Showing papers on "Multi-agent system published in 2018"

An Overview of Recent Advances in Fixed-Time Cooperative Control of Multiagent Systems

Abstract: Fixed-time cooperative control is currently a hot research topic in multiagent systems since it can provide a guaranteed settling time, which does not depend on initial conditions. Compared with asymptotic cooperative control algorithms, fixed-time cooperative control algorithms can achieve better closed-loop performance and disturbance rejection properties. Different from finite-time control, fixed-time cooperative control produces the faster rate of convergence and provides an explicit estimation of the settling time independent of initial conditions, which is desirable for multiagent systems. This paper aims at presenting an overview of recent advances in fixed-time cooperative control of multiagent systems. Some fundamental concepts about finite- and fixed-time stability and stabilization are first recalled with insight understanding. Then, recent results in finite- and fixed-time cooperative control are reviewed in detail and categorized according to different agent dynamics. Finally, this paper raises several challenging issues that need to be addressed in the near future.

Network Topology and Communication-Computation Tradeoffs in Decentralized Optimization

Abstract: In decentralized optimization, nodes cooperate to minimize an overall objective function that is the sum (or average) of per-node private objective functions. Algorithms interleave local computations with communication among all or a subset of the nodes. Motivated by a variety of applications..decentralized estimation in sensor networks, fitting models to massive data sets, and decentralized control of multirobot systems, to name a few..significant advances have been made toward the development of robust, practical algorithms with theoretical performance guarantees. This paper presents an overview of recent work in this area. In general, rates of convergence depend not only on the number of nodes involved and the desired level of accuracy, but also on the structure and nature of the network over which nodes communicate (e.g., whether links are directed or undirected, static or time varying). We survey the state-of-theart algorithms and their analyses tailored to these different scenarios, highlighting the role of the network topology.

Large-Scale Order Dispatch in On-Demand Ride-Hailing Platforms: A Learning and Planning Approach

Abstract: We present a novel order dispatch algorithm in large-scale on-demand ride-hailing platforms. While traditional order dispatch approaches usually focus on immediate customer satisfaction, the proposed algorithm is designed to provide a more efficient way to optimize resource utilization and user experience in a global and more farsighted view. In particular, we model order dispatch as a large-scale sequential decision-making problem, where the decision of assigning an order to a driver is determined by a centralized algorithm in a coordinated way. The problem is solved in a learning and planning manner: 1) based on historical data, we first summarize demand and supply patterns into a spatiotemporal quantization, each of which indicates the expected value of a driver being in a particular state; 2) a planning step is conducted in real-time, where each driver-order-pair is valued in consideration of both immediate rewards and future gains, and then dispatch is solved using a combinatorial optimizing algorithm. Through extensive offline experiments and online AB tests, the proposed approach delivers remarkable improvement on the platform's efficiency and has been successfully deployed in the production system of Didi Chuxing.

Multi-Agent Systems: A Survey

Abstract: Multi-agent systems (MASs) have received tremendous attention from scholars in different disciplines, including computer science and civil engineering, as a means to solve complex problems by subdividing them into smaller tasks. The individual tasks are allocated to autonomous entities, known as agents. Each agent decides on a proper action to solve the task using multiple inputs, e.g., history of actions, interactions with its neighboring agents, and its goal. The MAS has found multiple applications, including modeling complex systems, smart grids, and computer networks. Despite their wide applicability, there are still a number of challenges faced by MAS, including coordination between agents, security, and task allocation. This survey provides a comprehensive discussion of all aspects of MAS, starting from definitions, features, applications, challenges, and communications to evaluation. A classification on MAS applications and challenges is provided along with references for further studies. We expect this paper to serve as an insightful and comprehensive resource on the MAS for researchers and practitioners in the area.

Event-Triggered Control for Consensus of Multiagent Systems With Fixed/Switching Topologies

Abstract: In this paper, the leader-following consensus problem of high-order multiagent systems via event-triggered control is discussed. A novel distributed event-triggered communication protocol based on state estimates of neighboring agents is proposed to solve the consensus problem of the leader-following systems. We first investigate the consensus problem in a fixed topology, and then extend to the switching topologies. State estimates in fixed topology are only updated when the trigger condition is satisfied. However, state estimates in switching topologies are renewed with two cases: 1) the communication topology is switched or 2) the trigger condition is satisfied. Clearly, compared to continuous-time interaction, this protocol can greatly reduce the communication load of multiagent networks. Besides, the event-triggering function is constructed based on the local information and a new event-triggered rule is given. Moreover, “Zeno behavior” can be excluded. Finally, we give two examples to validate the feasibility and efficiency of our approach.

Consensus of Hybrid Multi-Agent Systems

Abstract: In this brief, we consider the consensus problem of hybrid multiagent systems. First, the hybrid multiagent system is proposed, which is composed of continuous-time and discrete-time dynamic agents. Then, three kinds of consensus protocols are presented for the hybrid multiagent system. The analysis tool developed in this brief is based on the matrix theory and graph theory. With different restrictions of the sampling period, some necessary and sufficient conditions are established for solving the consensus of the hybrid multiagent system. The consensus states are also obtained under different protocols. Finally, simulation examples are provided to demonstrate the effectiveness of our theoretical results.

Bipartite Tracking Consensus of Linear Multi-Agent Systems With a Dynamic Leader

Abstract: In this brief, the distributed bipartite tracking consensus problem for linear multi-agent systems (MASs) in the presence of a single leader is investigated. Compared with some related works on this topic, the leader’s control inputs in the present MAS model are allowed to be nonzero and unknown to each follower. To guarantee bipartite tracking consensus, a new kind of distributed non-smooth protocols based on the relative state information of neighboring agents are proposed and analyzed. With the help of tools from Lyapunov stability theory and graph theory, it is shown that bipartite tracking consensus in the close-loop MAS can be achieved if the gain matrix of protocol is suitably constructed and the control parameters of protocol are, respectively, larger than some positive quantities depending on global information of the considered MAS. To provide some efficient criteria for consensus seeking without involving any global information, some fully distributed protocols with adaptive control parameters are further designed and discussed. Finally, numerical simulations are given for illustration.

Achieving Cluster Formation of Multi-Agent Systems Under Aperiodic Sampling and Communication Delays

Abstract: This paper addresses the problem of cluster formation control for a networked multi-agent system (MAS) in the simultaneous presence of aperiodic sampling and communication delays. First, to fulfill multiple formation tasks, a group of agents are decomposed into $M$ distinct and nonoverlapping clusters. The agents in each cluster are then driven to achieve a desired formation, whereas the MAS as a whole accomplishes $ M $ cluster formations. Second, by a proper modeling of aperiodic sampling and communication delays, an aperiodic sampled-data cluster formation protocol (CFP) is delicately constructed such that the information exchanges among neighboring agents only occur intermittently at discrete instants of time. Third, a detailed theoretical analysis of cluster formability is carried out and a sufficient and necessary condition is provided such that the system is $M$ -cluster formable. Furthermore, a discontinuous Lyapunov functional approach is developed to derive a design criterion on the existence of an admissible sampled-data CFP. Finally, numerical simulations on a team of nonholonomic mobile robots are given to illustrate the effectiveness of the obtained theoretical result.

Formation Learning Control of Multiple Autonomous Underwater Vehicles With Heterogeneous Nonlinear Uncertain Dynamics

Abstract: In this paper, a new concept of formation learning control is introduced to the field of formation control of multiple autonomous underwater vehicles (AUVs), which specifies a joint objective of distributed formation tracking control and learning/identification of nonlinear uncertain AUV dynamics. A novel two-layer distributed formation learning control scheme is proposed, which consists of an upper-layer distributed adaptive observer and a lower-layer decentralized deterministic learning controller. This new formation learning control scheme advances existing techniques in three important ways: 1) the multi-AUV system under consideration has heterogeneous nonlinear uncertain dynamics; 2) the formation learning control protocol can be designed and implemented by each local AUV agent in a fully distributed fashion without using any global information; and 3) in addition to the formation control performance, the distributed control protocol is also capable of accurately identifying the AUVs’ heterogeneous nonlinear uncertain dynamics and utilizing experiences to improve formation control performance. Extensive simulations have been conducted to demonstrate the effectiveness of the proposed results.

Distributed Optimization for Control

Abstract: Advances in wired and wireless technology have necessitated the development of theory, models, and tools to cope with the new challenges posed by large-scale control and optimization problems over ...

Data-Driven Multiagent Systems Consensus Tracking Using Model Free Adaptive Control

Abstract: This paper investigates the data-driven consensus tracking problem for multiagent systems with both fixed communication topology and switching topology by utilizing a distributed model free adaptive control (MFAC) method. Here, agent’s dynamics are described by unknown nonlinear systems and only a subset of followers can access the desired trajectory. The dynamical linearization technique is applied to each agent based on the pseudo partial derivative, and then, a distributed MFAC algorithm is proposed to ensure that all agents can track the desired trajectory. It is shown that the consensus error can be reduced for both time invariable and time varying desired trajectories. The main feature of this design is that consensus tracking can be achieved using only input–output data of each agent. The effectiveness of the proposed design is verified by simulation examples.

Event-Triggered Communication for Leader-Following Consensus of Second-Order Multiagent Systems

Abstract: This paper is concerned with leader-following consensus of second-order multiagent systems with nonlinear dynamics. First, to save the limited communication resources, a new event-triggered control protocol is delicately developed without requiring continuous communication among the follower agents. Then, by employing the Lyapunov functional method and the Kronecker product technique, a novel sufficient criterion with less conservation is derived to guarantee the leader-following consensus while excluding the Zeno behavior. Furthermore, for the first time, an algorithm to actively adjust the leader adjacency matrix is presented, which efficiently expands the application range of some existing criteria. An example is finally given to illustrate the effectiveness of theoretical results.

Distributed $H_\infty$ Output-Feedback Control for Consensus of Heterogeneous Linear Multiagent Systems With Aperiodic Sampled-Data Communications

Abstract: This paper is concerned with the output-feedback controller design for consensus of a class of heterogeneous linear multiagent systems with the aperiodic sampled-data measurement. Under mild assumptions that the sampling periods are taken from a given set and the agent systems are time synchronized, an equivalent switched system model is first proposed for the heterogeneous agent system with nonuniform sampling. The overall leader-following tracking control problem (LFTCP) is then formulated as the output regulation of a discrete-time switched system. By using some algebraic manipulations, the control problem under consideration is further decoupled into two control subproblems, i.e., a static output-feedback (SOF) control problem plus a simple feedback control problem related to the communication topology. Based on the Lyapunov stability theory, some sufficient conditions are obtained for the solvability of LFTCP. In our results, the SOF controller gains are determined by solving some strict linear matrix inequalities. Finally, a simulation study on the modified Caltech multivehicle wireless testbed is presented to show the effectiveness of the proposed design method.

Decision Making in Multiagent Systems: A Survey

Abstract: Intelligent transport systems, efficient electric grids, and sensor networks for data collection and analysis are some examples of the multiagent systems (MAS) that cooperate to achieve common goals. Decision making is an integral part of intelligent agents and MAS that will allow such systems to accomplish increasingly complex tasks. In this survey, we investigate state-of-the-art work within the past five years on cooperative MAS decision making models, including Markov decision processes, game theory, swarm intelligence, and graph theoretic models. We survey algorithms that result in optimal and suboptimal policies such as reinforcement learning, dynamic programming, evolutionary computing, and neural networks. We also discuss the application of these models to robotics, wireless sensor networks, cognitive radio networks, intelligent transport systems, and smart electric grids. In addition, we define key terms in the area and discuss remaining challenges that include incorporating big data advancements to decision making, developing autonomous, scalable and computationally efficient algorithms, tackling more complex tasks, and developing standardized evaluation metrics. While recent surveys have been published on this topic, we present a broader discussion of related models and applications. Note to Practitioners: Future smart cities will rely on cooperative MAS that make decisions about what actions to perform that will lead to the completion of their tasks. Decision making models and algorithms have been developed and reported in the literature to generate such sequences of actions. These models are based on a wide variety of principles including human decision making and social animal behavior. In this paper, we survey existing decision making models and algorithms that generate optimal and suboptimal sequences of actions. We also discuss some of the remaining challenges faced by the research community before more effective MAS deployment can be achieved in this age of Internet of Things, robotics, and mobile devices. These challenges include developing more scalable and efficient algorithms, utilizing the abundant sensory data available, tackling more complex tasks, and developing evaluation standards for decision making.

Event-Triggered Leader-Following Consensus for Nonlinear Multiagent Systems Subject to Actuator Saturation Using Dynamic Output Feedback Method

Abstract: This paper addresses the distributed event-triggered consensus problem for a class of nonlinear multiagent systems subject to actuator saturation. A new distributed event-based dynamic output feedback controller is put forward via the relative output measurements of neighboring agents. It removes the impractical assumptions that the controllers for agents have to update continuously and the observers embedded in agents have to share information with their neighbors, thus the energy consumptions of agents are significantly reduced. Two event-triggered strategies for the cases with and without continuous communication among neighboring agents are established. Sufficient conditions are derived in terms of matrix inequalities to guarantee the exponential leader-following consensus. The problem of designing a distributed event-triggered control law such that the domain of attraction for consensus errors is enlarged, formulated, and solved as an optimization problem with matrix inequality constraints. Simulation results are given to verify the effectiveness of the theoretical results.

Adaptive multi-agents synchronization for collaborative driving of autonomous vehicles with multiple communication delays

Abstract: The development of automated and coordinated driving systems (platooning) is an hot topic today for vehicles and it represents a challenging scenario that heavily relies on distributed control in the presence of wireless communication network. To actuate platooning in a safe way it is necessary to design controllers able to effectively operate on informations exchanged via Inter-Vehicular Communication (IVC) systems despite the presence of unavoidable communication impairments, such as multiple time-varying delays that affect communication links. To this aim in this paper we propose a novel distributed adaptive collaborative control strategy that exploits information coming from connected vehicles to achieve leader synchronization and we analytically demonstrate its stability with a Lyapunov-Krasovskii approach. The effectiveness of the proposed strategy is shown via numerical simulations in P lexe , a state of the art IVC and mobility simulator that includes basic building blocks for platooning.

Multi-Agent Systems Applications in Energy Optimization Problems: A State-of-the-Art Review

Abstract: This article reviews the state-of-the-art developments in Multi-Agent Systems (MASs) and their application to energy optimization problems. This methodology and related tools have contributed to changes in various paradigms used in energy optimization. Behavior and interactions between agents are key elements that must be understood in order to model energy optimization solutions that are robust, scalable and context-aware. The concept of MAS is introduced in this paper and it is compared with traditional approaches in the development of energy optimization solutions. The different types of agent-based architectures are described, the role played by the environment is analysed and we look at how MAS recognizes the characteristics of the environment to adapt to it. Moreover, it is discussed how MAS can be used as tools that simulate the results of different actions aimed at reducing energy consumption. Then, we look at MAS as a tool that makes it easy to model and simulate certain behaviors. This modeling and simulation is easily extrapolated to the energy field, and can even evolve further within this field by using the Internet of Things (IoT) paradigm. Therefore, we can argue that MAS is a widespread approach in the field of energy optimization and that it is commonly used due to its capacity for the communication, coordination, cooperation of agents and the robustness that this methodology gives in assigning different tasks to agents. Finally, this article considers how MASs can be used for various purposes, from capturing sensor data to decision-making. We propose some research perspectives on the development of electrical optimization solutions through their development using MASs. In conclusion, we argue that researchers in the field of energy optimization should use multi-agent systems at those junctures where it is necessary to model energy efficiency solutions that involve a wide range of factors, as well as context independence that they can achieve through the addition of new agents or agent organizations, enabling the development of energy-efficient solutions for smart cities and intelligent buildings.

Event-Triggered Communication and Data Rate Constraint for Distributed Optimization of Multiagent Systems

Abstract: This paper is concerned with solving a large category of convex optimization problems using a group of agents, each only being accessible to its individual convex cost function. The optimization problems are modeled as minimizing the sum of all the agents’ cost functions. The communication process between agents is described by a sequence of time-varying yet balanced directed graphs which are assumed to be uniformly strongly connected. Taking into account the fact that the communication channel bandwidth is limited, for each agent we introduce a vector-valued quantizer with finite quantization levels to preprocess the information to be exchanged. We exploit an event-triggered broadcasting technique to guide information exchange, further reducing the communication cost of the network. By jointly designing the dynamic event-triggered encoding–decoding schemes and the event-triggered sampling rules (to analytically determine the sampling time instant sequence for each agent), a distributed subgradient descent algorithm with constrained information exchange is proposed. By selecting the appropriate quantization levels, all the agents’ states asymptotically converge to a consensus value which is also the optimal solution to the optimization problem, without committing saturation of all the quantizers. We find that one bit of information exchange across each connected channel can guarantee that the optimiztion problem can be exactly solved. Theoretical analysis shows that the event-triggered subgradient descent algorithm with constrained data rate of networks converges at the rate of ${O}( {\ln t/{\sqrt {t}}})$ . We supply a numerical simulation experiment to demonstrate the effectiveness of the proposed algorithm and to validate the correctness of theoretical results.

Leader–Follower Output Synchronization of Linear Heterogeneous Systems With Active Leader Using Reinforcement Learning

Abstract: This paper develops optimal control protocols for the distributed output synchronization problem of leader–follower multiagent systems with an active leader. Agents are assumed to be heterogeneous with different dynamics and dimensions. The desired trajectory is assumed to be preplanned and is generated by the leader. Other follower agents autonomously synchronize to the leader by interacting with each other using a communication network. The leader is assumed to be active in the sense that it has a nonzero control input so that it can act independently and update its control to keep the followers away from possible danger. A distributed observer is first designed to estimate the leader’s state and generate the reference signal for each follower. Then, the output synchronization of leader–follower systems with an active leader is formulated as a distributed optimal tracking problem, and inhomogeneous algebraic Riccati equations (AREs) are derived to solve it. The resulting distributed optimal control protocols not only minimize the steady-state error but also optimize the transient response of the agents. An off-policy reinforcement learning algorithm is developed to solve the inhomogeneous AREs online in real time and without requiring any knowledge of the agents’ dynamics. Finally, two simulation examples are conducted to illustrate the effectiveness of the proposed algorithm.

Resilient Randomized Quantized Consensus

Abstract: We consider the problem of multiagent consensus where some agents are subject to faults/attacks and might make updates arbitrarily. The network consists of agents taking integer-valued (i.e., quantized) states under directed communication links. The goal of the healthy normal agents is to form consensus in their state values, which may be disturbed by the non-normal, malicious agents. We develop update schemes to be equipped by the normal agents whose interactions are asynchronous and subject to nonuniform and time-varying time delays. In particular, we employ a variant of the so-called mean subsequence reduced algorithms, which have been long studied in computer science, where each normal agent ignores extreme values from its neighbors. We solve the resilient quantized consensus problems in the presence of totally/locally bounded adversarial agents and provide necessary and sufficient conditions in terms of the connectivity notion of graph robustness. Furthermore, it will be shown that randomization is essential both in quantization and in the updating times when normal agents interact in an asynchronous manner. The results are examined through a numerical example.

Optimized Multi-Agent Formation Control Based on an Identifier–Actor–Critic Reinforcement Learning Algorithm

Abstract: The paper proposes an optimized leader–follower formation control for the multi-agent systems with unknown nonlinear dynamics. Usually, optimal control is designed based on the solution of the Hamilton–Jacobi–Bellman equation, but it is very difficult to solve the equation because of the unknown dynamic and inherent nonlinearity. Specifically, to multi-agent systems, it will become more complicated owing to the state coupling problem in control design. In order to achieve the optimized control, the reinforcement learning algorithm of the identifier–actor–critic architecture is implemented based on fuzzy logic system (FLS) approximators. The identifier is designed for estimating the unknown multi-agent dynamics; the actor and critic FLSs are constructed for executing control behavior and evaluating control performance, respectively. According to Lyapunov stability theory, it is proven that the desired optimizing performance can be arrived. Finally, a simulation example is carried out to further demonstrate the effectiveness of the proposed control approach.

Cooperative Output Regulation of Linear Multi-Agent Systems by Intermittent Communication: A Unified Framework of Time- and Event-Triggering Strategies

Abstract: This paper addresses the cooperative output regulation problem of heterogeneous linear multi-agent systems with directed communication topologies, requiring only intermittent communication. First, we propose a unified framework of time- and event-triggering strategies. Then based on the unified triggering framework, a novel triggering mechanism utilizing the self-triggering strategy is developed. It is shown that with the proposed triggering mechanism, the cooperative output regulation problem can be solved by a distributed control law with only intermittent communication. It is further shown that with the unified framework of time- and event-triggering strategies for each agent, a positive minimum inter-event time can be explicitly given and Zeno behavior can be thus excluded. An example is finally provided to demonstrate the effectiveness of the proposed controller and the triggering mechanism.

Trusting Intelligent Machines: Deepening Trust Within Socio-Technical Systems

Abstract: Intelligent machines have reached capabilities that go beyond a level that a human being can fully comprehend without sufficiently detailed understanding of the underlying mechanisms. The choice of moves in the game Go (generated by Deep Mind?s Alpha Go Zero [1]) are an impressive example of an artificial intelligence system calculating results that even a human expert for the game can hardly retrace [2]. But this is, quite literally, a toy example. In reality, intelligent algorithms are encroaching more and more into our everyday lives, be it through algorithms that recommend products for us to buy, or whole systems such as driverless vehicles. We are delegating ever more aspects of our daily routines to machines, and this trend looks set to continue in the future. Indeed, continued economic growth is set to depend on it. The nature of human-computer interaction in the world that the digital transformation is creating will require (mutual) trust between humans and intelligent, or seemingly intelligent, machines. But what does it mean to trust an intelligent machine? How can trust be established between human societies and intelligent machines?

Multi-Agent Systems and Blockchain: Results from a Systematic Literature Review

Abstract: Multi-Agent Systems (MAS) technology is widely used for the development of intelligent distributed systems that manage sensitive data (e.g., ambient assisted living, healthcare, energy trading). To foster accountability and trusted interactions, recent trends advocate the use of blockchain technologies (BCT) for MAS. Although most of these approaches have only started exploring the topic, there is an impending need for establishing a research road-map, as well as identifying scientific and technological challenges in this scope. As a first necessary step towards this goal, this paper presents a systematic literature review of studies involving MAS and BCT as reconciling solutions. Aiming at providing a comprehensive overview of their application domains, we analyze motivations, assumptions, requirements, strengths, and limitations presented in the current state of the art. Moreover, discussing the future challenges, we introduce our vision on how MAS and BCT could be combined in different application scenarios.

Solving the Job-Shop Scheduling Problem in the Industry 4.0 Era

Abstract: Technological developments along with the emergence of Industry 4.0 allow for new approaches to solve industrial problems, such as the Job-shop Scheduling Problem (JSP). In this sense, embedding Multi-Agent Systems (MAS) into Cyber-Physical Systems (CPS) is a highly promising approach to handle complex and dynamic JSPs. This paper proposes a data exchange framework in order to deal with the JSP considering the state-of-the-art technology regarding MAS, CPS and industrial standards. The proposed framework has self-configuring features to deal with disturbances in the production line. This is possible through the development of an intelligent system based on the use of agents and the Internet of Things (IoT) to achieve real-time data exchange and decision making in the job-shop. The performance of the proposed framework is tested in a simulation study based on a real industrial case. The results substantiate gains in flexibility, scalability and efficiency through the data exchange between factory layers. Finally, the paper presents insights regarding industrial applications in the Industry 4.0 era in general and in particular with regard to the framework implementation in the analyzed industrial case.

Cooperative Optimal Control of Battery Energy Storage System Under Wind Uncertainties in a Microgrid

Abstract: Since high-penetration renewable sources are integrated into the future power system, energy storage systems are often installed to maintain the frequency stability in a microgrid. The operation mode of a microgrid may frequently change due to the intermittency of renewable sources, and energy storage systems will be charged/discharged accordingly to smooth and balance the generation of renewable sources. Thus, energy storage systems should be coordinated in a proper approach to ensure the supply–demand balance while increasing their profits and energy efficiency. To this end, a distributed optimal solution for energy storage systems to maintain the supply–demand balance while maximizing their welfare and energy efficiency is proposed to energy storage systems by enhancing the coordination through the communication under a multiagent system framework. Under this framework, each energy storage system is designated as an agent, and it only utilizes the local information to interact with the neighbouring agents. Additionally, since the participants in a microgrid may not be willing to release their information about cost functions, or even the local gradient with other neighbouring agents, the proposed solution could be implemented without these private information to the individual agents. The simulation studies are carried out for IEEE 14-bus and 30-BESS systems to validate the effectiveness of the proposed distributed solution.