Time-varying Communication Topologies Research Articles

Multi-group consensus of multi-agent systems subject to semi-Markov jump topologies against hybrid cyber-attacks

Multi-agent systems are widely used in practice. The existing results on the consensus of multi-agent systems mainly focus on the complete consensus, which can not deal with the situation of the system in the face of multiple tasks. In this paper, the multi-group consensus problem of multi-agent systems with semi-Markov jump topologies against hybrid cyber-attacks is studied. The purpose is to ensure that the multi-agent systems can achieve different consensus goals when they encounter hybrid cyber-attacks, and to solve the problem of time-varying communication topology between agents modeled by the semi-Markov process. Additionally, two types of cyber-attacks are taken into account in a single framework through two independent sets of Bernoulli sequences. Subsequently, a necessary condition is established to guide the system toward achieving the multi-group consensus objective. Lastly, a series of illustrative examples are presented to underscore the validity of the designed controller.

Consensus-Based Merging and Spacing Method for Aircraft Self-Separation at Intersections

Within the context of delegating separation related tasks from an air traffic controller to the flight deck, this paper analyzes the merging and spacing problem for autonomous aircraft operations at busy airway intersections. A consensus-based control protocol is developed to achieve an ordered and safe traffic flow at a given intersection waypoint. Two primary implementation steps are covered in the protocol. In the first step, consensus on the target merging flow is achieved, where the merging stream is modeled as a straight line with the minimum total converging distance. In the second step, multiple aircraft with double-integrator dynamics and time-varying communication topologies are guided to merge into the target flow while maintaining appropriate interaircraft spacing. A state control law is designed, and the Lyapunov analysis method is leveraged to derive sufficient conditions on the communication topology and the control parameter for merging agents to reach consensus in finite time. Performance of the proposed method is evaluated through experimental simulations under different hypothetical scenarios in the two-dimensional plane.

Temperature field optimization for distributed combined heat of multi-microwave sources based on group consistency under time-varying communication topology

Microwave heating is one of the commonly used methods for heat treatment of various materials. This paper proposes a consistent variable power heating strategy under multi microwave source cluster switching topology mode. Firstly, a distributed structure of microwave source cluster collaborative output with bidirectional ring communication topology was constructed using virtual structure method. On this basis, nonlinear distributed control with consistent output of microwave source clusters in different modes was achieved. Meanwhile, a microwave source clustering scheme is proposed, and the objective function of automatic power allocation for each subgroup and its constraint function are constructed to realize the formation of different clustering modes in different time periods, and the consistent output of self-allocation of power for each subgroup. Secondly, the feasibility and effectiveness of the proposed strategy are verified by the experiments of heating SiC materials in a high-power microwave reactor and analyzing the temperature uniformity and energy conversion efficiency during the heating process. Finally, the simulation results show that the temperature uniformity is improved by 35.7%–63.6% and the energy conversion efficiency is improved by 27.9%–41.1%, which can effectively optimize the temperature field distribution.

Attention Enhanced Reinforcement Learning for Multi agent Cooperation.

In this article, a novel method, called attention enhanced reinforcement learning (AERL), is proposed to address issues including complex interaction, limited communication range, and time-varying communication topology for multi agent cooperation. AERL includes a communication enhanced network (CEN), a graph spatiotemporal long short-term memory network (GST-LSTM), and parameters sharing multi-pseudo critic proximal policy optimization (PS-MPC-PPO). Specifically, CEN based on graph attention mechanism is designed to enlarge the agents' communication range and to deal with complex interaction among the agents. GST-LSTM, which replaces the standard fully connected (FC) operator in LSTM with graph attention operator, is designed to capture the temporal dependence while maintaining the spatial structure learned by CEN. PS-MPC-PPO, which extends proximal policy optimization (PPO) in multi agent systems with parameters' sharing to scale to environments with a large number of agents in training, is designed with multi-pseudo critics to mitigate the bias problem in training and accelerate the convergence process. Simulation results for three groups of representative scenarios including formation control, group containment, and predator-prey games demonstrate the effectiveness and robustness of AERL.

Attention-Based Mechanisms for Cognitive Reinforcement Learning

In this paper, we propose a cognitive reinforcement learning method based on an attention mechanism (CRL-CBAM) to address the problems of complex interactive communication, limited range, and time-varying communication topology in multi-intelligence collaborative work. The method not only combines the efficient decision-making capability of reinforcement learning, the representational capability of deep learning, and the self-learning capability of cognitive learning but also inserts a convolutional block attention module to increase the representational capability by using the attention mechanism to focus on important features and suppress unnecessary ones. The use of two modules, channel and spatial axis, to emphasize meaningful features in the two main dimensions can effectively aid the flow of information in the network. Results from simulation experiments show that the method has more rewards and is more efficient than other methods in formation control, which means a greater advantage when dealing with scenarios with a large number of agents. In group containment, the agents learn to sacrifice individual rewards to maximize group rewards. All tasks are successfully completed, even if the simulation scenario changes from the training scenario. The method can therefore be applied to new environments with effectiveness and robustness.

Differentially Private Distributed Nash Equilibrium Seeking for Aggregative Games

This article considers the privacy-preserving distributed Nash equilibrium seeking strategy design for average aggregative games, in which the players&#x2019; objective functions are considered to be sensitive information to be protected. In particular, we consider that the game is free of central node and the aggregate information is not directly available to the players. As there is no central authority to provide the aggregate information required by each player to update their actions, a dynamic average consensus protocol is employed to estimate it. To protect the players&#x2019; privacy, we perturb the transmitted information among the players by independent random noises drawn from Laplace distributions. By synthesizing the perturbed average consensus protocol with a gradient algorithm, a distributed privacy-preserving Nash equilibrium seeking strategy is established for the aggregative games under both fixed and time-varying communication topologies. With explicit quantifications of the mean square errors, the convergence results of the proposed methods are presented. Moreover, it is analytically proven that the proposed algorithm is <inline-formula><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula>-differentially private. The presented results indicate that there is a tradeoff between the convergence accuracy and the privacy level. Last, a numerical example is provided for the verification of the proposed methods.

Resilient Consensus in Robot Swarms With Periodic Motion and Intermittent Communication

In this article, we propose an approach to construct a time-varying communication topology with a resilient consensus performance for robot swarms with limited communication ranges. The robots are deployed to explore a large task space and achieve consensus despite the existence of a finite number of noncooperative members in the team. Existing methods encouraged robots to stay close to each other to achieve certain robustness requirements on the connectivity of the communication topology. We leverage on the robots&#x2019; mobility to design a time-varying integrated topology composed of several subgroups of robots deployed on nonoverlapping closed-loop paths. Robots are spread out and move along the paths, forming periodic communication links within or across groups. We analyze the time-varying topology synthesized and provide sufficient conditions for individual subgroups and the interconnection between them. We show designs satisfying the conditions with simulated examples in a lattice space, as well as in a task space with predefined paths.

Bearing-based Formation Tracking of Multi-UAV System under Time-varying Directed Interaction

A problem of bearing-based formation control of a group of Unmanned Aerial Vehicles (UAVs) with directed and time-varying communication topology is considered. A subset of vehicles called “leaders” are remotely controlled to perform formation manoeuvres. The remaining vehicles, called “followers” use autonomous bearing-based control law for achieving the desired formation and maintaining it during the manoeuvres. Sufficient conditions for uniqueness of the formation in leader-follower configuration are analysed. For the time-varying manoeuvres of the leaders, the proposed control is shown to exponentially stabilize the formation. A switching time bound is presented that ensures the stability of the system with the proposed control under switching interaction topology. The results are validated with numerical simulations on a multi-vehicle system with directed and time-varying communication topologies.

Synchronization of Multi-Agent Systems under Time-Varying Network via Time-Delay Approach to Averaging

This paper studies the leader-tracking problem of Multi-Agents Systems under a periodic time-varying communication topology, without requiring the connectivity of the network for all t ≥ 0. The case of both state and communication delays is considered. A fully distributed control protocol, along with the constructive time-delay approach to periodic averaging, are combined in order to solve the problem, thus ensuring that a time-dependent switching control rule preserves the input-to-state stability (ISS) of the entire network, despite the presence of disconnected topologies, state and communication delays. The original closed-loop error systems is transformed into a neutral-type system with discrete and distributed delays. ISS analysis of the neutral system employs appropriate Lyapunov-Krasovskii functionals leading to simple ISS conditions in terms of Linear Matrix Inequalities (LMIs), whose solution allows finding upper bounds on small parameter, state and communication delays that preserve ISS. Numerical simulations illustrate the effectiveness of the theoretical results.

Distributed data driven control for multi-agent consensus with unknown system dynamics

This study investigates the consensus tracking problem for unknown multi-agent systems (MASs) with time-varying communication topology by using the methods of data-driven control and model predictive control. Under the proposed distributed iterative protocol, sufficient conditions for reducing tracking error are analyzed for both time invariable and time varying desired trajectories. The main feature of the proposed protocol is that the dynamics of the multi-agent systems are not required to be known and only local input-output data are utilized for each agent. Numerical simulations are presented to illustrate the effectiveness of the derived consensus conditions.

Data-based optimal coordination control of continuous-time nonlinear multi-agent systems via adaptive dynamic programming method

This paper focuses on the optimal coordination control problem for continuous-time nonlinear multi-agent systems with completely unknown dynamics via a data-based distributed adaptive dynamic programming method. As for most real-world applications, accurate system models are complicated to obtain, which restricts the application of the conventional methods. Moreover, it is challenging to design optimal coordination control of multi-agent systems especially for the time-varying communication topology. To deal with the difficulties, we investigate a distributed adaptive dynamic programming method with identifier-critic architecture under the switching communication topology. First, using the available system data, an online adaptive identifier is developed to approximate the unknown model dynamics, and simultaneously a critic neural network is employed for approximation of the optimal cost function, which yields approximated optimal coordination control in real time. Then, we analyze the stability of our proposed scheme. Eventually, the simulation illustrates the effectiveness of the developed method.

Consensus-Based Decentralized Optimization for Distributed Generators Power Allocation Over Time-Varying Digraphs in Microgrids

In this article, a consensus-based decentralized optimization method for distributed generators (DGs) power allocation is proposed to ensure the economic and reliable operation of microgrids. According to the method, all parameters are designed in a fully distributed fashion, which does not depend on any centers or leaders. To further achieve decentralization and to meet the flexible requirement of DGs, the strategy is adaptable to time-varying communication topologies (time-varying digraphs) and is proved to be convergent. During the communication process, the stochastic noise is introduced to imitate a nonideal communication environment, and the convergence rate is accelerated via coupling designed gains of noise and feedback (mismatch). Meanwhile, the cost of reactive power generation and reactive power compensation are considered for a comprehensive optimization, which guarantees the locally reactive power balance and reduces the cost. Simulations based on a test microgrid are used to verify the effectiveness of the proposed method. Finally, the dynamic characteristics and robustness are evaluated and compared by numerical simulation results.

Distributed consensus of heterogeneous multi-agent systems subject to switching topologies and delays

In this paper, the problem of distributed output consensus is investigated for heterogeneous multi-agent systems subject to a class of uncertainties in communication channels. The studied uncertainties, leading to a time-varying communication topology modeled as a Markovian switching communication topology and delayed transit information, may happen discontinuously and affect the consensus behavior of multi-agent systems. To mitigate the effects of such uncertainties on output consensus, a novel switching distributed control protocol is proposed, aiming to guarantee output consensus of the studied networks in the mean square sense. Moreover, an algorithm is given to select the required controller gains based on the solutions to a Lyapunov inequality and a group of regulator equations. Along this framework, a simulation example on the IEEE 24 Bus System is presented to demonstrate the effectiveness of the algorithm and the theoretical results.

Scalable Distributed State Estimation in UTM Context.

This article proposes a novel approach to the Distributed State Estimation (DSE) problem for a set of co-operating UAVs equipped with heterogeneous on board sensors capable of exploiting certain characteristics typical of the UAS Traffic Management (UTM) context, such as high traffic density and the presence of limited range, Vehicle-to-Vehicle communication devices. The proposed algorithm is based on a scalable decentralized Kalman Filter derived from the Internodal Transformation Theory enhanced on the basis of the Consensus Theory. The general benefit of the proposed algorithm consists of, on the one hand, reducing the estimation problem to smaller local sub-problems, through a self-organization process of the local estimating nodes in response to the time varying communication topology; and on the other hand, of exploiting measures carried out nearby in order to improve the accuracy of the local estimates. In the UTM context, this enables each vehicle to estimate both its own position and velocity, as well as those of the neighboring vehicles, using both on board measurements and information transmitted by neighboring vehicles. A numerical simulation in a simplified UTM scenario is presented, in order to illustrate the salient aspects of the proposed algorithm.

A Computationally Efficient Consensus-Based Multiagent Distributed EMS for DC Microgrids

In this article, we present a fully distributed optimization approach for real-time energy management of dc microgrids. In the proposed approach, the energy management system (EMS) is formulated with a consensus algorithm based on incremental cost, which operates in a distributed manner for both generation and demand-side management. The proposed algorithm is privacy-preserving, scalable, plug-and-play capable, and robust against time-varying communication topologies. Furthermore, it converges very fast in terms of computation time and iterations. The scalability of the proposed algorithm was evaluated using IEEE bus systems in various sizes, number of agents, and communication topologies. A cooperative multilayered EMS optimally coordinated by multiple agents was also proposed in this article for integration. The effectiveness of the proposed system was validated through computer simulations and hardware experiments.

Noise-based synchronization of bounded confidence opinion dynamics in heterogeneous time-varying communication networks

A fundamental question that arises in the analysis of the noise-based synchronization of opinion dynamics with bounded confidence (BC) is what opinion structures can be synchronized by noise. In the standard Hegselmann-Krause (HK) model, each agent examines the opinion values of all other agents and then selects neighbors who are appropriate for opinion updating in accordance with the BC scheme. In reality, however, people are more likely to exchange opinions with only a limited set of individuals, resulting in a predetermined local communication network as postulated in the DeGroot model. In this paper, we develop a new model of opinion formation that endows the heterogeneous HK dynamics with a time-varying communication topology, and we investigate its noise-induced synchronization both mathematically and numerically. We show that opinion dynamics in this model are noise-synchronizable if and only if the switching communication graph is uniformly jointly connected. Our rigorous mathematical analysis and simulation experiments demonstrate that connected discourse topology in combination with fair amounts of noise can lead to a quasi-consensus that is inducible from any initial state or any underlying system size. Our findings have profound implications for the design of future strategies of social control and global opinion optimization.

Distributed Control of Time-Varying Signed Networks: Theories and Applications.

Signed networks admitting antagonistic interactions among agents may polarize, cluster, or fluctuate in the presence of time-varying communication topologies. Whether and how signed networks can be stabilized regardless of their sign patterns is one of the fundamental problems in the network system control areas. To address this problem, this paper targets at presenting a self-appraisal mechanism in the protocol of each agent, for which a notion of diagonal dominance degree is proposed to represent the dominant role of agent's self-appraisal over external impacts from all other agents. Selection conditions on diagonal dominance degrees are explored such that signed networks in the presence of directed time-varying topologies can be ensured to achieve the uniform asymptotic stability despite any sign patterns. Further, the established stability results can be applied to achieve bipartite consensus tracking of time-varying signed networks and realize state-feedback stabilization of time-varying systems. Simulations are implemented to verify our uniform asymptotic stability results for directed time-varying signed networks.

Distributed finite‐time information discovery‐based secondary restoration for islanded microgrids

A new finite-time secondary voltage and frequency restoration scheme for an islanded microgrid (MG) is proposed in this study. All distributed generators (DGs) do not have access to the voltage and frequency reference value which makes their control more challenging. Therefore, a finite-time Information Discovery Scheme (IDS) is incorporated into the distributed secondary control scheme. The IDS provides local estimates of the global references of voltage and frequency for DGs in finite-time, and distributed control scheme uses this information to restore the voltage and frequency of MG to their reference values while accurately sharing the active power among the DGs in finite-time. The proposed control scheme is implemented on a sparse communication network and obviates the need for a central controller thereby reducing the risk of single-point-failure. The proof for finite-time restoration and restoration time upper-bound are derived using Lyapunov theory. The effectiveness of the proposed control scheme is verified through simulation of an MG test system in MATLAB/SimPowerSystem environment for sudden load variation and time-varying communication topology. The proposed control scheme supports plug and play capability and exhibits better convergence performance and disturbance rejection than the asymptotic controller based on neighbourhood tracking error.

Robust Cooperative Output Regulation of Heterogeneous Uncertain Linear Multiagent Systems With Time-Varying Communication Topologies

This article investigates the robust cooperative output regulation problem for heterogeneous uncertain linear multiagent systems, with time-varying communication topologies. Suppose that the considered systems are detectable by all measurements, rather than the conventional regulated output alone. Under this weaker detectability condition, a new distributed regulator via partial state feedback is first established with the addition of an integrator to the input channel. This is then extended to obtain the practical cooperative output regulation via measurement feedback with the employment of an extended high-gain observer.

HVAC-Based Cooperative Algorithms for Demand Side Management in a Microgrid

The high penetration of renewable power generators and various loads have brought a great challenge for dispatching energy in a microgrid system. Heating ventilation air conditioning (HVAC) system, as a household appliance with high popularity, can be considered as an effective technology to alleviate energy dispatch issues. This paper presents novel distributed algorithms based on HVAC to solve the demand side management problem, where the microgrid system with HVAC units is considered as a multi-agent system (MAS). The approach provides a desirable operating frequency signal for each HVAC based on the power mismatch value occurring on each local bus. It utilizes demand response of the HVAC units to minimize the supply-demand mismatch, thus reducing the quantity and capacity of energy storage devices potentially to be required. Compared with existing approaches focusing on the distributed algorithms under a fixed communication network, this paper addresses a consensus problem under a switching topology by using the Lyapunov argument. It is verified that a jointly strong and connected topology is a sufficient condition in order to achieve an average consensus for a time-varying topology. A number of cases are studied to evaluate the effectiveness of the algorithms by taking into account its power constraints, dynamic behaviors, anti-damage characteristics and time-varying communication topology. Modelling these system interactions has demonstrated the feasibility of the proposed microgrid system.