Output-Feedback-Based Consensus Control Without Continuous Interagent Communication

ABSTRACTThis paper focuses on a distributed convex optimisation problem over networks with time-varying topologies. First, an event-triggered strategy is employed to reduce computation and communication load in the networked systems. Second, the exponential convergence of event-triggered zero-gradient-sum algorithm is guaranteed if the corresponding time-varying network topology satisfies a new connected condition, called cooperatively connected condition. This condition does not require topologies constantly connected or jointly connected but only requires the integral of the Laplacian matrix of the network topology over a period of time is connected. Hence, it is suitable for more general time-varying topologies. Third, a convergence analysis technique is developed which is based on the difference of the Lyapunov function rather than its differentiation. Finally, a simulation example is provided to verify the results obtained in this paper.

An output-feedback-based decentralized controller that uses event-triggered communication is developed for the leader-follower consensus problem. An observer is designed to estimate the state of each agent, and the estimated state is communicated to its neighboring agents for interaction. To eliminate continuous inter-agent communication, another estimators that estimate the states of neighboring agents are designed for control feedback and are updated via intermittent communication to reset increasing estimate errors. The communication times are based on an event-triggered strategy and are adapted based on the interplay between the control performance and the amount of reduced communication. The main contribution of the developed triggering approach is that inter-agent communication is not required to determine when a state update is needed. Since the developed control scheme introduces switched dynamics, analysis is conducted to indicate that Zeno behavior does not exist. A convergence analysis is also conducted to show bounded convergence of the developed control methodology.

Consensus conditions and convergence speeds are crucial for distributed consensus algorithms of networked systems. Based on a basic first-order average-consensus protocol with time-varying topologies and additive noises, this paper first investigates its critical consensus condition on network topology by stochastic approximation frameworks. A new joint-connectivity condition called extensible joint-connectivity that contains a parameter $\delta$ (termed the extensible exponent) is proposed. With this and a balanced topology condition, we show that a critical value of $\delta$ for consensus is $1/2$ . Optimization on convergence rate of this protocol is further investigated. It is proved that the fastest convergence rate, which is the theoretic optimal rate among all controls, is of the order $1/t$ for the best topologies, and is of the order $1/t^{1-2\delta }$ for the worst topologies, which are balanced and satisfy the extensible joint-connectivity condition. For practical implementation, certain open-loop control strategies are introduced to achieve consensus with a convergence rate of the same order as the fastest convergence rate. Furthermore, a consensus condition is derived for nonstationary and strongly correlated random topologies. The algorithms and consensus conditions are applied to distributed consensus computation of mobile ad-hoc networks; and their related critical exponents are derived from relative velocities of mobile agents for guaranteeing consensus.

Although quite a few results on resilient consensus of multi-agent systems with malicious agents and fixed topology have been reported in the literature, we lack any known results on such a problem for multi-agent systems with time-varying topologies. Herein, we study the resilient consensus problem of time-varying networked systems in the presence of misbehaving nodes. A novel concept of joint ( r, s) -robustness is firstly proposed to characterize the robustness of the time-varying topologies. It is further revealed that the resilient consensus of multi-agent systems under F-total malicious network can be reached by the Weighted Mean-Subsequence-Reduced algorithm if and only if the time-varying graph is jointly ( F+1, F+1) -robust. Numerical simulations are finally performed to verify the effectiveness of the analytical results.

To ensure that social networks (e.g. opinion consensus, cooperative estimation, distributed learning and adaptation etc.) proliferate and efficiently operate, the participating agents need to collaborate with each other by repeatedly sharing information. However, sharing information is often costly for the agents while resulting in no direct immediate benefit for them. Hence, lacking incentives to collaborate, strategic agents who aim to maximize their own individual utilities will withhold rather than share information, leading to inefficient operation or even collapse of networks. In this paper, we develop a systematic framework for designing distributed rating protocols aimed at incentivizing the strategic agents to collaborate with each other by sharing information. The proposed incentive protocols exploit the ongoing nature of the agents' interactions to assign ratings and through them, determine future rewards and punishments: agents that have behaved as directed enjoy high ratings -- and hence greater future access to the information of others; agents that have not behaved as directed enjoy low ratings -- and hence less future access to the information of others. Unlike existing rating protocols, the proposed protocol operates in a distributed manner, online, and takes into consideration the underlying interconnectivity of agents as well as their heterogeneity. We prove that in many deployment scenarios the price of anarchy (PoA) obtained by adopting the proposed rating protocols is one. In settings in which the PoA is larger than one, we show that the proposed rating protocol still significantly outperforms existing incentive mechanisms such as Tit-for-Tat. Importantly, the proposed rating protocols can also operate efficiently in deployment scenarios where the strategic agents interact over time-varying network topologies where new agents join the network over time.

This paper studies the problem of adaptively searching for an unknown target using multiple agents connected through a time-varying network topology. Agents are equipped with sensors capable of fast information processing, and we propose an asynchronous decentralized algorithm for controlling their search based on noisy observations. We propose asynchronous decentralized algorithms for adaptive query-based search that combine the Bayesian bisection method and social learning. Under standard assumptions on the time-varying network dynamics, we prove convergence to correct consensus on the value of the parameter as the number of iterations grow. Our results establish that stability and consistency can be maintained even with one-way updating and randomized pairwise averaging, thus providing a scalable low complexity alternative to the synchronous decentralized estimation algorithms studied in previous works. We illustrate the effectiveness and robustness of our algorithm for random network topologies.

Distributed formation control of multi-UAV systems using relative information

The evolution toward 6G Computing Power Networks (CPN) aims to deeply integrate multi-tier computing resources across Cloud, Edge, and end devices. However, the significant heterogeneity of computing resources, characterized by varying hardware architectures such as CPUs, GPUs, and NPUs, coupled with the time-varying network topology caused by terminal mobility, poses severe challenges to realizing efficient integrated scheduling that satisfies Quality of Service (QoS). To address spatiotemporal mismatches between task requirements and hardware architectures, this paper proposes an integrated scheduling method combining Discrete Time-Varying Graph (DTVG) construction with Multi-Agent Reinforcement Learning (MARL). Specifically, we model the dynamic interaction between mobile tasks and heterogeneous nodes as a DTVG to capture spatiotemporal evolution and employ a QMIX-based algorithm to enable collaborative decision-making among distributed agents. Simulation results demonstrate that the proposed approach effectively solves the joint optimization problem of heterogeneous resource matching and dynamic path planning, significantly outperforming traditional baselines in terms of resource utilization and average latency. This study confirms that incorporating graph-theoretic modeling with reinforcement learning offers a robust solution for the complex coupling of communication and computation in dynamic 6G networks.

In this article, we propose a novel four-stage distributed controller for a dc microgrid that achieves proportional power sharing and average voltage regulation for the voltages at actuated and unactuated buses. The controller is presented for a dc microgrid comprising multiple distributed generation units (DGUs) with time-varying actuation states, dynamic <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$RLC$</tex-math> </inline-formula> lines, nonlinear constant impedance, current, and power (ZIP) loads, and a time-varying network topology. The controller comprising a nonlinear gain, proportional–integral (PI) controllers, and two dynamic distributed averaging stages is designed for asymptotic stability. This constitutes deriving passivity properties for the dc microgrid, along with each of the controller subsystems. Thereafter, design parameters are found through a passivity-based optimization using the worst-case subsystem properties. The resulting closed loop is robust against DGU actuation changes, network topology changes, and microgrid parameter changes. The stability and robustness of the proposed control are verified via simulations.

In the two-layer space communication network (TLSCN), communications can be performed to obtain higher throughput and lower latency by introducing various types of spatial nodes. However, the mobility of spatial nodes and the connectivity of spatial links result in the time-varying network topology, and intermittent link connection. This further leads to the lack of continuous contact, unreliable transmission, and high transmission cost. In this article, contact plan design (CPD) is employed to address the above problems by increasing the contact reliability while decreasing the contact cost. First, a directional space-time graph (DSTG) is constructed by considering the motion trajectory of spatial nodes and the time evolution nature of TLSCN. Afterwards, based on DSTG, we propose three CPD methods with greedy algorithm by considering the limited computing power of nodes. The three methods can optimize the objective functions of the total contact reliability, total contact cost, and invalid probability cost, respectively. The simulation results show that the proposed CPD methods can effectively improve the contact reliability, reduce the contact cost of TLSCN, and perform well with the increase of network density.

This study investigates amplitude death in delayed coupled oscillators with a time-varying network topology. The local stability of amplitude death is governed by a linear time-varying system. This system is difficult to analyze; however, if the time-varying period is sufficiently short compared with the oscillation period, the linear time-varying system can be regarded as the linear time-invariant system. Thus, we can easily analyze the local stability of amplitude death in the fast time-varying network. Our analytical results are verified by some numerical simulations.

We consider the problem of cooperatively minimizing the sum of convex functions, where the functions represent local objective functions of the agents. We assume that each agent has information about his local function, and communicate with the other agents over a time-varying network topology. For this problem, we propose a distributed subgradient method that uses averaging algorithms for locally sharing information among the agents. In contrast to previous works that make worst-case assumptions about the connectivity of the agents (such as bounded communication intervals between nodes), we assume that links fail according to a given stochastic process. Under the assumption that the link failures are independent and identically distributed over time (possibly correlated across links), we provide convergence results and convergence rate estimates for our subgradient algorithm.

We consider the problem of tracking the minimum of a time-varying convex optimization problem over a dynamic graph. Motivated by target tracking and parameter estimation problems in intermittently connected robotic and sensor networks, the goal is to design a distributed algorithm capable of handling nondifferentiable regularization penalties. The proposed proximal online gradient descent algorithm is built to run in a fully decentralized manner and utilizes consensus updates over possibly disconnected graphs. The performance of the proposed algorithm is analyzed by developing bounds on its dynamic regret in terms of the cumulative path length of the time-varying optimum. It is shown that as compared to the centralized case, the dynamic regret incurred by the proposed algorithm over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$T$</tex-math></inline-formula> time slots is worse by a factor of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\log (T)$</tex-math></inline-formula> only, despite the disconnected and time-varying network topology. The empirical performance of the proposed algorithm is tested on the distributed dynamic sparse recovery problem, where it is shown to incur a dynamic regret that is close to that of the centralized algorithm.

Finite-time weighted average consensus with respect to a monotonic function and its application

Event-triggered distributed adaptive cooperative control for multiple dynamic positioning ships with actuator faults