The study on the multi-rigid body dynamics for the multi-agent polishing system

This paper puts forward a multi-Agent alliance control model that is made up of the series robot Agent, the parallel moving platform Agent and the polishing tool system Agent and so on by organically introducing the MAS (Multi-Agent System) theory into the polishing. Through the introduction of point-curve constraint, the dynamics equation of the constraint multi-body for the polishing system is established in order to accurately program the moving track of the robots. Meanwhile, the control parameters during the polishing can be optimized and the foundation can be afforded for carrying out the collocation of the optimal parameters with the series-parallel coordinates polishing and programming the optimal path on the free-form surface.

As the complexity and scale of modern buildings increase, there is a growing need for efficient, scalable, and flexible control systems to manage the myriad of sensors, actuators, and meters involved in building automation. Traditional building management systems (BMS) struggle with scalability, especially when dealing with multi-agent control setups that require extensive manual configuration. This limitation poses significant challenges in deploying advanced control strategies across different building environments, leading to inefficiencies and underutilization of building automation technologies.Previous research has explored various aspects of multi-agent systems (MAS) in building automation, including agent-based modeling, decentralized control strategies, and the application of machine learning to optimize building operations. However, much of the existing work has focused on small-scale implementations or specific applications, such as energy management or HVAC control. The problem of automating the setup of multi-agent control systems to adapt to different building environments with minimal manual intervention remains largely unaddressed.The aim of this work is to develop a scalable framework for the automated setup of multi-agent control systems in buildings. This framework will enable the seamless integration of sensors, actuators, and meters into a unified control system, significantly reducing the need for manual configuration. The framework will leverage large language models (LLMs) for semantic tagging and context understanding, thereby facilitating the automated configuration of action and input spaces for individual agents within the control system.The proposed framework will consist of three key components: (1) an LLM-based system for semantic tagging and context interpretation, (2) an automated setup process for defining the action and input spaces of the agents, (3) and an initial offline training of the agents. The LLM will be trained on a diverse dataset of building management setups to ensure it can accurately tag and interpret various building elements. The system will be tested in real-world office buildings to evaluate its scalability, accuracy, and effectiveness.Preliminary simulations indicate that the automated setup framework can significantly reduce the time and effort required to deploy multi-agent control systems in buildings. We expect that this framework will enable more scalable and flexible building management systems, ultimately leading to improved energy efficiency, occupant comfort, and overall operational performance. The significance of this work lies in its potential to transform the deployment of building automation systems, making advanced control strategies more accessible and cost-effective across various building types and scales.

This paper proposes a methodology for developing multi-agent systems. Several design considerations for robotic agents are discussed. A script specifies a multi-agent system by defining the behavior of each agent. An agent is modeled by an extended finite state automaton. By using the state transition rules as the building blocks, an agent as well as a multi-agent system can be developed efficiently. This methodology is further applied to multi-agent robotic systems in which reactivity and interaction are important issues. A script-based architecture is developed to implement the agents in multi-agent robotic systems. This architecture considers not only the control architecture of a single agent, but also the interactions among multiple agents and their environments. A script for the object-sorting task problem is utilized for demonstration.

Development of complicated products is a project of system engineeringIt involves extensive and complicated knowledge,design meth ods and auxiliary technologyVarious factors affect each otherSo,modern produ ct development is a typical group problem with distributed and dynamic features It is apparent superiority to solve this problem with a multiagent system re presenting various knowledge domainsDistributed artificial intelligence knowle dge being used,the multiagent collaborative design system concept and model ba sed on Internet environment are put forwardThe realizing method of product developing agents,interactive process among multiagents,and organiz ation and implementing of the design project of the multiagent collaborative d esign system are discussed in detailApplication examples are also presented

Nowadays developing methods for distributed computing is received much attention. One of the methods of distributed computing is using of multi-agent systems. The organization of distributed computing based on the conventional network computers can experience security threats performed by computational processes. Authors have developed the unified agent algorithm of control system of computing network nodes operation. Network PCs is used as computing nodes. The proposed multi-agent control system for the implementation of distributed computing allows in a short time to organize using of the processing power of computers any existing network to solve large-task by creating a distributed computing. Agents based on a computer network can: configure a distributed computing system; to distribute the computational load among computers operated agents; perform optimization distributed computing system according to the computing power of computers on the network. The number of computers connected to the network can be increased by connecting computers to the new computer system, which leads to an increase in overall processing power. Adding multi-agent system in the central agent increases the security of distributed computing. This organization of the distributed computing system reduces the problem solving time and increase fault tolerance (vitality) of computing processes in a changing computing environment (dynamic change of the number of computers on the network). Developed a multi-agent system detects cases of falsification of the results of a distributed system, which may lead to wrong decisions. In addition, the system checks and corrects wrong results.

A test bed for multiagent control systems in road traffic management is presented. As the complexity of traffic control on a network grows, it becomes more difficult to coordinate the actions of the large number of heterogeneous traffic management instruments that are available in the network. One way of handling this complexity is to divide the coordination problem into smaller coherent subproblems that can be solved with a minimum of interaction. Multiagent systems can aid in the distribution of the problem (over the various agents that compose the multiagent system) and facilitate the coordination of the activities of these agents when required. In the literature, no consensus exists about the best configuration of the traffic-managing multiagent system and how the activities of the agents that compose the multiagent system should be coordinated. The decomposition of a problem into various subproblems is an active field of research in the world of distributed artificial intelligence. A survey of approaches reported in the literature is presented. Subsequently, both the test bed and the modules that compose it are introduced. Finally, an application is presented that illustrates some of the research the test bed has made possible.

In this paper, we present a multi-agent information system model that can deal with complex multi-activity information-driven tasks. After defining a single information agent architecture in an abstract way, we describe the multi-agent model (called the parallel model), as well as a simple collaboration protocol for the multiple activity information agents. Experimental results are presented with the multi-agent system that demonstrate that a significantly larger number of relevant documents can be retrieved through collaboration than what is possible with decoupled agents. Hence, such multi-agent parallel models appear to hold considerable promise in providing the user with high quality information services in a variety of business and scientific information-driven tasks.

This work proposes a residential load management system using multiagent technology with the objective of cost and comfort management. Smart appliances in a residential unit are modelled as agents which are controlled using optimization algorithm. These agents cooperate and communicate with each other using agent communication language (ACL) to reduce electricity cost and high peaks without affecting end user comfort. In this regards, different types of agents including load agents, price agent, temperature agent, management agent and optimal stopping rule (OSR) agent are considered. To make agent behaviour intelligent and adaptive, OSR is used. Simulations are conducted to provide the overview of residential load management using multiagent system (MAS). Simulation results show the effectiveness of MAS in terms of electricity cost, user comfort and peak shaving.

A multi-agent controller on embedded system is presented for complex mechatronics in this paper. The agents' concept is combined with a physical control system to produce a controller agent. The multi-controller system of complex mechatronics can be regarded as a series of controller agents, and they can be converted to the Multi-Agent System (MAS) easily by changing corresponding concepts. Controller agents can be implemented by a number of agent tasks located on different microprocessors at the hardware level and coordinated by a Real-Time Operating System (RTOS) at the software level. In the view of control agents and MAS, an embedded control system with ARM and DSP is designed and developed for a vision-based Automated Guided Vehicle (AGV), from the agents' partitions to the MAS design, and from the hardware construction to the software programming. This development paradigm demonstrates an effective and concise implementation way in which the MAS-based approach is adopted on the embedded control system. It is expected to help the multi-controller system design for complex mechatronics.

Distributed consensus tracking for linear multiagent systems (MASs) with directed switching topologies and a dynamic leader is investigated in this paper. By fully considering the special feature of Laplacian matrices for topology candidates, several new classes of multiple Lyapunov functions (MLFs) are constructed in this paper for leader-following MASs with, respectively, an autonomous leader and a nonautonomous leader. Under the condition that each possible topology graph contains a spanning tree rooted at the leader node, some efficient criteria for achieving consensus tracking in the considered MASs are provided. Specifically, it is proven that consensus tracking in the closed-loop MASs can be ensured if the average dwell time for switching among different topologies is larger than a derived positive quantity and the control parameters in tracking protocols are appropriately designed. It is further theoretically shown that the present Lyapunov inequality based criteria for consensus tracking with an autonomous leader are much less conservative than the existing ones derived by the M-matrix theory. The results are then extended to the case where the topology graph only frequently contains a directed spanning tree as the MASs evolve over time. At last, numerical simulations are performed to illustrate the effectiveness of the analytical analysis and the advantages of the proposed MLFs.

Multi-agent models have been used to simulate complex systems in many domains. In some models, the agents move in a physical/grid space and are constrained by their locations on the spatial space, e.g. Sugarscape. In others, the agents interact in a social multi-dimensional space and are bound to their knowledge and social positions, e.g. Construct. However, many real world problems require a mixed model containing both spatial and social features. This paper introduces such a multi agent system, Construct-Spatial, which simulates agent communication and movement simultaneously. It is an extended version of Construct, which is a multi-agent social model, and its extension is based on a multi-agent grid model, Sugarscape. To understand the impact of this integration of the two spaces, we run virtual experiments and compare the output from the combined space to those from each of the two spaces. The initial analysis reveals that the integration facilitates unbalanced knowledge distribution across the agents compared to the grid-only model and limits agent network connections compared to the social network model without spatial constraints. After the comparisons, we setup what-if scenarios where we varied the type of the threats faced by network and observe their emergent behaviors. From the what-if analyses, we locate the best destabilization scenario and find the propagation of the effects from the spatial space to the social network space. We believe that this model can be a conceptual model for assessing the efficiency and the robustness of team deployments, network node distributions, sensor distributions, etc.

For a class of second order linear dynamic multiagent systems with leader and bounded input disturbance, a multi-agent system model is established by graph theory and matrix theory knowledge. A second-order sliding mode control algorithm based on finite state structure is applied to mult-iagent system. Under the condition that the first derivative of sliding mode is unknown, the control output is generated by switching between multiple state machines according to the measured value of sliding mode, so that the sliding mode and its first derivative converge to zero in a finite time, and to achieve the goal that the multi-agent system can be consistent for a limited period of time. The algorithm weakens the chattering problem in the traditional sliding mode control, and improves the accuracy and robustness of the control system. The effectiveness of the algorithm is proved by simulation experiments.

The present paper proposes a multi-agent control system for rail transit. The multi-agent system (MAS) consists of multiple train agents, station agents and a central agent. The implementation of the proposed system is based on distributed optimal control. In the proposed system, each train agent can directly obtain information from the neighboring train agents. Each train agent consists of five subsystems, which are train data set, safety inspection system, timetable inspection system, energy optimization system and trajectory generate system. The train agent can optimize the speed trajectory according to the running state of adjacent trains with the cooperation of the five subsystems. The built-in algorithm of the proposed system can infer the driving strategy (such as time-priority or distance-priority) that should be adopted based on specific situations, which provides a good anti-disturbance ability. Furthermore, the distributed optimization method enables the system to perform a multi-objective optimization in a short time when the system is disturbed.

This paper proposes a multi-agent based control system for real-time and adaptive Volt/VAR Optimization (VVO) and Conservation Voltage Reduction (CVR) in Smart Substations. The design and implementation of the proposed distributed control system using agent technology is discussed in the paper. Furthermore, the architecture, tasks and limits of each Intelligent Agent (IA) as a component of a multi-agent system (MAS) have been explained. A number of control functions are simulated and the results are presented in the paper. The results obtained demonstrate the potential of MAS for improving the efficiency of the system.

Understanding and modeling behavior of multi-agent systems is a central step for artificial intelligence. Here we present a deep generative model which captures behavior generating process of multi-agent systems, supports accurate predictions and inference, infers how agents interact in a complex system, as well as identifies agent groups and interaction types. Built upon advances in deep generative models and a novel attention mechanism, our model can learn interactions in highly heterogeneous systems with linear complexity in the number of agents. We apply this model to three multi-agent systems in different domains and evaluate performance on a diverse set of tasks including behavior prediction, interaction analysis and system identification. Experimental results demonstrate its ability to model multi-agent systems, yielding improved performance over competitive baselines. We also show the model can successfully identify agent groups and interaction types in these systems. Our model offers new opportunities to predict complex multi-agent behaviors and takes a step forward in understanding interactions in multi-agent systems.