

The issue of finite-time H∞ state estimation is studied for a class of discrete-time nonlinear two-time-scale complex networks (TTSCNs) whose measurement outputs are transmitted to a remote estimator via a bandwidth-limited communication network under the stochastic communication protocol (SCP). To reflect different time scales of state evolutions, a new discrete-time TTSCN model is devised by introducing a singular perturbation parameter (SPP). For the sake of avoiding/alleviating the undesirable data collisions, the SCP is adopted to schedule the data transmissions, where the transition probabilities involved are assumed to be partially unknown. By constructing a new Lyapunov function dependent on the information of the SCP and SPP, a sufficient condition is derived which ensures that the resulting error dynamics is stochastically finite-time bounded and satisfies a prescribed H∞ performance index. By resorting to the solutions of several matrix inequalities, the gain matrices of the state estimator are given and the admissible upper bound of the SPP can be evaluated simultaneously. The performance of the designed state estimator is demonstrated by two examples.

Event-Triggered Quantized Communication-Based Consensus in Multiagent Systems via Sliding Mode

To handle the common existing constraints, that is, limited energy supplies and limited communication bandwidth in multiagent systems (MASs), this article investigates the consensus problem in MASs with event-triggered communication (ETC) and state quantization. In order to compensate for the effect brought by mismatched disturbances, we also propose a novel multiple discontinuous sliding-mode surface, and the corresponding sliding-mode control law is constructed by considering the event-triggered and dynamic quantized mechanisms jointly. Under such a scheme, it is shown that the state trajectories of all the agents will be regulated to achieve consensus asymptotically and the Zeno behavior can be avoided completely. We further extend this work to self-triggered and periodic event-triggered cases. Particularly, in a periodic event-triggered approach, the new form of triggering conditions and upper bound of the sampling periods are provided explicitly. As a result, all agents can reach bounded consensus. Moreover, the upper bound of the consensus error can be arbitrarily adjusted by appropriately selecting parameters, and the periodic event-triggered case will be reduced to the event-triggered case when the bound approaches 0 (sampling periods approach 0 at the same time). A numerical example is illustrated to verify the effectiveness of the proposed algorithms.