Design Of Multi-agent Systems Research Articles

Robust Consensus Design of Uncertain Multiagent Systems With Bounded Gains and Incremental Nonlinear Interactions

Robust Consensus Design of Uncertain Multiagent Systems With Bounded Gains and Incremental Nonlinear Interactions

A Two-Stage Hybrid Stochastic–Robust Coordination of Combined Energy Management and Self-Healing in Smart Distribution Networks Incorporating Multiple Microgrids

This paper presents a two-stage hybrid stochastic–robust coordination of energy management and self-healing in smart distribution networks with multiple microgrids. A multi-agent systems approach is first used for coupling energy management and self-healing strategies of microgrids, based on expert system rules. The second stage problem, a framework similar to that of the first stage, is then established for the smart distribution networks. Then, hybrid stochastic–robust optimization is used to model the uncertainties of demand, energy price, power generation of renewable energy sources, demand of electric vehicles, and accessibility of zone agents. Further, the grey wolf algorithm is used to solve the formulated optimization problem and achieve an optimal and reliable solution. The proposal is validated on a 69-bus distribution network consisting of three microgrids. The results validate that the proposal minimizes microgrids’ utilization indices, such as energy costs, energy losses, and network voltage drops, while simultaneously managing a flexible distribution network. It is also verified that the proposed multi-agent system design provides a high-speed and optimized self-healing solution for the network.

Adaptive distributed fault estimation observer design for nonlinear time-delay multi-agent systems with directed graphs

This paper focuses the development of an adaptive observer-based distributed fault estimation observer (DFEO) for multi-agent nonlinear time-delay systems under a directed communication topology. The process involves constructing a fault estimation observer for each agent based on their relative output estimation errors. An adaptive DFEO design is proposed for multi-agent systems with a directed communication topology. Furthermore, investigating the effect of faulty agents on the criteria of fault detection on healthy agents. The proposed design is represented with Linear Matrix Inequalities (LMIs) to establish a systematic design procedure. The study also includes simulation results to demonstrate the effectiveness of the proposed design techniques.

Consensus algorithms for double-integrator dynamics with different velocity and actuator saturation constraints

This paper considers consensus strategy design for double-integrator multi-agent systems with matched disturbances under a directed graph. Specifically, we consider the case where both the control inputs and velocities of the agents are subject to different and asymmetric constraints. A novel distributed algorithm exploiting saturation functions is proposed to achieve asymptotic consensus without violating the predefined velocity and actuator saturation constraints. Subsequently, we extend the obtained results to event-triggered communication, where agent position broadcasting occurs only when specific triggering conditions are met. We rigorously prove that there exists a positive minimum inter-event time to rule out Zeno behavior. Finally, the simulation results confirm the theoretical findings and illustrate the achievable performance.

Optimal synchronization with [formula omitted]-gain performance: An adaptive dynamic programming approach

This paper studies an optimal synchronous control protocol design for nonlinear multi-agent systems under partially known dynamics and uncertain external disturbance. Under some mild assumptions, Hamilton–Jacobi–Isaacs equation is derived by the performance index function and system dynamics, which serves as an equivalent formulation. Distributed policy iteration adaptive dynamic programming is developed to obtain the numerical solution to the Hamilton–Jacobi–Isaacs equation. Three theoretical results are given about the proposed algorithm. First, the iterative variables is proved to converge to the solution to Hamilton–Jacobi–Isaacs equation. Second, the L2-gain performance of the closed loop system is achieved. As a special case, the origin of the nominal system is asymptotically stable. Third, the obtained control protocol constitutes an Nash equilibrium solution. Neural network-based implementation is designed following the main results. Finally, two numerical examples are provided to verify the effectiveness of the proposed method.

Event-triggered based adjustable containment control scheme design for multi-agent systems

The traditional containment control problem uses a mapping method with a fixed communication topology to generate a set of reference signals for followers, which may lead to a situation where the position of the reference signal cannot be adjusted and causes collisions. Therefore, based on backstepping theory, we propose a novel containment control scheme to address this problem, where the generated reference signals can be adjusted arbitrarily. In particular, it is important to point out that an estimator is designed to guide the follower if the reference signal is not available, so that the collision problem is avoided. In addition, the computational complexity of the controller is also reduced and the communication resources are saved by introducing the command filters and event-triggered mechanism. It is proved that the tracking errors of the closed multi-agent systems are uniformly and ultimately bounded through Lyapunov theory. Finally, experimental simulations verify that the proposed control scheme is reasonable and effective. Furthermore, the proposed scheme provides a more flexible solution for containment control studies, especially for collision avoidance research of multi-agents.

Design of consensus and cluster consensus controller for first-order nonlinear multi-agent systems based on subgroup structure

It is well known that nonlinear multi-agent systems (MASs) with a directed topology can reach a consensus if the global coupling coefficient is strong enough. In this paper, we propose a novel distributed protocol based on the subgroup structure. Our results demonstrate that the MAS design can follow a part-to-whole approach, starting with subgroups and gradually building up to a more complex system. We firstly consider two cases for the MASs: one with a single leader and one with a leader group, where agents within the leader group can communicate with each other, but cannot receive the information of other group. We show that followers can track the leader or group leaders if intra-coupling within the subgroup and information strength received by the root node are above a certain threshold. Next, we investigate cluster consensus and practical cluster consensus for the Laplacian matrix with unequal row sum, equal row sum, and mixing of partitioned matrices. Our results reveal that row sum and community structure of the partitioned matrix are crucial in determining the final state of each agent. Our proposed control protocol is highly flexible and relies only on the cluster structure and information received by the root node, rather than global information. Moreover, our method is not limited to tracking problems, cooperative control, or multi-objective control, and can accommodate new group agents joining the system, making it highly adaptable. We provide illustrative examples to demonstrate the effectiveness of our approach.

Decentralized leader-following consensus control design for discrete-time multi-agent systems with switching topology

The problem of consensus control of linear discrete-time multi-agent systems (MASs) with switching topology is considered in the presence of a leader. The goal of consensus control is to bring the states of all agents to the leader state while providing stability for local agents, as well as the MAS as a whole. In contrast to the traditional approach, which uses the concept of an extended dynamic multi-agent system model and communication topology graph Laplacian, this paper proposes a decomposition approach, which provides a separate design of local controllers. The control law is chosen in the form of distributed feedback with discrete PID controllers. The problem of local controllers’ design is reduced to a set of semidefinite programming problems using the method of invariant ellipsoids. Sufficient conditions for agents’ stabilization and global consensus condition fulfillment are obtained using the linear matrix inequality technique. The availability of information about a finite set of possible configurations between agents allows us to design local controllers offline at the design stage. A numerical example demonstrates the effectiveness of the proposed approach.

Robust [formula omitted] time-varying formation control of singular multiagent systems under jointly connected topologies

This study investigates time-varying formation design and analysis problems for the singular multiagent systems (SMASs) with external disturbances and jointly connected topologies. Firstly, the novel nonlinear distributed formation protocol is formulated and the finite-time reachability of sliding mode surfaces is proved. Another improvement emphasizes that a common matrix can be extracted from the decomposition of all switching Laplacian matrices, which simplifies the formation control problem to admissible analysis. Secondly, by using the feedback impulse elimination and Cauchy convergence criterion methods, the solvability of the time-varying formation control is obtained. Thirdly, the external disturbances are well suppressed by the robust H∞ stability analysis. Finally, to verify the validity of the proposed theory, the simulation examples of hexagonal time-varying formation are provided.

Distribution of Roots of Quasi-Polynomials of Neutral Type and Its Application—Part II: Consensus Protocol Design of Multi-Agent Systems Using Delayed State Information

Distribution of Roots of Quasi-Polynomials of Neutral Type and Its Application—Part II: Consensus Protocol Design of Multi-Agent Systems Using Delayed State Information

Domain estimation and coupled controller design for high-dimensional nonlinear multi-agent systems

This paper investigates the consensus problem of a class of high-dimensional nonlinear multi-agent systems. The agents are homogeneous and have coupled interactions with their nearby agents. Firstly, we study the consensus of this class of nonlinear multi-agent systems and present some consensus conditions based on the linearization technique. By transforming the consensus problem into a stability problem, we show that there exists a domain such that if all pairs of connected agents stay in the domain, the system will achieve consensus under the consensus conditions. Then, we propose a method to estimate the consensus domain since the accurate description of the domain is still challenging. Moreover, a method of coupled controller design is presented to make the multi-agent systems achieve consensus. At last, two numerical examples and a practical example are used to show the effectiveness of the results proposed in this paper.

An explanation-oriented inquiry dialogue game for expert collaborative recommendations

This work presents a requirement analysis for collaborative dialogues among medical experts and an inquiry dialogue game based on this analysis for incorporating explainability into multiagent system design. The game allows experts with different knowledge bases to collaboratively make recommendations while generating rich traces of the reasoning process through combining explanation-based illocutionary forces in an inquiry dialogue. The dialogue game was implemented as a prototype web-application and evaluated against the specification through a formative user study. The user study confirms that the dialogue game meets the needs for collaboration among medical experts. It also provides insights on the real-life value of dialogue-based communication tools for the medical community.

Game-Based Backstepping Design for Strict-Feedback Nonlinear Multi-Agent Systems Based on Reinforcement Learning.

In this article, the game-based backstepping control method is proposed for the high-order nonlinear multi-agent system with unknown dynamic and input saturation. Reinforcement learning (RL) is employed to get the saddle point solution of the tracking game between each agent and the reference signal for achieving robust control. Specifically, the approximate optimal solution of the established Hamilton-Jacobi-Isaacs (HJI) equation is obtained by policy iteration for each subsystem, and the single network adaptive critic (SNAC) architecture is used to reduce the computational burden. In addition, based on the separation operation of the error term from the derivative of the value function, we achieve the different proportions of the two agents in the game to realize the regulation of the final equilibrium point. Different from the general use of the neural network for system identification, the unknown nonlinear dynamic term is approximated based on the state difference obtained by the command filter. Furthermore, a sufficient condition is established to guarantee that the whole system and each subsystem included are uniformly ultimately bounded. Finally, simulation results are given to show the effectiveness of the proposed method.

Observer-Based Adaptive Consensus Protocol Design for Multi-Agent Systems with One-Sided Lipschitz Nonlinearity

This paper considers the observer-based leader–follower consensus problem of multi-agent systems with one-sided Lipschitz conditions and quadratic inner-boundedness nonlinearity. Based on the relative outputs of neighboring agents, an adaptive protocol over a directed graph is designed via assigning a time-varying coupling weight to each node. Compared with the existing protocols, the proposed protocol can not only be utilized in the unidirectional flow of information, but it does not require any global graph information for its design. It has been shown that the established criteria can not only make the observer error tendency zero, but one can also achieve the leader–follower consensus of such nonlinear multi-agent systems. Furthermore, the gains of observer and protocol parameters can be constructed by solving the linear matrix inequalities (LMIs), which are conveniently checked. The results are illustrated by numerical simulations.

Improved prescribed performance fuzzy control for nonlinear multiagent systems with actuator faults and dual input saturations

This paper conducts the distributed adaptive prescribed performance fuzzy control design for nonlinear multiagent systems (MASs) subjected to actuator faults and dual input saturations. A unified smooth characterizing function is constructed to approximate a practical input saturations model. Then, with the aid of the mean-value theorem, Nussbaum-type function and boundary estimation method, the distributed saturated fault-tolerant controllers are devised to deal with actuator faults and dual input saturations. In order to achieve excellent tracking performance of followers, an improved prescribed performance control method with a tight constraint region is proposed. Different from most existing control methods, the proposed method not only ensures that the tracking error rapidly converges to the specified area within a predetermined time, but also achieves transient performance without obvious overshoot. Stability analysis proves that the consensus tracking error converges to an arbitrary small specified area within a preset time and all signals of the overall controlled systems are semi-globally uniformly ultimately bounded (SUUB). Eventually, the comprehensive simulation results validate the feasibility and superiority of the control strategy.

The design and implementation of intelligent ubiquitous learning multi-agent context-aware system

Advances in technology in education have had a profound impact on Human-Computer Interaction. In this article, we explore how emerging technologies can make learning more affordable and accessible. This study aims to present a new approach to ubiquitous learning, which uses a multi-agent system to facilitate the learning process. To develop the multi-agent system, we used JADE-LEAP (Lightweight and Extensible Agent Platform). The application developed adapts several parameters according to the dynamic situation of the learner's contextual information. We used contextual technologies such as GPS sensors and 4G networks to obtain the dynamic situation. This application provides a set of functionalities that can be used by learners via devices anywhere and anytime and supports formal learning. Finally, to verify the usefulness of this application, we conducted a case study with different scenarios. Computer science students from various Algerian universities took part in the study. Keywords: Artificial intelligence; educational technologies; personalized education; smart learning; ubiquitous computing.

Observer-based leader-following consensus for positive multi-agent systems over time-varying graphs

This paper addresses the leader-following consensus problem for discrete-time positive multi-agent systems over time-varying graphs. We assume that the followers may have mutually different positive dynamics that can also be different from the leader. Compared with most existing positive consensus works for homogeneous multi-agent systems, the formulated problem is more general and challenging due to the interplay between the positivity requirement and high-order heterogeneous dynamics. To solve the problem, we present an extended version of existing observer-based design for positive multi-agent systems. By virtue of the common quadratic Lyapunov function technique, we show the followers will maintain their state variables in the positive orthant and finally achieve an output consensus specified by the leader. A numerical example is used to verify the efficacy of our algorithms.

A REFERENCE ARCHITECTURE OF HUMAN CYBER-PHYSICAL SYSTEMS – PART III: SEMANTIC FOUNDATIONS

The design and analysis of multi-agent human cyber-physical systems in safety-critical or industry-critical domains calls for an adequate semantic foundation capable of exhaustively and rigorously describing all emergent effects in the joint dynamic behavior of the agents that are relevant to their safety and well-behavior. We present such a semantic foundation. This framework extends beyond previous approaches by extending the agent-local dynamic state beyond state components under direct control of the agent and belief about other agents (as previously suggested for understanding cooperative as well as rational behavior) to agent-local evidence and belief about the overall cooperative, competitive, or coopetitive game structure. We argue that this extension is necessary for rigorously analyzing systems of human cyber-physical systems because humans are known to employ cognitive replacement models of system dynamics that are both non-stationary and potentially incongruent. These replacement models induce visible and potentially harmful effects on their joint emergent behavior and the interaction with cyber-physical system components.

Application-Oriented Homogeneous Control Protocol Design for Multi-Agent Systems Under Input Constraints

An application-oriented homogeneous control protocol is proposed to solve the consensus problem of multi-agent system (MAS) modeled by higher-order integrator. This nonlinear control protocol is able to homogenize the linear system with a special degree called homogeneity degree. This homogeneous control protocol ensures asymptotically/finite-time stable multi-agent systems (MASs) (or fixed-time attractive to compact sets containing the origin) by selecting different homogeneity degrees. If the linear control protocol for each agent is provided, the proposed nonlinear homogeneous control protocol in this paper can be easily implemented without requiring any tuning of the control parameters. A bounded homogeneous control protocol, which is a special form of the controller proposed, is also introduced to address the same problem with input constraints. Finally, numerical simulations are conducted to demonstrate the effectiveness of the proposed approach.

Multi-agent Intelligent System Design for Telemedicine Applications to Develop Health Care Systems of Developing Countries

Various factors controlling health care in any country, but still there is a weakness in the infrastructure of health care systems in some developing countries, including many Arab countries, due to the shortage of medical specialists personnel, global economic crisis, and wars and disasters that struck many countries. A crying need for a tele-medical care becomes a necessity, and in order to overcoming this weakness we have proposed a multi-agent intelligent Telemedicine system in this research. The proposed system can help solve the shortage of specialists urgently and at lower costs. Furthermore, it can save the efforts of the patients' expensive travel for treatment abroad. Moreover, it can avoid infections from social closeness in times of disasters and pandemics.
 Nowadays Telemedicine systems have gradually become a popular medical model which have always attracted much attention due to the continuous development of the multi-agents' structure and capabilities of the artificial intelligence systems. So we will focus in the prototype design of our system on the algorithms, techniques and devices that support the multi-agents' structure and AI technologies. The Multi-agent approach happens to be a suitable structure for the development of Telemedicine systems. Worth mentioning it can solve problems that are difficult or impossible for an individual agent or a monolithic system, and the AI capabilities will minimize human errors and improve health care outcomes.
 We will start by a state-of-the-art literature survey as conducted on the E-health system architecture, AI capabilities, and multi-agent platform, and then we will illustrate prototype design of the suggested Multi-agent intelligent system for Telemedicine which will contribute in the development of the health care systems in the developing countries.
 Keywords: Healthcare system; Medical System; Artificial Intelligence; Multi-Agent, Telemedicine; Telehealth.