Hybrid Multi-agent System Research Articles

Consensus tracking control for a class of general linear hybrid multi-agent systems: A model-free approach

This paper studies the consensus tracking problem for a class of general linear hybrid multi-agent systems via a model-free approach. The hybrid multi-agent system is composed of N followers with discrete-time or continuous-time linear dynamics and one leader with discrete-time linear dynamics. The proposed distributed consensus tracking controller consists of a compensation term and an optimal control term. Specifically, the compensation term is introduced to address the effect of agents’ inherent dynamics on consensus tracking, and the optimal control term is designed to achieve the performance goal. By designing distributed observers for the following agents, we first propose a model-based distributed consensus tracking protocol. A model-free distributed consensus tracking protocol is further constructed via designing adaptive observers and identifying agents’ dynamics. The salient features of the model-free consensus tracking protocol are that the system matrix of the leader is only available for some followers and any follower does not need to know the eigenvalues of the graph matrix. Moreover, in order to reduce the computational burden, a system identification process is implemented based on the parallel learning method. Finally, the presented theoretical results are verified by simulation.

Group consensus of multi-agent systems with hybrid characteristics and directed topological networks

In this paper, the group consensus problem is investigated for multi-agent systems (MASs) with hybrid characteristics and directed topological networks. Firstly, the dynamical model of hybrid multi-agent system (MAS) is constructed, which includes discrete time agents and continuous time agents. A class of distributed control protocols are put forward for hybrid MASs. Then, under fixed and directed topological networks, sufficient and necessary conditions for the realization of group consensus are provided on basis of matrix theory and graph theory. Finally, simulations examples are given to further verify the validity of our theoretical results.

О некоторых вопросах архитектуры искусственной личности

This paper examines the possibility of artificial personality as a form of strong AI and as a model of the human psyche. It is argued that connectionism is not relevant for creating an artificial personality – brain is a hybrid system, which, in addition to self-learning circuits, contains algorithmic procedures. A hypothesis of the functional role of the awareness of self-consciousness property as a reflexive rank, since genuine intelligence arise, is proposed. AI as a model can be built as a hybrid multi-agent system. Some circuits may be analog, others – digital; some systems may be self-learning, others may operate under algorithmic rules and mathematical functions. The success of perceptronical inclines to idea that sensation modeling can be self-learning. The level of emotions can be realized as primary representations in the analog form. The later may be abstracted in judgments treated as secondary representations. The level of self-consciousness or meta-psychological states implies certain possibility of learning to self-learning (as an analogue of awareness of self-awareness) using secondary representations at the input.

Consensus of Second‐Order Heterogeneous Hybrid Multiagent Systems via Event‐Triggered Protocols

This paper investigates the event‐based consensus problem for the heterogeneous hybrid multiagent system (MAS). First, the heterogeneous hybrid MAS is proposed which contains continuous and discrete‐time subsystems with second‐order and first‐order heterogeneous dynamics. Second, the event‐triggered protocols are proposed, which mainly include the event‐based control laws and event‐triggered conditions for different kinds of agents. Then, the consensus conclusions of fixed topology and switching topologies are obtained based on graph theory and nonnegative matrix theory, which include the constraints on control parameters, coupling gains, and sampling interval to guarantee consensus. Finally, a simulation example is given to verify the efficiency of the proposed protocols.

Further analysis for consensus of hybrid multiagent systems: A unified framework

AbstractHybrid multiagent systems exist widely in real world and have been applied in many engineering fields. In this article, we consider the consensus problem for the hybrid multiagent system consisting of continuous‐time and discrete‐time dynamic agents. Different from the previous works, in this article, the interactions between different agents no longer only occur at the sampling time, but continuous‐time dynamic agents can interact with their continuous‐time dynamic neighbors in real time. By using graph theory and differential mean value theorem of matrix function, a consensus criterion is obtained for the hybrid multiagent system. A unified framework is also established for the consensus of continuous‐time and discrete‐time multiagent systems. Finally, a simulation example is given to illustrate the validity of our protocol.

Consensus of the Hybrid Multiagent System Under Impulse Control

In this brief, we focus on two types of consensus issues of hybrid multiagent system under impulse control. Firstly, the hybrid multiagent system is introduced under impulse control, which includes continuous and discrete dynamic interactions. We use the characteristic of the impulse controller to transform the hybrid system into a single system. By using relevant knowledge of graph theory and matrix theory, the necessary and sufficient conditions for the consensus of the hybrid multiagent system are established. Secondly, taking advantage of impulse control, an index function for evaluating the performance between agents is introduced. It is shown that the hybrid multiagent system can achieve the consensus with guaranteed position performance by combining the coupling weights. Finally, two numerical examples are presented to verify the validity of the main results.

Consensus, cooperative learning, and flocking for multiagent predator avoidance.

Multiagent coordination is highly desirable with many uses in a variety of tasks. In nature, the phenomenon of coordinated flocking is highly common with applications related to defending or escaping from predators. In this article, a hybrid multiagent system that integrates consensus, cooperative learning, and flocking control to determine the direction of attacking predators and learns to flock away from them in a coordinated manner is proposed. This system is entirely distributed requiring only communication between neighboring agents. The fusion of consensus and collaborative reinforcement learning allows agents to cooperatively learn in a variety of multiagent coordination tasks, but this article focuses on flocking away from attacking predators. The results of the flocking show that the agents are able to effectively flock to a target without collision with each other or obstacles. Multiple reinforcement learning methods are evaluated for the task with cooperative learning utilizing function approximation for state-space reduction performing the best. The results of the proposed consensus algorithm show that it provides quick and accurate transmission of information between agents in the flock. Simulations are conducted to show and validate the proposed hybrid system in both one and two predator environments, resulting in an efficient cooperative learning behavior. In the future, the system of using consensus to determine the state and reinforcement learning to learn the states can be applied to additional multiagent tasks.

Testing a hybrid hardware quantum multi-agent system architecture that utilizes the quantum speed advantage for interactive computer music

This paper introduces MIq (Multi-Agent Interactive qgMuse), which builds on the single agent quantum system qgMuse using teleportation. MIq is the first attempt at a real-time interactive quantum computer music algorithm that utilises the quantum advantage. Previous interactive or real-time quantum music algorithms running on quantum computers have been mappings of classical computing algorithms, with no quantum advantage obtained. MIq provides a quadratic speed-up over classical methods. It is a Quantum Hybrid Multi-agent System architecture implemented on 5 and 14 qubit quantum hardware. Classical agents and quantum agents connect via a classical/quantum hybrid agent that enables communication using quantum teleportation.

Second-Order Consensus of Hybrid Multi-Agent Systems With Unknown Disturbances Via Sliding Mode Control

In this paper, we consider the second-order consensus problem of hybrid multi-agent systems with unknown disturbances by using sliding mode control under the leader-follower network. First, the hybrid multi-agent system model with disturbances and nonlinear term is proposed, which is composed of continuous-time dynamic agents and discrete-time dynamic agents. Second, the definition of the second-order consensus of hybrid multi-agent system is given. Then, we assume that the interaction among all agents happens in sampling time and each continuous-time dynamic agent can observe its own states in real time. Based on the equivalent approaching law and the states information among agents, the sliding mode control protocols are designed to achieve the second-order consensus of the hybrid multi-agent system. Some sufficient conditions are given for solving the second-order consensus under the sliding mode control protocols. Finally, some simulations are also given to illustrate the validity of the proposed method.

Hybrid multi‐agent‐based adaptive control scheme for AC microgrids with increased fault‐tolerance needs

This paper presents a fault-tolerant secondary and adaptive primary microgrid control scheme using a hybrid multi-agent system (MAS), capable of operating either in a semi-centralised or distributed manner. The proposed scheme includes a droop-based primary level that considers the microgrid energy reserves in production and storage. The secondary level is responsible for: a) the microgrid units' coordination, b) voltage and frequency restoration and c) calculation of the droop/ reversed-droop coefficients. The suggested architecture is arranged upon a group of dedicated asset agents that collect local measurements, take decisions independently and, collaborate in order to achieve more complex control objectives. Additionally, a supervising agent is added to fulfill secondary level objectives. The hybrid MAS can operate either with or without the supervising agent operational, manifesting fast redistribution of the supervising agent tasks. The proposed hybrid scheme is tested in simulation upon two separate physical microgrids using three scenarios. Additionally, a comparison with conventional control methodologies is performed in order to illustrate further the operation of a hybrid approach. Overall, results show that the proposed control framework exhibits unique characteristics regarding reconfigurability and fault-tolerance, while power quality and improved load sharing are ensured even in case of critical component failure.

Consensus of hybrid multi-agent systems by event-triggered/self-triggered strategy

This paper studies the consensus problem of hybrid multi-agent systems, where the multiple discrete-time and continuous-time dynamic individuals constitute a hybrid multi-agent group. Firstly, a hybrid event-triggered method is proposed. Under this method, the hybrid consensus can be achieved asymptotically. Besides, the Zeno-behavior of the hybrid multi-agent system will not make an appearance due to the special design of the hybrid event-triggered conditions. Then, a self-triggered algorithm is further proposed to avoid the continuous monitoring of event-triggered conditions. Finally, several simulation examples are presented which are comparative and show the effectiveness of the two method. Moreover, the impact of the parameters in the main results is analyzed.

Second-order consensus of hybrid multi-agent systems

It is well known that heterogeneity is an important feature of multi-agent systems. In this paper, we consider the second-order consensus of hybrid multi-agent system which is composed of continuous-time and discrete-time dynamic agents. By analyzing the interactive mode of different dynamic agents, two kinds of effective consensus protocols are proposed for the hybrid multi-agent system. The analysis tool developed in this paper is based on algebraic graph theory and system transformation method. Some necessary and sufficient conditions are established for solving the second-order consensus of hybrid multi-agent system. Two examples are also provided to demonstrate the effectiveness of the theoretical results.

Consensus of hybrid multi-agent systems with heterogeneous dynamics

ABSTRACT This paper addresses the consensus problems for two kinds of hybrid multi-agent systems. The first kind of hybrid multi-agent system is composed of first-order discrete-time and second-order continuous-time dynamical agents. Based on different interactive modes, two effective protocols are presented for the hybrid multi-agent system, respectively. By using graph theory and system transformation method, some criteria are obtained for solving the consensus problem. The second one consists of first-order continuous-time and second-order discrete-time dynamical agents. The effective protocols are also proposed to guarantee the consensus. Meanwhile, the necessary and sufficient conditions are established for solving the consensus problem. Simulation examples are provided to demonstrate the effectiveness of theoretical results.

RESEARCH ON MECHANISM OF REAL-TIME MAS BASED DYNAMIC INTELLIGENT MANUFACTURING SYSTEMS

With the improvement of consumption concept, customer’s demand is becoming more and more dynamic, diversified and personalized, which requires the manufacturing system to be more and more robust and real-time. To achieve such purpose, in this paper, a hierarchical heterogeneous hybrid multi-agent system (MAS) based control architecture is proposed, a MAS-based shop floor scheduling model is designed, an event-driven rolling horizon scheduling mechanism is selected as the scheduling mechanism of the system and an improved contract network protocol is proposed to act as the system’s scheduling algorithm. According to the simulation experiments based on the ever built experimental platform, Internet of Things (IoT) based manufacturing real-time scheduling system is proven to be feasible. DOI: http://dx.doi.org/10.5755/j01.mech.24.1.18631

Consensus of Hybrid Multi-Agent Systems.

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.

H∞Formation Control and Obstacle Avoidance for Hybrid Multi-Agent Systems

A new concept ofH∞formation is proposed to handle a group of agents navigating in a free and an obstacle-laden environment while maintaining a desired formation and changing formations when required. With respect to the requirements of changing formation subject to internal or external events, a hybrid multiagent system (HMAS) is formulated in this paper. Based on the fact that obstacles impose the negative effect on the formation of HMAS, theH∞formation is introduced to reflect the above disturbed situation and quantify the attenuation level of obstacle avoidance via theH∞-norm of formation stability. An improved Newtonian potential function and a set of repulsive functions are employed to guarantee the HMAS formation-keeping and collision-avoiding from obstacles in a path planning problem, respectively. Simulation results in this paper show that the proposed formation algorithms can effectively allow the multiagent system to avoid penetration into obstacles while accomplishing prespecified global objective successfully.

Neural Networks for Real-Time Traffic Signal Control

Real-time traffic signal control is an integral part of the urban traffic control system, and providing effective real-time traffic signal control for a large complex traffic network is an extremely challenging distributed control problem. This paper adopts the multiagent system approach to develop distributed unsupervised traffic responsive signal control models, where each agent in the system is a local traffic signal controller for one intersection in the traffic network. The first multiagent system is developed using hybrid computational intelligent techniques. Each agent employs a multistage online learning process to update and adapt its knowledge base and decision-making mechanism. The second multiagent system is developed by integrating the simultaneous perturbation stochastic approximation theorem in fuzzy neural networks (NN). The problem of real-time traffic signal control is especially challenging if the agents are used for an infinite horizon problem, where online learning has to take place continuously once the agent-based traffic signal controllers are implemented into the traffic network. A comprehensive simulation model of a section of the Central Business District of Singapore has been developed using PARAMICS microscopic simulation program. Simulation results show that the hybrid multiagent system provides significant improvement in traffic conditions when evaluated against an existing traffic signal control algorithm as well as the SPSA-NN-based multiagent system as the complexity of the simulation scenario increases. Using the hybrid NN-based multiagent system, the mean delay of each vehicle was reduced by 78% and the mean stoppage time, by 85% compared to the existing traffic signal control algorithm. The promising results demonstrate the efficacy of the hybrid NN-based multiagent system in solving large-scale traffic signal control problems in a distributed manner

A hybrid multi-agent system architecture for enterprise integration using computer networks

The manufacturing industries are now experiencing fierce pressure of competition from every corner on this planet. In addition, the advancement in computer networks and information technologies has been gradually reshaping the manufacturing companies by shifting from the industrial age to the information era. Due to these elevated competitiveness and advanced computer technology, a number of new manufacturing and management strategies (e.g., CE or CIM) have emerged for the innovation of manufacturing enterprises. Although they have different definitions and scopes, there are several common issues: inter-enterprise functions integration; inter-enterprise resources integration; and collaboration. This paper proposes a new multi-agent system (MAS) architecture to support the inter-enterprise functions/resources integration and collaboration over the networked environment, including the hybrid agent architecture and hybrid network architecture. In contrast to the existing agent architectures, the proposed agent architecture enables agents to exhibit the hybrid (continuous and discrete) behavior and interactions. In addition, our network architecture is more suitable for building the large-scale distributed manufacturing systems that are prone to dynamic random changes of their environment. Based on the proposed MAS architecture, a collaborative product development environment is implemented as a starting point, and a multidisciplinary team-oriented design problem is illustrated to provide the vision of the proposed MAS architecture.