Multi-agent Control Research Articles

A Multi-Agent Control for Energy Management in PV/Wind/Fuel Cell/Battery Hybrid Power System

The usage of renewable energy systems increases worldwide due to extinction of conventional sources and also the absence of some serious environmental effects such as global warming, ozone layer depletion etc. These renewable power systems are not able to satisfy the load continuously due to seasonal availability of the resources. A Hybrid Power System (HPS) formed with renewable energy sources are a solution to provide power for stand-alone electrical loads. However, the energy management in HPS is quite complex as it relies on a central controller. This paper proposes a distributed Energy Management System (EMS) to control the energy flow in the PV/Wind/Fuel Cell/Battery HPS based on multi-agent system (MAS) technology. With this concept, a HPS is seen as a collection of different elements called agents, collaborates to reach a global coordination to satisfy the demand in the system. The Algorithm of the Multi-Agent System technique for HPS has been implemented using MATLAB/Simulink environment. The results show that the algorithm is effectively working for a HPS to provide power to the load and control power flow between various elements of the system.

Multi-Agent Behavioral Control System for the Ore Grinding Process

In this paper, a small multi-agent system (MAS) is proposed based on behavioral approach for the complex grinding processes. Causal association agents were established according to the material balance of grinding processes, and prediction agents and stability control agents were built by adding prediction and control algorithms. The simulation results prove that the system has good stability and anti-interference performance. Multi-agent behavioral control method can be considered as a potential solution to stabilize grinding processes.

Multi-agent Remote Sensing Image Segmentation Algorithm

Due to fractal network evolution algorithm (FNEA) in the treatment of the high spatial resolution remote sensing image (HSRI) using a parallel global control strategies which limited when the objects in each cycle by traversal of and not good use the continuity of homogenous area on the space and lead to problems such as bad image segmentation, therefore puts forward the remote sensing image segmentation algorithm based on multi-agent. The algorithm in the merger guidelines, combining the image spectral and shape information, and by using region merging process of multi-agent parallel control integral, its global merger control strategy can ensure algorithm has the advantages of parallel computing and fully considering the regional homogeneity, and continuity. Finally simulation experiment was performed with FNEA algorithms, experimental results show that the proposed algorithm is better than FNEA algorithm in dividing the overall effect, has a good stability

Sensor deployment for pipeline leakage detection via optimal boundary control strategies

We consider a multi-agent control problem using PDE techniques for a novel sensing problem arising in the leakage detection and localization of offshore pipelines. A continuous protocol is proposed using parabolic PDEs and then a boundary control law is designed using the maximum principle. Both analytical and numerical solutions of the optimality conditions are studied.

Stochastic optimized intelligent controller for smart energy efficient buildings

Abstract Smart buildings are rising concept for energy resource management in the buildings to reduce energy consumption and wastage. Energy management and control system comprises of variety of technologies, which affect human working comfort. The challenging task of the building control is to achieve interior building environment comfort with high-energy efficiency. In this study, multi-agent control system has been developed in combination with stochastic optimization using genetic algorithm (GA). The corresponding simulations of effective management of energy and consumer comfort are presented. The developed control system provides significant improvement in energy consumption and interior environmental comfort in smart buildings.

Decentralized manufacturing management by a multi-agent optimal control method

A supply chain management system is defined as communications among suppliers, plants, distribution centres, retailers and demand stimulus. These systems are large scale and multi-agent, and therefore a decentralized control method must be used. Also, demand forecasting, as a challenge in production management, can be estimated by advanced methods or modelled using demand forecasting functions. In this paper, a new decentralized receding horizon control method is used to achieve customer contentment and low-cost inventory in a complete chain of supply, manufacture, assembly, warehouse, distribution and retail units. The main novelty of the method returns to the use of both the move suppression term and the look-ahead idea to increase robustness and smoothness in a supply chain containing assembly units. Also, a Kalman filter estimator is applied to estimate states and output variables. For this purpose, a suitable model and appropriate optimal control method are developed. Finally, the efficiency is indicated regarding simulation results.

Rule pool updating through Sarsa‐learning to improve adaptability in changing environments

AbstractGenetic network programming (GNP) is a new evolutionary algorithm using the directed graph as its chromosome. A GNP‐based rule accumulation (GNP‐RA) method was proposed previously for multiagent control. However, in changing environments where new situations appear frequently, the old rules in the rule pool become incompetent for guiding the agent's actions, and therefore updating them becomes necessary. This paper proposes a more robust rule‐based model which can adapt to the environment changes. In order to realize this, Sarsa‐learning is used as a tool to update the rules to cope with the unexperienced situations in new environments. Furthermore, Sarsa‐learning helps to generate better rules by selecting really important judgments and actions during training. In addition, the ε‐greedy policy of Sarsa enables GNP‐RA to explore the solutions space sufficiently, generating more rules. Simulations on the tile world problem show that the proposed method outperforms the previous ones, namely GP and reinforcement learning. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Distributed nonlinear control of mobile autonomous multi-agents

This paper studies the distributed nonlinear control of mobile autonomous agents with variable and directed topology. A new distributed nonlinear design scheme is presented for multi-agent systems modeled by double-integrators. With the new design, the outputs of the controlled agents asymptotically converge to each other, as long as a mild connectivity condition is satisfied. Moreover, the velocity (derivative of the output) of each agent can be restricted to be within any specified neighborhood of the origin, which is of practical interest for systems under such physical constraint. The new design is still valid if one of the agents is a leader and the control objective is to achieve leader-following. As an illustration of the generality and effectiveness of the presented methodology, the formation control of a group of unicycle mobile robots with nonholonomic constraints is revisited. Instead of assuming the point-robot model, the unicycle model is transformed into two double-integrators by dynamic feedback linearization, and the proposed distributed nonlinear design method is used to overcome the singularity problem caused by the nonholonomic constraint by properly restricting the velocities. Simulation results are included to illustrate the theoretical results.

Multi-agent consensus under a communication–broadcast mixed environment

This paper addresses multi-agent control under an environment where both agent-to-agent communication and one-to-all broadcast are available. The problem studied here is a consensus problem for a pre-specified location, i.e. collecting the agents at a desired location in an n-dimensional space, with a limited communication range and an unlimited broadcast range, which is a nice example to be solved under the mixed environment. For solving this, we first introduce a concept of the connected agent group. Using this notion, the problem is reduced to a consensus problem for the group-to-group relation and that for the agent-to-agent relation in the groups, from which we derive a controller achieving the consensus with probability 1. Finally, the performance of the proposed controller is demonstrated by numerical simulation.

Multi-agent Control of High Redundancy Actuation

The high redundancy actuator (HRA) concept is a novel approach to fault tolerant actuation that uses a high number of small actuation elements, assembled in series and parallel in order to form a single actuator which has intrinsic fault tolerance. Whilst this structure affords resilience under passive control methods alone, active control approaches are likely to provide higher levels of performance. A multiple-model control scheme for an HRA applied through the framework of multi-agent control is presented here. The application of this approach to a 10 × 10 HRA is discussed and consideration of reconfiguration delays and fault detection errors are made. The example shows that multi-agent control can provide tangible performance improvements and increase fault tolerance in comparison to a passive fault tolerant approach. Reconfiguration delays are shown to be tolerable, and a strategy for handling false fault detections is detailed.

Multi-agent control of community and utility using Lagrangian relaxation based dual decomposition

In multi-agent based demand response program, communities and a utility make decisions independently and they interact with each other with limited information sharing. This paper presents the design of multi-agent based demand response program while considering ac network constraints. This project develops two types of information sharing and iterative decision making procedures for the utility and communities to reach Nash equilibrium. The distributed algorithms of decision making are based on Lagrangian relaxation, duality, and the concept of upper and lower bounds. The first algorithm is subgradient iteration based distributed decision making algorithm and the second algorithm is based on lower bound and upper bound switching. The two algorithms require different information flow between the utility and communities. With the adoption of distributed algorithms, the utility solves optimal power flow at each iteration while considering ac network constraints, and the communities also conduct optimization. Through information sharing, the utility and the communities update their decisions until convergence is reached. The decision making algorithms are tested against three test cases: a distribution network IEEE 399 system, two meshed networks (IEEE 30-bus system and IEEE 300-bus system). Fast convergence is observed in all three cases, which indicates the feasibility of the demand response design.

406 組み立て工程におけるエージェントベース制御システム(OS5 : 製造の見える化(2))

406 組み立て工程におけるエージェントベース制御システム(OS5 : 製造の見える化(2))

Active noise control system via multi-agent credit assignment

Multi-Agent systems have proved powerful in various sciences and engineering problems. This paper proposes a novel Multi-Agent Active Noise Control ANC formulation via the credit assignment approach. The introduced ANC removes multi-tonal acoustic noises in the environment invoking reinforcement learning techniques. In some multi-agent systems, for the training of all agents, only one reward is available. It is clear that this reward does not belong to one particular agent. The assignment of this reward to the agents is a problem which is known as Multi-Agent Credit Assignment MCA. In this research, each agent is responsible for reducing the noise power of one single harmonic, while only the total noise power of the signals is known. Therefore, it is required to assign a power contribution to each single harmonic. To resolve this problem, at first, the Knowledge Evaluation Based Critic Assignment KEBCA idea with proper modification is used and then a new method is introduced for this special problem. Simulation results show good improvement in the system performance by switching the single agent into the multi-agent system.

A Coordinated Control Strategy of Voltage Regulation in Power System based on Multi-Agent System

The development of power system from centralized to decentralized control provides a great opportunities for multi agent control. This paper presents a coordination algorithm for power systems based on concept of agents and multi agent systems. Agents provides the ability to execute better controllability on large scale systems including power systems. Here, multi agent systems is defined to regulate voltage of the power system. Tertiary and secondary voltage control that applying controllers to regulate the voltage in the power system are designed by agents. Voltage can be regulated using agents to support security of the power system and avoid fluctuation.

Decentralised formation control and stability analysis for multi-vehicle cooperative manoeuvre

Multi-vehicle cooperative formation control problem is an important and typical topic of research on multi-agent system. This paper presents a formation stability conjecture to conceive a new methodology for solving the decentralised multi-vehicle formation control problem. It employs the “extension-decomposition-aggregation” scheme to transform the complex multi-agent control problem into a group of sub-problems which is able to be solved conveniently. Based on this methodology, it is proved that if all the individual augmented subsystems can be stabilised by using any approach, the overall formation system is not only asymptotically but also exponentially stable in the sense of Lyapunov within a neighbourhood of the desired formation. Simulation study on 6-DOF aerial vehicles (Aerosonde UAVs) has been performed to verify the achieved formation stability result. The proposed multi-vehicle formation control strategy can be conveniently extended to other cooperative control problems of multi-agent systems.

Self Organizing Networks: A Reinforcement Learning approach for self-optimization of LTE Mobility parameters

With the evolution of broadband mobile networks towards LTE and beyond, the support for the Internet and Internet based services is growing. Self Organizing Network (SON) functionalities intend to optimize the network performance for the improved user experience while at the same time reducing the network operational cost. This paper proposes a Reinforcement Learning (RL) based framework to improve throughput of the mobile users. The problem of spectral efficiency maximization is modeled as co-operative Multi-Agent control problem between the neighbouring eNodeBs (eNBs). Each eNB has an associated agent that dynamically changes the outgoing Handover Margin (HM) to its neighbouring cells. The agent uses the RL technique of Fuzzy Q-Learning (FQL) to learn the optimal mobility parameter i.e., HM value. The learning framework is designed to operate in an environment with the variations in traffic, user positions and propagation conditions. Simulation results have shown the proposed approach improves the network capacity and user experiences in terms of throughput.

Multi-agent Control of Customer's Loads for Influence of Renewable Energy on the Power System

Multi-agent Control of Customer's Loads for Influence of Renewable Energy on the Power System

Multi-agent Control Approach for Autonomous Mobile Manipulators: Simulation Results on RobuTER/ULM

This article presents a multi-agent approach for controlling autonomous mobile manipulators. The proposed approach assigns a hybrid agent (Mobile base agent) for the control of the mobile base, a reactive agent (Joint agent) to each degree-of-freedom (dof) of the manipulator, and a Supervisory agent to assure coordination and to synchronize the work of the whole agents of the system.The initial simulation results, obtained via different positioning tasks on RobuTER/ULM with and without considering breakdowns, show that the main advantage of such an approach is that it pledges a fault-tolerant response to various types of breakdowns without adding any specific dysfunction treatment.

The design conception of multipurpose underwater tethered systems with centralized data exchange

The structure and design features of a typical tethered underwater system are given. The design complexity, limitations on the installation of additional equipment and the need for the creation of multi-purpose tethered underwater systems are shown. Logic modules, of which almost all tethered underwater systems consist: actuating mechanisms and sensors of the underwater robotic vehicle, control and display devices of the control station, documenting devices, are singled out. The concept of creating multi-purpose tethered underwater systems is developed, which allows their prompt modification at the stages of design and operation. The modular construction of the tethered underwater system with the centralized information exchange for the implementation of the developed concept is proposed. Application of the developed concept allows to integrate modern systems of automatic control into the tethered underwater system, perform full information documenting, realize remote and multi-agent control, apply satellite and network-centric technologies, study the prototypes of the latest specialized attached implements.

Distributed Software Architecture Enabling Peer-to-Peer Communicating Controllers

This paper presents a novel model-driven software architecture for systems with high degree of redundancy and modularity of the equipment. The architecture is based on totally decentralized control. It combines adaptability and robustness of multi-agent control architectures with portability and interoperability benefits of IEC 61499 function block architecture. The architecture has been successfully proven feasible on a number of field trials, including modeling and implementation of a medium-scale airport baggage handling control. Deployment was done on distributed networks consisting of configurations ranging from a few to dozens of communicating control nodes. The work confirmed the ability to deliver similar functional characteristics as centralized systems but in a distributed implementation. Performance testing and development verified sufficient performance and software life-cycle benefits.