A survey on recent advances in distributed sampled-data cooperative control of multi-agent systems

Consensus for nonlinear multi-agent systems with actuator saturation by adaptive event-triggered scheme

This paper studies the leader-follower consensus problem for nonlinear multi-agent systems with actuator saturation by adaptive event-triggered scheme. This adaptive mechanism allows parameters in triggered mechanism to change automatically compared to fixed ones in traditional strategies. The paper introduces two distinct approaches to address the issue of saturation. The first approach is sector bounded condition, while the second relies on convex hull representation. Compared with the former one, the latter one can reduce the conservatism of controller design effectively. Furthermore, it is assumed that the nonlinear function adheres to incremental quadratic limitations, thereby offering a more comprehensive depiction of its nonlinear properties. Finally, the validity of the proposed approaches is demonstrated by one numerical example in the background of mechanical systems.

Decentralized Sampled-Data Control for Stochastic Disturbance in Interconnected Power Systems With PMSG-Based Wind Turbines

The presented work is concerned with the stability performance of frequency response for interconnected power systems (IPSs) with permanent magnet synchronous generator (PMSG)-based wind turbines (WTs) via a decentralized control scheme against external and stochastic disturbances. To do this, initially, the state-space model is derived when a PMSG is penetration into IPSs. Differing from existing works, IPSs with PMSG-based WTs, including the stochastic disturbance, are modeled as a stochastic state-space model. Then, decentralized sampled-data load frequency control is designed to regulate the frequency response of the proposed model against the deterministic and stochastic noises. By constructing bilateral looped Lyapunov functional and using Itôs formula, stochastic sufficient conditions are derived, which ensure that the closed-loop form of the proposed model is asymptotically stable in the mean square with H∞ performance index γ . Finally, three-area IPSs with PMSG-based WTs are validated with derived sufficient conditions. The simulation results confirmed the better-stability performance of frequency response for the proposed stochastic IPSs with PMSG-based WTs.

Adaptive Event-Triggered Consensus of Multi-Agent Systems in Sense of Asymptotic Convergence

In this paper, the asymptotic consensus control of multi-agent systems with general linear agent dynamics is investigated. A neighbor-based adaptive event-triggering strategy with a dynamic triggering threshold is proposed, which leads to a fully distributed control of the multi-agent system, depending only on the states of the neighboring agents at triggering moments. By using the Lyapunov method, we prove that the states of the agents converge asymptotically. In addition, the proposed event-triggering strategy is proven to exclude Zeno behavior. The numerical simulation results illustrate that the agent states achieve consensus in sense of asymptotic convergence. Furthermore, the proposed strategy is shown to be scalable in case of variable agent numbers.

Sliding Mode Control for Sampled-Data Systems Subject to Deception Attacks: Handling Randomly Perturbed Sampling Periods

In this article, the sliding mode control problem is addressed for a class of sampled-data systems subject to deception attacks. The sampling periods undergo component-wise random perturbations that are governed by a Markovian chain. The component of the sampled output is transmitted via an individual communication channel that is vulnerable to deception attacks, and Bernoulli-distributed stochastic variables are utilized to characterize the random occurrence of the deception attacks initiated by the adversaries. A sliding mode controller is designed to drive the state into the sliding domain around the specified sliding surface, and sufficient conditions are derived to guarantee the exponentially ultimate boundedness of the resultant closed-loop system in the mean-square sense. Furthermore, an optimization problem is established to pursue locally optimal control performance. Finally, a simulation example is given to verify the effectiveness and advantages of the developed controller design approach.

Replicating File Segments between Multi-Cloud Nodes in a Smart City: A Machine Learning Approach

The design and management of smart cities and the IoT is a multidimensional problem. One of those dimensions is cloud and edge computing management. Due to the complexity of the problem, resource sharing is one of the vital and major components that when enhanced, the performance of the whole system is enhanced. Research in data access and storage in multi-clouds and edge servers can broadly be classified to data centers and computational centers. The main aim of data centers is to provide services for accessing, sharing and modifying large databases. On the other hand, the aim of computational centers is to provide services for sharing resources. Present and future distributed applications need to deal with very large multi-petabyte datasets and increasing numbers of associated users and resources. The emergence of IoT-based, multi-cloud systems as a potential solution for large computational and data management problems has initiated significant research activity in the area. Due to the considerable increase in data production and data sharing within scientific communities, the need for improvements in data access and data availability cannot be overlooked. It can be argued that the current approaches of large dataset management do not solve all problems associated with big data and large datasets. The heterogeneity and veracity of big data require careful management. One of the issues for managing big data in a multi-cloud system is the scalability and expendability of the system under consideration. Data replication ensures server load balancing, data availability and improved data access time. The proposed model minimises the cost of data services through minimising a cost function that takes storage cost, host access cost and communication cost into consideration. The relative weights between different components is learned through history and it is different from a cloud to another. The model ensures that data are replicated in a way that increases availability while at the same time decreasing the overall cost of data storage and access time. Using the proposed model avoids the overheads of the traditional full replication techniques. The proposed model is mathematically proven to be sound and valid.

Robust bipartite tracking consensus of multi-agent systems via neural network combined with extended high-gain observer

In this paper, the robust bipartite tracking consensus problem for second-order multi-agent systems has been addressed in the presence of lumped disturbances and unknown velocity information. The leader's dynamics are modeled by uncertain fractional-order equality based on the neural network (NN), and the followers can only obtain the position information of the leader under the signed networks. A novel robust bipartite tracking consensus control scheme is developed by combining the NN approximators, the continuous sliding mode control (SMC) strategy, and the improved extended high-gain observer (EHGO). The improved EHGO is used to compensate for and estimate each agent's lumped disturbances and unknown velocity information in the controller design process. Moreover, NN is constructed to approximate the velocity and acceleration of the uncertain leader's dynamics for generating the feedforward signal of controllers, and the adaptive update laws of estimation parameters are generated online based on the Lyapunov function. Finally, the effectiveness of the proposed control strategy is verified by some numerical simulations.

Performance analysis for interconnected time-delay systems with networked communication

This paper studies the network-based performance analysis for interconnected time-delay system (ITDS), where each subsystem experiences delays in the state and connects arbitrarily via the packet-based communication network. The ITDS with networked communication is modeled as hybrid systems with memory to incorporate the delay and network-induced imperfections into a unified framework. Based on this model, a general hybrid Lyapunov-Krasovskii functional is constructed. Sufficient linear matrix inequality conditions for the ITDS to be asymptotically stable and L₂ stable are proposed, respectively. These conditions are only related to the interconnected structure, the parameters of each subsystem and local network, leading them convenient for feasibility computation. Two examples, including a numerical example and a three-area time-delay power system, are given to show the feasibility of the derived results.

Consensus for Second-Order Multiagent Systems Under Two Types of Sampling Mechanisms: A Time-Varying Gain Observer Method

This article considers the consensus of second-order multiagent systems (MASs) under a directed communication topology. By introducing time-varying gains into observers, two types of novel continuous-discrete-time observers are presented to estimate state information of agents under two sampling mechanisms, respectively, namely: 1) a synchronous nonuniform sampling (SNS) mechanism and 2) an asynchronous nonuniform sampling (ANS) mechanism. A new design method of the time-varying gain observer is proposed, in that it can allow for one to select a consensus protocol with lower cost observers to attain the consensus task. Only sampled position information needs to be transmitted in designing consensus protocols for the MSAs, and neither velocity information nor control input information is required. Under the SNS mechanism, a consensus condition is obtained and it is shown that the introduction of time-varying gains in the observers indeed can improve their convergence. Under the ANS mechanism, the corresponding consensus protocol allows the sampling and transmission of different agents to be asynchronous and aperiodic, which means that each agent may sample and transmit its information in line with its own sampling requirements. Furthermore, each sampling period can be arbitrarily selected within a certain range while ensuring the consensus of the MASs. Finally, numerical examples are given to illustrate the effectiveness of the obtained results.

Formation-containment control of multiple underactuated surface vessels with sampling communication via hierarchical sliding mode approach

The formation-containment control problem of multiple underactuated surface vessels (USVs) is investigated in this paper. A hierarchical sliding mode control strategy is proposed to solve this problem under sampling communication. The proposed control comprises two layers: a local sliding model control layer and a distributed coordination layer. The local control layer is designed to drive each USV tracking the reference trajectories, and the distributed coordination layer is proposed to generate the reference trajectories satisfying the control objective of formation-containment control. To achieve the formation-containment control of the closed-loop multiple USVs, a sufficient condition is obtained by utilizing the Lyapunov stability and eigenvalue analysis. Finally, a simulation result is provided to show the effectiveness of the proposed hierarchical sliding mode approach.

A one-layer recurrent neural network for nonsmooth pseudoconvex optimization with quasiconvex inequality and affine equality constraints

As two important types of generalized convex functions, pseudoconvex and quasiconvex functions appear in many practical optimization problems. The lack of convexity poses some difficulties in solving pseudoconvex optimization with quasiconvex constraint functions. In this paper, we propose a one-layer recurrent neural network for solving such problems. We prove that the state of the proposed neural network is convergent from the feasible region to an optimal solution of the given optimization problem. We show that the proposed neural network has several advantages over the existing neural networks for pseudoconvex optimization. Specifically, the proposed neural network is applicable to optimization problems with quasiconvex inequality constraints as well as affine equality constraints. In addition, parameter matrix inversion is avoided and some assumptions on the objective function and inequality constraints in existing results are relaxed. We demonstrate the superior performance and characteristics of the proposed neural network with simulation results in three numerical examples.

H∞ Cluster Formation Control of Networked Multiagent Systems With Stochastic Sampling

This article is concerned with the H_∞ cluster formation control of a multiagent system (MAS) with stochastic sampling in network environments. First, based on a directed communication topology with acyclic partition, agents are separated into several clusters. The agents moving in the same cluster are expected to achieve a desired formation collaboratively, while the agents in different clusters have different formation patterns. Second, the external disturbance for each agent is taken into account. The associated H_∞ cluster formation control, whose performance bound can be obtained by considering both formation information and cluster characteristics, is introduced to measure the disturbance attenuation ability. Third, by casting a stochastic sampled-data-based H_∞ cluster formation problem into an H_∞ control problem of a stochastic system, an H_∞ cluster formation criterion is derived for the MAS with external disturbance. Finally, a lower-dimensional cluster formation criterion is obtained for the disturbance-free MAS. Cluster formation performance analysis testifies the effectiveness of the proposed design methods.

Simultaneous Localization of Biobotic Insects using Inertial Data and Encounter Information

Several recent research efforts have shown that the bioelectrical stimulation of their neuro-mechanical system can control the locomotion of Madagascar hissing cockroaches (Gromphadorhina portentosa). This has opened the possibility of using these insects to explore centimeter-scale environments, such as rubble piles in urban disaster areas. We present an inertial navigation system based on machine learning modules that is capable of localizing groups of G. portentosa carrying thorax-mounted inertial measurement units. The proposed navigation system uses the agents' encounters with one another as signals of opportunity to increase tracking accuracy. Results are shown for five agents that are operating on a planar (2D) surface in controlled laboratory conditions. Trajectory reconstruction accuracy is improved by 16% when we use encounter information for the agents, and up to 27% when we add a heuristic that corrects speed estimates via a search for an optimal speed-scaling factor.

Differentially Private Consensus With Quantized Communication

This paper focuses on studying the differentially private consensus problem in multiagent networks under a quantized communication environment, where the exact real-value state is not available for transmission due to the range limitation of digital channels. We first extend the differentially private consensus model to the case of a quantized communication environment integrated with a dynamic encoding/decoding scheme and propose a differentially private communication algorithm utilizing the quantized state with a bounded quantizer instead of the exact real-value state to reach an agreement while protecting the initial or current states of the participants from information disclosure. Then, the convergence analysis of mean square consensus in the case of an unbounded quantizer is given to explain the sufficiency of the extended model and convergence conditions. To overcome the uncertainty of saturation in the case of a bounded quantizer, we also give a statistical analysis on the boundedness of quantization that the bounded quantizer with a finite number of bits can remain unsaturated with a desired high probability under certain conditions. Furthermore, we provide the statistical analysis on the convergent accuracy, which shows that the agreement value just converges to a random variable that falls in the neighboring range of the initial state average and the expectation of the agreement value is equal to the initial state average exactly. In addition, we provide the differential privacy analysis for individual agents and the whole network, and then establish the potential relationship between the dynamic encoding/decoding scheme and the differential privacy mechanism. Finally, the simulation results visually show that the proposed algorithm and the main theoretical results are effective and correct.

Event-Triggered Controller Design With Varying Gains for T-S Fuzzy Systems

This paper investigates the problem of designing event-triggered (ET) fuzzy controllers with varying gains for T-S fuzzy systems. New types of fuzzy controllers named ET fuzzy controllers with varying gains are proposed, in which the gains are automatically updated according to an online iteration algorithm at different triggering instants. By designing discontinuous Lyapunov function with time-varying Lyapunov matrix, sufficient conditions in terms of linear matrix inequalities (LMIs) are obtained to ensure the stability of the closed-loop system. It is shown that the proposed method achieves better system performances than the existing design methods with a quadratic Lyapunov function or ET fuzzy controllers with fixed gains. An example is provided to illustrate the effectiveness of the proposed method.

Event-Triggered Active MPC for Nonlinear Multiagent Systems With Packet Losses

In this paper, event-triggered active model predictive control is investigated for a nonlinear multiagent system (MAS) with packet losses. By designing event-triggered mechanisms which reduce sensing cost, event-triggered conditions are detected at certain sampling instants. The prediction horizons of all agents are selected actively through the event-triggered mechanisms. Selecting a maximal predictive horizon, a common predictive horizon of the nonlinear MAS is obtained to ensure synchronous updating. The Bernoulli distributions are applied to describe the packet losses which are dealt with by model predictive control. Finally, the effectiveness of the proposed algorithm design is verified by a numerical simulation.

Event-Based Resilient Formation Control of Multiagent Systems

This paper focuses on the time-varying formation tracking issue for nonlinear multiagent systems (MASs). Based on the explicit characterizations of frequency, duration, and magnitude properties for deception attacks, a hybrid framework is proposed for time-varying formation tracking of nonlinear MASs. To realize the desired formation tracking performance under deception attacks, the distributed edge-based event-triggered communication strategies are proposed with Zeno-freeness. The designed strategies are resilient to deception attacks under some appropriate assumptions, to realize a predefined formation and simultaneously track the convex combination of leaders' states. The designed control strategies render that we do not need to detect when the deception attack happens. Furthermore, the obtained results can be deduced to deal with consensus/synchronization problems, target enclosing problems for MASs with one/multiple leaders, where the communication is attacked by malicious attackers. An example of time-varying formation tracking of unmanned aerial vehicles is provided to show the effectiveness of the obtained results.

Distributed Event-Triggered Formation Control of Multiagent Systems via Complex-Valued Laplacian

Event-triggered formation control of multiagent systems under an undirected communication graph is investigated using complex-valued Laplacian. Both continuous-time and discrete-time models are considered. The dynamics of each agent is described by complex-valued differential or difference equations. For each agent, only the discrete-time information of its neighbors is used in the design of formation controllers and event triggers. Triggering time instants for any agent are determined by certain events that depend on the states of its neighboring agents. Continuous updating of controllers and continuous communication among neighboring agents are avoided. The obtained results show that formation can reach specific but arbitrary formation shape. Furthermore, it is shown that the closed-loop system does not exhibit the Zeno phenomenon for the continuous-time dynamics case or the Zeno-like behavior for the discrete-time dynamics case. Finally, numerical simulations for both the continuous-time and the discrete-time dynamics cases are presented to illustrate the effectiveness of the proposed distributed event-triggered control methods.

Output Consensus for Heterogeneous Linear Multiagent Systems With a Predictive Event-Triggered Mechanism

In this paper, an output consensus problem for a heterogeneous linear multiagent system with a predictive event-triggered mechanism on directed graphs is investigated. An event-triggered consensus protocol is proposed by introducing an internal reference model for each agent to handle the heterogeneity existing in the system. With the proposed mechanism, each agent predicts its internal reference model's input by employing the estimate of the internal reference model's state differences between itself and its neighbor agents. As it considers the internal reference model's inputs of agents, the system requires far fewer event-triggered times to achieve consensus, leading to a significant reduction in communication cost among agents. A necessary and sufficient condition of the output consensus for the heterogeneous linear multiagent system is put forth. Furthermore, Zeno behavior can be ruled out for each agent. Some numerical examples are given to demonstrate the effectiveness of the proposed control mechanism.

Distributed Edge-Based Event-Triggered Formation Control

This paper considers the formation control problem for general linear networked agents constrained with event-triggered communications. We propose four kinds of edge-based event-triggered protocols, each of which can be used to achieve given formation structures and eliminate the unexpected Zeno behavior. Since the whole protocols are designed according to sampled information at event instants rather than real-time information, these protocols efficiently avoid continuous communications, reduce the bandwidth need of communication, and decrease the energy consuming. The distributed static state feedback edge-based event-triggered protocol or the adaptive one is applicable for the occasions with available states. Different from the state feedback protocols, users can choose the distributed static output feedback edge-based event-triggered protocol or the adaptive one no matter whether agents' states are available or not. It is worth emphasizing that the adaptive state (or output) feedback event-triggered protocol requires no global information of the network topology and can be used in a fully distributed and scalable manner. Finally, numerical examples on formation control are offered to testify the feasibility of the proposed protocols.

Periodic Event-Triggered Suboptimal Control With Sampling Period and Performance Analysis

In this paper, the periodic event-triggered suboptimal control (PETSOC) method is developed for continuous-time linear systems. Different from event-triggered control, where the triggering condition is monitored continuously, the developed PETSOC method only verifies the triggering condition periodically at sampling instants, which further reduces computational resources. First, the control gain of the PETSOC is designed based on the algebraic Riccati equation. Subsequently, the periodic event-triggering condition is proposed for the suboptimal control method, which is only verified at sampling instants periodically. The sampling period is determined and analyzed based on the continuous form of the triggering condition. Moreover, the stability and the performance upper bound of the closed-loop system with the PETSOC are proved. Finally, the effectiveness of the developed PETSOC is validated through simulation on an unstable batch reactor.

Bipartite Fixed-Time Output Consensus of Heterogeneous Linear Multiagent Systems

In this article, the bipartite fixed-time output consensus problem of heterogeneous linear multiagent systems (MASs) is investigated. First, a distributed bipartite fixed-time observer is proposed, by which the follower can estimate the leader's state. The estimate value is the same as the leader's state in modulus but may not in sign due to the existence of antagonistic interactions between agents. Then, an adaptive bipartite fixed-time observer is further proposed. It is fully distributed without involving any global information. This adaptive bipartite fixed-time observer can estimate not only the leader's system matrix but also the leader's state. Next, distributed nonlinear control laws are developed based on two observers, respectively, such that the bipartite fixed-time output consensus of heterogeneous linear MASs can be achieved. Moreover, the upper bound of the settling time is independent of initial states of agents. Finally, the examples are given to demonstrate the results.

Bit-Rate Conditions for the Consensus of Quantized Multiagent Systems With Network-Induced Delays Based on Event Triggering

This paper is mainly concerned with bit-rate conditions to guarantee the consensus of unstable scalar multiagent systems by event-triggering strategies. In such systems, state information is quantized and transmitted among agents through digital communication networks which suffer from network-induced delays. In order to save communication resources, we implement a periodic event-triggering scheme, under which agents quantize and transmit their own states when a predefined event is triggered. By introducing a well-designed internal saturation function, the proposed strategy can place a priori bounds on the control inputs of agents and guarantee the asymptotic consensus of agents at a finite bit rate, even under network-induced delays. By extracting extra information from the receive time instants of data packets, our strategy can require less information carried by data packets and a lower bit rate to maintain the desired asymptotic consensus than some state-of-the-art time-triggered consensus strategies. Under our strategy, the obtained bit-rate conditions depend on the network-induced delays, the unstable eigenvalue of agent dynamics, and the network topology. The simulation results are provided to illustrate the effectiveness of these bit-rate conditions.

Consensus via event-triggered strategy of nonlinear multi-agent systems with Markovian switching topologies

The consensus problem via event-triggered strategy of nonlinear multi-agent systems (MASs) with Markovian switching topologies is addressed in this paper. To simplify information transmission, a distributed event-triggered mechanism (ETM) is designed. By combining the Markovian switching topologies and ETM, an appropriate consensus control protocol is presented. Considering the impact of delay on systems stability, the system model is transformed by the delay system approach. By choosing a Lyapunov-Krasovskii function and applying linear matrix inequalities technique, sufficient conditions are obtained to ensure the consensus stability with H_∞ norm bound of the MASs. A numerical simulation is given to confirm the effectiveness of the designed method.

Dynamic Intermittent Feedback Design for H∞ Containment Control on a Directed Graph

This article develops a novel distributed intermittent control framework with the ultimate goal of reducing the communication burden in containment control of multiagent systems communicating via a directed graph. Agents are assumed to be under disturbance and communicate on a directed graph. Both static and dynamic intermittent protocols are proposed. Intermittent H_∞ containment control design is considered to attenuate the effect of the disturbance and the game algebraic Riccati equation (GARE) is employed to design the coupling and feedback gains for both static and dynamic intermittent feedback. A novel scheme is then used to unify continuous, static, and dynamic intermittent containment protocols. Finally, simulation results verify the efficacy of the proposed approach.

Synchronization and Consensus in Networks of Linear Fractional-Order Multi-Agent Systems via Sampled-Data Control

This article addresses synchronization and consensus problems in networks of linear fractional-order multi-agent systems (LFOMAS) via sampled-data control. First, under very mild assumptions, the necessary and sufficient conditions are obtained for achieving synchronization in networks of LFOMAS. Second, the results of synchronization are applied to solve some consensus problems in networks of LFOMAS. In the obtained results, the coupling matrix does not have to be a Laplacian matrix, its off-diagonal elements do not have to be nonnegative, and its row-sum can be nonzero. Finally, the validity of the theoretical results is verified by three simulation examples.