Discrete-Time Distributed Potential-Based Coordination in Networked Multi-Agent Systems

Nowadays, the efficiency of multiagent systems in wireless sensor networks prompts the researchers to use these emerging mobile software packets in different simulated approaches or real-world applications. Heterogeneous and distributed wireless sensor networks could be integrated with the multiagent systems to map the real-world challenges into the artificial intelligence world. The multiagent systems have been applied from simulated approaches like object detection/tracking, healthcare, control/assistant, and security systems to real-world applications, including medical/human-care systems and unmanned aerial vehicles. Furthermore, the integration of wireless sensor networks with multiagent systems have emerged novel application, which is known as mobile robots. However, the extensive use of mobile agents in wireless sensor networks has posed different challenges for researchers, including security, resource, and timing limitation. In this work, we review recent simulated approaches and real-world applications of multiagent systems in wireless sensor networks, in which a set of common factors about the things that have been studied are extracted and compared to analyze the performance of mobile agent–based systems in the wireless sensor networks, as well. This analysis provides new research directions about multiagent systems in wireless sensor networks for interested researchers. Finally, a novel framework for dealing with the challenges of multiagent-based applications in the wireless sensor networks which have been mentioned is suggested.

Markovian jumping systems have arisen naturally in the mathematical modeling of phenomena spanning disciplines in the social sciences, natural sciences, and engineering. This kind of stochastic dynamical systems can be employed to model the dynamics when parameters are subject to random abrupt changes due to sudden environment changes, subsystem switching, system noises, executor faults, and so forth. Much attention has been given to modeling, optimization, and real applications of such stochastic dynamical systems in the literature in recent years. As the advanced control and optimization will provide a basis for the design and application of such stochastic systems, these advanced techniques would result in substantial and sustainable benefits. The accepted papers in this special issue include stochastic stability, stabilization, stochastic control optimization, system modeling and identification methods, predictive control, signal processing, robust filtering, multiagent systems, networked control systems, time-delayed systems, neural networks, the Takagi-Sugeno fuzzy systems, simulated annealing, and fault detection methods.

The paper deals with the consensus convergence analysis of discrete-time single-integrator multi-agent systems (MASs) in switching networks. Here, we suppose that the switching network is composed of strongly connected digraphs. By adopting a time-varying quadratic Lyapunov function, we give the rigorous proof that the consensus of the single-integrator MASs can be achieved by either a constant-gain protocol or a decaying-gain protocol. This Lyapunov function based consensus analysis method is different from some existed ones for unbalanced switching networks (e.g., the property of products of indecomposable and aperiodic matrices) and provides a new perspective to study the consensus problem of MASs in unbalanced switching networks with uncertainties (e.g., additive noises).

This paper investigates an observer-based control strategy for networked multi-agent systems with general linear dynamics and time-varying communication delays in a sampling setting. The communication topology is assumed to be directed fixed. Different from a traditional observer-based controller for a single system, the objects that need to be observed here are the state differences between an agent and its neighbours. Using a system transformation method, some equivalent conditions concerning the consensus of multi-agent systems are established. Moreover, we prove that both the connection weights and the communication topology play an important role in the study of multi-agent systems, and establish a linear matrix inequality based observer design method.

Control of time-delay systems represents one important class of control problems. A lot of work has been done in recent years. Much of the research work has been focused on the stability analysis and stabilization of time-delay systems based on the Lyapunov functional and linear matrix inequality (LMI) approaches. While the LMI approach does provide an e±cient tool, the results are mostly only su±cient and only numerical solutions are available. Our research aims to present analytical solutions by some algebraic approaches, which can provide an insightful understanding of control problem of time-delay systems. On the other hand, there has also been significant recent interest in networked multi-agent system (MAS) where unavoidably there exist delays in information acquisition as well as information exchange between agents in addition to possible input/state delays in each agent. This motivates the study of MASs with input and communication delays. The thesis is divided into two parts: Part I is focused on optimal control of single agent systems with multiple input delays; Part II is on distributed consensus control of MAS with communication and input delays. More specifically, in Part I, we shall study the linear quadratic regulation (LQR) and tracking for a single agent with linear dynamics. We first focus on LQR for linear discrete-time systems with multiple delays in a single input channel. By introducing a backward stochastic system, we establish a relationship between the LQR problem for the original system and optimal estimation for a backward stochastic system which can be solved by using projection approach. The explicit optimal controllers are developed for both finite time and infinite time horizon cases. A parallel result for linear continuous-time systems is also established, where the optimal feedback gain is given in terms of the solution of Riccati-type partial differential equations (PDEs).

This paper, provides a new control scheme, to solve consensus problem in multi agent networked system. This controller design is based on adaptive neuro-fuzzy inference system. Because of changing network topology in wireless sensory networks and existence of noise in harsh industrial environments, it is important to use robust controller to tolerate these conditions. Fast convergence and less sensitivity to model uncertainties are the main benefits of our controller. Simulation results shows flexibility of this controller can decrease effects of noise and disturbance and improve the performance of networked system.

For a discrete-time linear system that has an open-loop pole outside the unit circle and is subject to actuator saturation, global asymptotic stabilization cannot be achieved. As a consequence, a discrete-time multi-agent system containing such an actuator saturating agent can only reach regional consensus, that is, the consensus can be achieved only if the initial state of each agent resides in a region of the state space. The region of all these initial states is referred to as the domain of consensus. In general, the exact characterization of the domain of consensus is impossible. In this paper, we consider the problem of estimating the domain of consensus of a discrete-time multi-agent system with an agent subject to actuator saturation. We establish a set of conditions under which an ellipsoidal set can be used as an estimate of the domain of consensus. Based on these conditions, an LMI-based optimization problem is formulated for obtaining the largest ellipsoidal estimate of the domain of consensus. Simulation results illustrate the effectiveness of the proposed approach and show the relationship between the size of the largest ellipsoidal estimate and the communication topology of the multi-agent system.

For constructing a decentralized optimal control strategy for networked multi-agent systems that can accomplish consensus in a leaderless structure, we employ the inverse optimal control approach which circumvents the task of solving a HJB equation by introducing a cooperative control Lyapunov function. The decentralized controller we obtain is more flexible than the controller u = Lx in fixed network, because it not only has the advantages to handle systems of high relative degree, but it can also improve the performance of networked multi-agent systems by adjusting a positive definite and symmetrical matrix. On the other hand, the decentralized controller is optimal with respect to a meaningful cost functional which is useful to quantify and improve the performance of networked multi-agent systems. Finally, examples are presented to illustrate both the design process and performance of cooperative controls.

Effective communication is crucial for the performance and collaboration within cooperative networked multi-agent systems. However, existing literature lacks comprehensive solutions for dynamically monitoring and adjusting communication topologies to balance connectivity and energy efficiency. This study addresses this gap by proposing a distributed approach for estimating and controlling system connectivity over time. We introduce a modified consensus protocol where agents exchange local assessments of communication link quality, enabling the estimation of a global weighted adjacency matrix without requiring centralized information. The system's connectivity is measured using the second smallest eigenvalue of the communication graph Laplacian, commonly referred to as algebraic connectivity. Additionally, we enhance the consensus protocol with an adaptive mechanism to expedite convergence, irrespective of system size or structure. Furthermore, we present an analytical method for connectivity control based on the Fiedler vector approximation, facilitating the addition or removal of communication links. This method adjusts control parameters to accommodate minor variations in link quality while reconfiguring the network in response to significant changes. Notably, it identifies and eliminates energy-consuming yet non-contributory links, improving long-term connectivity efficiency. Simulation experiments across diverse scenarios and the number of agents validate the efficacy of our proposed algebraic connectivity estimation and tracking strategy. Results demonstrate robust connectivity maintenance against external disturbances and agent failures, underscoring the practical utility of our approach for real-world multi-agent systems.

Observer-based HOFA predictive cooperative control for networked multi-agent systems under time-variant communication constraints

In this paper, we investigate two classes of consensus protocols for networked multi-agent systems: linear time-invariant (LTI) systems and linear time-delay systems. Based on the topology of multi-agent systems, the first-order integrator model is developed. The digraph (directed graph) is employed to show the topology of networked systems, and then a consensus convergence criterion is established. For LTI systems, we prove that their consensus value will converge globally asymptotically to the convex hull of initial states. By solving a set of linear equations, we get the convex combinations of equilibria, and we obtain the convex value of continuous system. If the topology is fixed and time-invariant, the consensus value of the linear time-delay system is also the convex hull of the initial states and is identical to the LTI system. Finally, a network of six agents is presented to show the effectiveness of the results of this paper.

Bipartite hybrid formation tracking control for heterogeneous multi-agent systems in multi-group cooperative-competitive networks

Networked multiagent systems consist of interacting agents that locally exchange information. Since these systems do not in general have a centralized architecture to monitor the activity of each agent, resilient distributed control system design for networked multiagent systems is essential in providing high system performance, reliability, and operation in the presence of system uncertainties. An important set of classes of such system uncertainties that can significantly deteriorate the achievable closed-loop system performance is sensor, actuator, and plant uncertainties, with sensor and actuator uncertainty possibly arising due to adversarial sensor and actuator compromise. This paper presents a novel distributed robust adaptive control architecture for leader-follower networked multiagent systems with undirected communication graph topologies to mitigate the effects of time-varying sensor and actuator attacks, and timeinvariant plant parameter uncertainty. The proposed adaptive control architecture guarantees uniform ultimate boundedness of the controlled closed-loop system. Finally, two numerical examples are provided to illustrate the efficacy of the proposed distributed robust adaptive control architecture.

Based on the algebraic graph theory, the networked multi-agent continuous systems are investigated. Firstly, the digraph (directed graph) represents the topology of a networked system, and then a consensus convergence criterion of system is proposed. Secondly, the issue of stability of multi-agent systems and the consensus convergence problem of information states are all analysed. Furthermore, the consensus equilibrium point of system is proved to be global and asymptotically reach the convex combination of initial states. Finally, two examples are taken to show the effectiveness of the results obtained in this paper.

Ubiquitous computing and The Internet-of-Things (IoT) grow rapidly in today's life and evolving to Self-organizing systems (SoS). A unified and scalable information processing and communication methodology is required. In this work, mobile agents are used to merge the IoT with Mobile and Cloud environments seamless. A portable and scalable Agent Processing Platform (APP) provides an enabling technology that is central for the deployment of Multi-Agent Systems (MAS) in strong heterogeneous networks including the Internet. A large-scale use-case deploying Multi-agent systems in a distributed heterogeneous seismic sensor and geodetic network is used to demonstrate the suitability of the MAS and platform approach. The MAS is used for earthquake monitoring based on a new incremental distributed learning algorithm applied to seismic station data, which can be extended by ubiquitous sensing devices like smart phones. Different (mobile) agents perform sensor sensing, aggregation, local learning and prediction, global voting and decision making, and the application.