Cooperative Tracking Control of Multiagent Systems: A Heterogeneous Coupling Network and Intermittent Communication Framework

Fully distributed dynamic event-triggered formation-containment control for networked unmanned surface vehicles with intermittent wireless network communications

Favorable neighboring interactions and economical transmission costs are the foundations of formation-containment control (FCC), while the complex marine environments hamper its expansion on networked unmanned surface vehicles (USVs). In this context, this paper investigates an intermittent dynamic event-triggered control scheme for USVs experiencing communication interruptions to achieve FCC. Specifically, the control architecture consists of two synchronously working sub-layers. In the first layer, an intermittent communications-based formation tracking controller is initially developed to endow USVs with higher endurance against communication interruptions, such that the leader USVs can form a desired formation pattern while following a virtual leader. Meanwhile, a dynamic event-triggered mechanism (DETM) is incorporated into the intermittent controller to reduce the update frequency of control signals with computable minimum inter-event time (MIET). Similarly, an intermittent DETM-based controller is proposed for followers to achieve containment missions in the second layer. Moreover, the global information is unnecessary with time-varying control gains. Finally, the simulations are provided to verify the effectiveness and superiority of the proposed control scheme.

Distributed control for a class of nonlinear systems based on distributed high-gain observer

This paper concerns the problem of designing distributed control law for a class nonlinear system in which the measurement outputs of the system are distributed in different subsystems. It leads to a challenge that the states of the original systems cannot be completely reconstructed by any single subsystem. In order to solve this problem, distributed state observers and the distributed observer-based distributed control problem emerges as the times require. However, the distributed observers problem of the nonlinear systems is rarely studied, and the distributed control law formed by distributed nonlinear observers has hardly ever been studied up to now. To this end, this paper develops the distributed high-gain observers for a class of nonlinear systems. Unlike the previous several results, our study has the ability to deal with model uncertainty, and devotes itself to overcoming the problem that the separation principle is not tenable. In addition, based on the state estimate generated by the designed distributed observer, an output feedback control law formed by applying the state estimate is developed. Furthermore, a class of sufficient conditions is proved for guaranteeing the error dynamics of the distributed observer and the state trajectory of the closed-loop system to enter an arbitrary small invariant set around the origin. Finally, the simulation results verify the effectiveness of the proposed method.

Distributed formation for interconnected networks with minimum energy restriction and interaction silence

The minimum-energy formation strategy for interconnected networks with distributed formation protocols is persented, where the impacts of the total energy restriction and the interaction silence are analyzed, respectively. The critical feature of this article is that the distributed formation and the minimum-energy restriction are realized simultaneously, and the total energy restriction is minimum in the sense of the linear matrix inequality. However, the guaranteed-cost formation strategy and the limited-budget formation strategy cannot guarantee that the energy restriction is minimum. Firstly, sufficient conditions for minimum-energy-restriction formation without the interaction silence are proposed, which can be solved by a specific optimization approach in terms of the linear matrix inequality, and the formation whole motion trajectory is determined, which is closely related to the average of the initial states of all agents and formation control vectors. Then, minimum-energy-restriction formation criteria for interconnected systems with the interaction silence are proposed by introducing two inhibition parameters and the interaction silence rate. Finally, two simulation examples are performed to illustrate the effectiveness of theoretical analyses.

Distributed Observer Design for Linear Systems to Achieve Omniscience Asymptotically Under Jointly Connected Switching Networks

The distributed observer problem is motivated by the case where the output information of the system is decentralized in different subsystems. In this scene, all the subsystems form an observer network, and each of them has access to only a part of output information and the information exchanged via the given communication networks. Due to the limitation of communication conditions among subsystems, the communication network is often time varying and disconnected. However, the existing research about the aforementioned scene is still not enough to solve this problem. To this end, this article is concerned with the challenge of the distributed observer design for linear systems under time-variant disconnected communication networks. The design method is successfully established by fixing both completely decentralized output information and incompletely decentralized output information into account. Our work overcomes the limitation of the existing results that the distributed observer can only reconstruct the full states of the underlying systems by means of fast switching. In the case of completely decentralized output information, a group of sufficient conditions is put forward for the system matrix, and it is proved that the asymptotical omniscience of the distributed observer could be achieved as long as anyone of the developed conditions is satisfied. Furthermore, unlike similar problems in multiagent systems, the systems that can meet the proposed conditions are not only stable and marginally stable systems but also some unstable systems. As for the case where the output information is not completely decentralized, the results show with the observable decomposition and states reorganization technology that the distributed observer could achieve omniscience asymptotically without any constraints on the system matrix. The validity of the proposed design method is emphasized in two numerical simulations.

Coordinated control of quasilinear multiagent systems via output feedback predictive control

This study considers the coordinated control for quasilinear multiagent systems (QMASs). An output feedback predictive control (OFPC) strategy is given to implement both coordination and simultaneous stability and output consensus (SSOC). In the OFPC strategy, a cost function aiming at coordination relationship is minimized by predictive control thus coordination among QMASs is implemented. Further discussion derives a criterion to maintain the closed-loop QMASs realize the SSOC. Finally, two examples are proposed to richly illustrate the availability of the OPFC strategy.

Effect of local and global information on the dynamical interplay between awareness and epidemic transmission in multiplex networks

Recent few years have witnessed a growing interest in exploring the dynamical interplay between awareness and epidemic transmission within the framework of multiplex networks. However, both local and global information have significant impacts on individual awareness and behavior, which have not been adequately characterized in the existing works. To this end, we propose a local and global information controlled spreading model to explore the dynamics of two spreading processes. In the upper layer, we construct a threshold model to describe the awareness diffusion process and introduce local and global awareness information as variables into an individual awareness ratio. In the lower layer, we adopt the classical susceptible-infected-susceptible model to represent the epidemic propagation process and introduce local and global epidemic information into individual precaution degree to reflect individual heterogeneity. Using the microscopic Markov chain approach, we theoretically derive the threshold for epidemic outbreaks. Our findings suggest that the local and global information can motivate individuals to increase self-protection awareness and take more precaution measures, thereby reducing disease infection probability and suppressing the spread of epidemics.

Intelligent Knowledge Distribution: Constrained-Action POMDPs for Resource-Aware Multiagent Communication

This article addresses a fundamental question of multiagent knowledge distribution: what information should be sent to whom and when with the limited resources available to each agent? Communication requirements for multiagent systems can be rather high when an accurate picture of the environment and the state of other agents must be maintained. To reduce the impact of multiagent coordination on networked systems, for example, power and bandwidth, this article introduces two concepts for the partially observable Markov decision processes (POMDPs): 1) action-based constraints that yield constrained-action POMDPs (CA-POMDPs) and 2) soft probabilistic constraint satisfaction for the resulting infinite-horizon controllers. To enable constraint analysis over an infinite horizon, an unconstrained policy is first represented as a finite-state controller (FSC) and optimized with policy iteration. The FSC representation then allows for a combination of the Markov chain Monte Carlo and discrete optimization to improve the probabilistic constraint satisfaction of the controller while minimizing the impact on the value function. Within the CA-POMDP framework, we then propose intelligent knowledge distribution (IKD) which yields per-agent policies for distributing knowledge between agents subject to interaction constraints. Finally, the CA-POMDP and IKD concepts are validated using an asset tracking problem where multiple unmanned aerial vehicles (UAVs) with heterogeneous sensors collaborate to localize a ground asset to assist in avoiding unseen obstacles in a disaster area. The IKD model was able to maintain asset tracking through multiagent communications while only violating soft power and bandwidth constraints 3% of the time, while greedy and naive approaches violated constraints more than 60% of the time.

Epidemic Propagation With Positive and Negative Preventive Information in Multiplex Networks

We propose a novel epidemic model based on two-layered multiplex networks to explore the influence of positive and negative preventive information on epidemic propagation. In the model, one layer represents a social network with positive and negative preventive information spreading competitively, while the other one denotes the physical contact network with epidemic propagation. The individuals who are aware of positive prevention will take more effective measures to avoid being infected than those who are aware of negative prevention. Taking the microscopic Markov chain (MMC) approach, we analytically derive the expression of the epidemic threshold for the proposed epidemic model, which indicates that the diffusion of positive and negative prevention information, as well as the topology of the physical contact network have a significant impact on the epidemic threshold. By comparing the results obtained with MMC and those with the Monte Carlo (MC) simulations, it is found that they are in good agreement, but MMC can well describe the dynamics of the proposed model. Meanwhile, through extensive simulations, we demonstrate the impact of positive and negative preventive information on the epidemic threshold, as well as the prevalence of infectious diseases. We also find that the epidemic prevalence and the epidemic outbreaks can be suppressed by the diffusion of positive preventive information and be promoted by the diffusion of negative preventive information.

Prescribed Performance Cooperative Control for Multiagent Systems With Input Quantization

This paper studies the quantized cooperative control problem for multiagent systems with unknown gains in the prescribed performance. Different from the finite-time control, a speed function is designed to realize that the tracking errors converge to a prescribed compact set in a given finite time for multiagent systems. Meanwhile, we consider the problem of unknown gains and input quantization, which can be addressed by using a lemma and Nussbaum function in cooperative control. Moreover, the fuzzy logic systems are proposed to approximate the nonlinear function defined on a compact set. A distributed controller and adaptive laws are constructed based on the Lyapunov stability theory and backstepping method. Finally, the effectiveness of the proposed approach is illustrated by some numerical simulation results.

Edge-Based Finite-Time Protocol Analysis With Final Consensus Value and Settling Time Estimations

The objective of this paper is to design the protocols with a final consensus value and settling time estimations for finite-time consensus of multiagent systems. A couple of new edge-based protocols are developed for multiple second-order nonlinear agents under bounded or Lipschitz-type nonlinear functions, respectively. The final consensus value of the multiagent system is obtained as an average expression. Further, to obtain the estimation of the finite settling time, a special Lyapunov function is constructed. Through the construction processes, both the final consensus value and the settling time are obtained. Finally, as applications, a finite-time formation controller based on the first protocol is designed for multiple mini-spacecraft, verified by simulations.

Finite-Time Distributed Average Tracking for Second-Order Nonlinear Systems

This paper studies the distributed average tracking (DAT) problem for multiple reference signals described by the second-order nonlinear dynamical systems. Leveraging the state-dependent gain design and the adaptive control approaches, a couple of DAT algorithms are developed in this paper, which are named finite-time and adaptive-gain DAT algorithms. Based on the finite-time one, the states of the physical agents in this paper can track the average of the time-varying reference signals within a finite settling time. Furthermore, the finite settling time is also estimated by considering a well-designed Lyapunov function in this paper. Compared with asymptotical DAT algorithms, the proposed finite-time algorithm not only solve finite-time DAT problems but also ensure states of physical agents to achieve an accurate average of the multiple signals. Then, an adaptive-gain DAT algorithm is designed. Based on the adaptive-gain one, the DAT problem is solved without global information. Thus, it is fully distributed. Finally, numerical simulations show the effectiveness of the theoretical results.

Spatial Association and Effect Evaluation of CO2 Emission in the Chengdu-Chongqing Urban Agglomeration: Quantitative Evidence from Social Network Analysis

Urban agglomeration, an established urban spatial pattern, contributes to the spatial association and dependence of city-level CO2 emission distribution while boosting regional economic growth. Exploring this spatial association and dependence is conducive to the implementation of effective and coordinated policies for regional level CO2 reduction. This study calculated CO2 emissions from 2005–2016 in the Chengdu-Chongqing urban agglomeration with the IPAT model, and empirically explored the spatial structure pattern and association effect of CO2 across the area leveraged by the social network analysis. The findings revealed the following: (1) The spatial structure of CO2 emission in the area is a complex network pattern, and in the sample period, the CO2 emission association relations increased steadily and the network stabilization remains strengthened; (2) the centrality of the cities in this area can be categorized into three classes: Chengdu and Chongqing are defined as the first class, the second class covers Deyang, Mianyang, Yibin, and Nanchong, and the third class includes Zigong, Suining, Meishan, and Guangan—the number of cities in this class is on the rise; (3) the network is divided into four subgroups: the area around Chengdu, south Sichuan, northeast Sichuan, and west Chongqing where the spillover effect of CO2 is greatest; and (4) the higher density of the global network of CO2 emission considerably reduces regional emission intensity and narrows the differences among regions. Individual networks with higher centrality are also found to have lower emission intensity.

How Environmental Protection Motivation Influences on Residents’ Recycled Water Reuse Behaviors: A Case Study in Xi’an City

Pro-environmental behaviors related to reclaimed water reuse are regarded as important motivations for both environmental protection and the use of reclaimed water, and these motivations could affect the citizens’ decision whether they will accept reclaimed water reuse. A hypothesis model was developed as the NAM (Norm Activation Model) has changed, and this hypothesis model was used to explore the factors that affect the citizen’s decision about the reclaimed water reuse, and obtain a better understanding of the mechanism of urban citizens in environmental protection and the related outcomes. First, 584 samples were used to verify the reliability and validity of data, and AMOS21.0 was used to test the goodness-of-fit between the sample data and the hypothesis model. Based on this, the applicability of the improved NAM was verified through the study of recycled water reuse. The hypothesis model was used to analyze its direct influences, showing that environmental motivation has positive influences on the citizens’ acceptance toward recycled water reuse. Besides, Bootstrap method was used to verify the mediation effect, proving that awareness of consequences regarding environmental pollution caused by human activities and ascription of responsibility could strengthen the citizens’ motivation to protect the environment.