Fixed-Time Antidisturbance Consensus Tracking for Nonlinear Multiagent Systems With Matching and Mismatching Disturbances

In this paper,fixed-time consensus tracking for mul-tiagent systems(MASs)with dynamics in the form of strict feed-back affine nonlinearity is addressed.A fixed-time antidistur-bance consensus tracking protocol is proposed,which consists of a distributed fixed-time observer,a fixed-time disturbance observer,a nonsmooth antidisturbance backstepping controller,and the fixed-time stability analysis is conducted by using the Lyapunov theory correspondingly.This paper includes three main improvements.First,a distributed fixed-time observer is developed for each follower to obtain an estimate of the leader’s output by utilizing the topology of the communication network.Second,a fixed-time disturbance observer is given to estimate the lumped disturbances for feedforward compensation.Finally,a nonsmooth antidisturbance backstepping tracking controller with feedforward compensation for lumped disturbances is designed.In order to mitigate the“explosion of complexity”in the tradi-tional backstepping approach,we have implemented a modified nonsmooth command filter to enhance the performance of the closed-loop system.The simulation results show that the pro-posed method is effective.

Adaptive Event-Triggered Time-Varying Output Group Formation Containment Control of Heterogeneous Multiagent Systems

In this paper,a class of time-varying output group formation containment control problem of general linear hetero-geneous multiagent systems(MASs)is investigated under directed topology.The MAS is composed of a number of tracking leaders,formation leaders and followers,where two different types of leaders are used to provide reference trajectories for movement and to achieve certain formations,respectively.Firstly,compen-sators are designed whose states are estimations of tracking lead-ers,based on which,a controller is developed for each formation leader to accomplish the expected formation.Secondly,two event-triggered compensators are proposed for each follower to evalu-ate the state and formation information of the formation leaders in the same group,respectively.Subsequently,a control protocol is designed for each follower,utilizing the output information,to guide the output towards the convex hull generated by the forma-tion leaders within the group.Next,the triggering sequence in this paper is decomposed into two sequences,and the inter-event intervals of these two triggering conditions are provided to rule out the Zeno behavior.Finally,a numerical simulation is intro-duced to confirm the validity of the proposed results.

Limited-Budget Consensus Design and Analysis for Multiagent Systems With Switching Topologies and Intermittent Communications

This paper investigates limited-budget consensus design and analysis problems of general high-order multiagent systems with intermittent communications and switching topologies.The main contribution of this paper is that the tradeoff design between the energy consumption and the consensus performance can be realized while achieving leaderless or leaderfollowing consensus,under constraints of limited budgets and intermittent communications.Firstly,a new intermittent limitedbudget consensus control protocol with a practical trade-off design index is proposed,where the total budget of the whole multiagent system is limited.Then,leaderless limited-budget consensus design and analysis criteria are derived,in which the matrix variables of linear matrix inequalities are determined according to the total budget and the practical trade-off design parameters.Meanwhile,an explicit formulation of the consensus function is derived to describe the consensus state trajectory of the whole system.Moreover,a new two-stage transformation strategy is utilized for leader-following cases,by which the dynamics decomposition of leaderless and leader-following cases can be converted into a unified framework,and sufficient conditions of the leader-following limited-budget consensus design and analysis are determined via those of the leaderless cases.Finally,numerical simulations are given to illustrate theoretical results.

Distributed model predictive control for multiagent systems with improved consistency

This paper addresses an improved distributed model predictive control （DMPC） scheme for multiagent systems with an attempt to improving its consistency. The deviation between what an agent is actually doing and what its neighbors believe that agent is doing is penalized in the cost function of each agent. At each sampling instant the compatibility constraint of each agent is set tighter than the previous sampling instant. Like the traditional approach, the performance cost is utilized as the Lyapunov function to prove closed-looped stability. The closed-loop stability is guaranteed if the weight matrix for deviation in the cost function are sufficiently large. The proposed distributed control scheme is formulated as quadratic programming with quadratic constraints. A numerical example is given to illustrate the effectiveness of the proposed scheme.

A Personalized Search Model Using Online Social Network Data Based on a Holonic Multiagent System

Personalized search utilizes user preferences to optimize search results,and most existing studies obtain user preferences by analyzing user behaviors in search engines that provide click-through data.However,the behavioral data are noisy because users often clicked some irrelevant documents to find their required information,and the new user cold start issue represents a serious problem,greatly reducing the performance of personalized search.This paper attempts to utilize online social network data to obtain user preferences that can be used to personalize search results,mine the knowledge of user interests,user influence and user relationships from online social networks,and use this knowledge to optimize the results returned by search engines.The proposed model is based on a holonic multiagent system that improves the adaptability and scalability of the model.The experimental results show that utilizing online social network data to implement personalized search is feasible and that online social network data are significant for personalized search.

Pseudo-Predictor Feedback Control for Multiagent Systems with Both State and Input Delays

This paper is concerned with the consensus problem for high-order continuous-time multiagent systems with both state and input delays.A novel approach referred to as pseudopredictor feedback protocol is proposed.Unlike the predictorbased feedback protocol which utilizes the open-loop dynamics to predict the future states,the pseudo-predictor feedback protocol uses the closed-loop dynamics of the multiagent systems to predict the future agent states.Full-order/reduced-order observer-based pseudo-predictor feedback protocols are proposed,and it is shown that the consensus is achieved and the input delay is compensated by the proposed protocols.Necessary and sufficient conditions guaranteeing the stability of the integral delay systems are provided in terms of the stability of the series of retarded-type time-delay systems.Furthermore,compared with the existing predictor-based protocols,the proposed pseudo-predictor feedback protocol is independent of the input signals of the neighboring agents and is easier to implement.Finally,a numerical example is given to demonstrate the effectiveness of the proposed approaches.

Consensus of multiagent systems based on disturbance observer

The objectives of this work are the development and design of disturbance observers (DO’s) for a team of agents that accomplish consensus on agents’ states in the presence of exogenous disturbances. A pinning control strategy is designed for a part of agents of the multiagent systems without disturbances, and this pinning control can bring multiple agents’ states to reaching an expected consensus value. Under the effect of the disturbances, nonlinear disturbance observers are developed for disturbances generated by an exogenous system to estimate the disturbances. Asymptotical consensus of the multiagent systems with disturbances under the composite controller can be achieved. Finally, by applying an example of multiagent systems with switching topologies and exogenous disturbances, the design of the parameters of DO’s are illuminated.

On consensus protocols for high-order multiagent systems

In this paper,the static consensus problem and the dynamic consensus problem are considered for a class of high-order multiagent systems.With the proposed consensus protocols,necessary and sufficient conditions for the consensus problems are obtained.For the static consensus protocol,the desired consensus speed can be achieved by adjusting feedback gains.Simulations show the effectiveness of the proposed consensus protocols.

Distributed optimization for discrete-time multiagent systems with nonconvex control input constraints and switching topologies

This paper addresses the distributed optimization problem of discrete-time multiagent systems with nonconvex control input constraints and switching topologies.We introduce a novel distributed optimization algorithm with a switching mechanism to guarantee that all agents eventually converge to an optimal solution point,while their control inputs are constrained in their own nonconvex region.It is worth noting that the mechanism is performed to tackle the coexistence of the nonconvex constraint operator and the optimization gradient term.Based on the dynamic transformation technique,the original nonlinear dynamic system is transformed into an equivalent one with a nonlinear error term.By utilizing the nonnegative matrix theory,it is shown that the optimization problem can be solved when the union of switching communication graphs is jointly strongly connected.Finally,a numerical simulation example is used to demonstrate the acquired theoretical results.

New Algorithm for FMS Scheduling Based on Multiagent System Architecture

Redefined benefit-driven function is used to study the dynamic scheduling of FMS based on multiagent architecture. Each agent is dedicated to a work center, i.e. a set of the manufacturing system. In one hand, each agent selects locally and dynamically the dispatching rule(DR) that seems to be most suited to the operating conditions, production objectives and current shop status. On the other hand, each task should bring certain amount of benefit for the manufacturer. So, it is reasonable to have the dynamic scheduling of FMS relying upon multiagent architecture using the benefit-driven function as a strategy. Well, today's manufacturing corporation, especially the high & new technology one and deep machining one, the cost of their products is mainly determined by how much the knowledge is input From this viewpoint, we redefined the benefit-driven function, hi the end, this approach is compared with other existing DRs on a job-shop problem, already used in other research works.

Model Checking for Probabilistic Multiagent Systems

In multiagent systems,agents usually do not have complete information of the whole system,which makes the analysis of such systems hard.The incompleteness of information is normally modelled by means of accessibility relations,and the schedulers consistent with such relations are called uniform.In this paper,we consider probabilistic multiagent systems with accessibility relations and focus on the model checking problem with respect to the probabilistic epistemic temporal logic,which can specify both temporal and epistemic properties.However,the problem is undecidable in general.We show that it becomes decidable when restricted to memoryless uniform schedulers.Then,we present two algorithms for this case:one reduces the model checking problem into a mixed integer non-linear programming(MINLP)problem,which can then be solved by Satisfiability Modulo Theories(SMT)solvers,and the other is an approximate algorithm based on the upper confidence bounds applied to trees(UCT)algorithm,which can return a result whenever queried.These algorithms have been implemented in an existing model checker and then validated on experiments.The experimental results show the efficiency and extendability of these algorithms,and the algorithm based on UCT outperforms the one based on MINLP in most cases.

A new approach to consensus problems in discrete-time multiagent systems with time-delays

In this paper, consensus problems in discrete-time multiagent systems with time-invariant delays are considered. In order to characterize the structures of communication topologies, the concept of ＂pre-leader-follower＂ decomposition is introduced. Then, a necessary and sufficient condition for state consensus is established. By this method, consensus problems in networks with a single time-delay, as well as with multiple time-delays, are studied, and some necessary and sufficient conditions for solvability of consensus problems are obtained.

Formation and obstacle avoidance control for multiagent systems

This paper considers the problems of formation and obstacle avoidance for multiagent systems.The objective is to design a term of agents that can reach a desired formation while avoiding collision with obstacles.To reduce the amount of information interaction between agents and target,we adopt the leader-follower formation strategy.By using the receding horizon control (RHC),an optimal problem is formulated in terms of cost minimization under constraints.Information on obstacles is incorporated online as sensed in a limited sensing range.The communication requirements between agents are that the followers should obtain the previous optimal control trajectory of the leader to each update time.The stability is guaranteed by adding a terminal-state penalty to the cost function and a terminal-state region to optimal problem.Finally,simulation studies are provided to verify the effectiveness of the proposed approach.

Adaptive output feedback formation tracking for a class of multiagent systems with quantized input signals

A novel adaptive output feedback control approach is presented for formation tracking of a multiagent system with uncertainties and quantized input signals. The agents are described by nonlinear dynamics models with unknown parameters and immeasurable states. A high-gain dynamic state observer is established to estimate the immeasurable states. With a proper design parameter choice, an adaptive output feedback control method is developed employing a hysteretic quantizer and the designed dynamic state observer. Stability analysis shows that the control strategy can guarantee that the agents can maintain the formation shape while tracking the reference trajectory. In addition, all the signals in the closed-loop system are bounded. The effectiveness of the control strategy is validated by simulation.

A Study of Applications of Multiagent System Specifications and the Key Techniques in Automatic Abstracts System

Abstract In this thesis, multiagent system specifications, multiagent system architectures, agent communication languages and agent communication protocols, automatic abstracting based on multiagent technologies are studied. Some concerned problems of designs and realization of automatic abstracting systems based on multiagent technologies are studied, too. Chapter 1 shows the significance and objectives of the thesis, its main contents are summarized, and innovations of the thesis are showed. Some basic concepts of agents and multiagent systems are studied in Chapter 2. The definitions of agents and multiagent systems are given, and the theory, technologies and applications of multiagent systems are summarized. Furthermore, some important studying trends of multiagent systems are set forward. Multiagent system specifications are studied in Chapter 3. MAS/KIB—a multiagent system specification is built using mental states such as K (Know), B (Belief), and I (Intention), its grammar and semanteme are discussed, axioms and inference rules are given, and some properties are researched. We also compare MAS/KIB with other existing specifications. MAS/KIB has the following characteristics: (1) each agent has its own world outlook; (2) no global data in the system; (3) processes of state changes are used as indexes to systems; (4) it has the characteristics of not only time series logic but also dynamic logic; and (5) interactive actions are included. The architectures of multiagent systems are studied in Chapter 4. First, we review some typical architecture of multiagent systems, agent network architecture, agent federated architecture, agent blackboard architecture, and Foundation of Intelligent Physical Agent(FIPA) architecture. For the first time, we set forward and study the layering and partitioning models of the architectures of multiagent systems, organizing architecture models, and interoperability architecture model of multiagent systems. Chapter 5 studies agent communication languages. We study FIPA Agent Communication Language(ACL) message grammar, Communicative Act(CA) types, ACL semantics and Agent Communication Protocols. The string grammar of ACL messages and SL grammar represented by EBNF are given, and a new ACL semantic model is set forward. We study automatic abstracting based on multiagent technologies in Chapter 6. For the first time, the idea of constructing automatic abstracting systems based on multiagent technologies is set forward, and the necessity and feasibility of constructing such systems are set forth. The architecture of automatic abstracting systems based on multiagent technologies is provided, and the basic elements and mutual relations are studied. We set forth the working principle of the automatic abstracting systems based on multiagent technologies and how the contradiction of domain currency and abstract quality of automatic abstracting systems is resolved. Studies, simulation analysis and design of the automatic abstracting experimental system based on multiagent technologies(ABES/MAT) are made in Chapter 7. We study the architecture of ABES/MAT, and set forth its basic elements and mutual relations. The user model and mathematics descriptions of ABES/MAT in the Internet environment are provided. Three coordination algorithms that synthetically use environment information, system information and history information are set forward. Then, we analyze the performance of ABES/MAT based on the simulations, and get suitable number fo abstracting agents for each domain in a given load. Finally, we compare the three coordination algorithms. The realization of ABES/MAT is introduced in Chapter 8. Firstly, we study the problem of the agentification of legacy systems, and show how to realize the agentification of glanc, ladies and news. Then, the architectures and functions of Coordination Management Agent(CMA) and Information Agent(IA) are set forth. The agent naming grammar represented by EBNF is given., In the following, we explain the designed agent communicative acts and management

SOCIAL OR INDIVIDUAL LEARNING？ AN AGGREGATED SOLUTION FOR COORDINATION IN MULTIAGENT SYSTEMS

There are mainly two different ways of learning for animals and humans： trying on yourself through interactions or imitating/copying others through communication/observation. How these two learning strategies differ and what roles they are playing in achieving coordination among individuals are two challenging problems for researchers from various disciplines. In multiagent systems, most existing work simply focuses on individual learning for achieving coordination among agents. The social learning perspective has been largely neglected. Against this background, this article contributes by proposing an integrated solution to decision making between social learning and individual learning in multiagent systems. Two integration modes have been proposed that enable agents to choose in between these two learning strategies, either in a t＇Lxed or in an adaptive manner. Experimental evaluations have shown that these two kinds of leaning strategies have different roles in maintaining efficient coordination among agents. These differences can reveal some significant insights into the manipulation and control of agent behaviors in multiagent systems, and also shed light on understanding the social factors in shaping coordinated behaviors in humans and animals.

Bipartite consensus for nonlinear time-delay multiagent systems via time-varying gain control method

The bipartite consensus problem is addressed for a class of nonlinear time-delay multiagent systems in this paper.Therein,the uncertain nonlinear dynamics of all agents satisfy a Lipschitz growth condition with unknown constants,and part of the state information cannot be measured.In this case,a time-varying gain compensator is constructed,which only utilizes the output information of the follower and its neighbors.Subsequently,a distributed output feedback control protocol is proposed on the basis of the compensator.According to Lyapunov stability theory,it is proved that the bipartite consensus can be guaranteed by means of the designed control protocol.Different from the existing literature,this paper studies the leader-follower consensus problem under a weaker connectivity condition,i.e.,the signed directed graph is structurally balanced and contains a directed spanning tree.Two simulation examples are carried out to show the feasibility of the proposed control strategy.

Event-Triggered Adaptive Neural Control for Multiagent Systems with Deferred State Constraints

This paper focuses on the leader-following consensus control problem for nonlinear multiagent systems subject to deferred asymmetric time-varying state constraints.A distributed eventtriggered adaptive neural control approach is advanced.By virtue of a distributed sliding-mode estimator,the leader-following consensus control problem is converted into multiple simplified tracking control problems.Afterwards,a shifting function is utilized to transform the error variables such that the initial tracking condition can be totally unknown and the state constraints can be imposed at a specified time instant.Meanwhile,the deferred asymmetric time-varying full state constraints are addressed by a class of asymmetric barrier Lyapunov function.In order to reduce the burden of communication,a relative threshold event-triggered mechanism is incorporated into controller and Zeno behavior is excluded.Based on Lyapunov stability theorem,all closed-loop signals are proved to be semi-globally uniformly ultimately bounded.Finally,a practical simulation example is given to verify the presented control scheme.

Tracking Control of Multi-Agent Systems Using a Networked Predictive PID Tracking Scheme

With the rapid development of network technology and control technology,a networked multi-agent control system is a key direction of modern industrial control systems,such as industrial Internet systems.This paper studies the tracking control problem of networked multi-agent systems with communication constraints,where each agent has no information on the dynamics of other agents except their outputs.A networked predictive proportional integral derivative(PPID)tracking scheme is proposed to achieve the desired tracking performance,compensate actively for communication delays,and simplify implementation in a distributed manner.This scheme combines the past,present and predictive information of neighbour agents to form a tracking error signal for each agent,and applies the proportional,integral,and derivative of the agent tracking error signal to control each individual agent.The criteria of the stability and output tracking consensus of multi-agent systems with the networked PPID tracking scheme are derived through detailed analysis on the closed-loop systems.The effectiveness of the networked PPID tracking scheme is illustrated via an example.

Enabling Technology of Multiagent Manufacturing System:A Novel Mode of Self-organizing IoT Manufacturing

As the manufacturing mode focuses more on network and community,the orders and production processes are becoming highly dynamic and unpredictable.The traditional manufacturing system cannot handle those exceptional events such as rush orders and machine breakdowns.Nevertheless,the multiagent manufacturing system(MAMS)becomes a critical pattern to deal with these disturbances in a real-time way.However,due to the lack of universality,MAMS is difficult to be applied to industrial sites.A new multiagent architecture and the relay cooperation model based on a positive process relation matrix are proposed to address this paper’s issue.An optimized contract net protocol(CNP)-based negotiation mechanism is developed to improve the efficiency of collaboration in the proposed architecture.Finally,a case study of self-organizing internet of things(Io T)manufacturing system is used to test the feasibility and effectiveness of the method.It is shown that the proposed self-organizing Io T manufacturing mode outperforms the traditional manufacturing system in terms of makespan and critical machine workload balancing under disturbances through comparison.