Agent-based Control Research Articles

Robotic Ubiquitous Cognitive Ecology for Smart Homes

Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agent-based control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a proof of concept smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feedback received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work.

Decentralized self-selection of swarm trajectories: from dynamical systems theory to robotic implementation

In this paper, we present a distributed control strategy, enabling agents to converge onto and travel along a consensually selected curve among a class of closed planar curves. Individual agents identify the number of neighbors within a finite circular sensing range and obtain information from their neighbors through local communication. The information is then processed to update the control parameters and force the swarm to converge onto and circulate along the aforementioned planar curve. The proposed mathematical framework is based on stochastic differential equations driven by white Gaussian noise (diffusion processes). Using this framework, there is maximum probability that the swarm dynamics will be driven toward the consensual closed planar curve. In the simplest configuration where a circular consensual curve is obtained, we are able to derive an analytical expression that relates the radius of the circular formation to the agent’s interaction range. Such an intimate relation is also illustrated numerically for more general curves. The agent-based control strategy is then translated into a distributed Braitenberg-inspired one. The proposed robotic control strategy is then validated by numerical simulations and by implementation on an actual robotic swarm. It can be used in applications that involve large numbers of locally interacting agents, such as traffic control, deployment of communication networks in hostile environments, or environmental monitoring.

Multi-Agent Design for the Micro-Grid Demonstration Project

A Micro-Grid (MG) is an energy community having various Distributed Energy Resources (DERs) such as diesel engines, micro turbines, solar power, wind power, fuel cells and energy storage devices. The multi-agent architecture offers a hierarchical structure is very suitable for managing multi-individual and heterogeneous DERs in the MG environment. This paper proposes a Agent-based Demand and Supply Control Framework (AD-SCF) which merges with the existing MG Energy Management System (EMS). Moreover, the AD-SCF adopts the embedding of sensors that can provide environmental and DERs information to EMS. It enhances the exchanges information EMS with the other key elements to determine the best optimal controls on power flow, load dispatch and scheduling in MG.

Management of electric vehicle battery charging in distribution networks with multi-agent systems

An agent-based control system that manages the battery charging of electric vehicles in power distribution networks is presented. The electric vehicle battery charging schedules are calculated according to electricity prices and distribution network technical constraints. The design of the multi-agent system is described. The real-time operation of the multi-agent system was demonstrated in a test-bed of a laboratory micro-grid.

Stability Analysis of an Agent-Based Smart Grid Control Marketplace

While coupling locally dispersed producers and consumers to large distributed networks comes with different socio-economic advantages as increase in production, higher market adaptability and higher resource efficiency, an immanent disadvantage is the accompanying raise in control complexity. Distributed agent-based control approaches are envisioned as a solution for managing distributed and complex production, supply, and infrastructure networks. Nevertheless they are difficult to be analyzed and hard to be handled. The major challenges coming with distributed control solutions may be found in the field of stability problems such as oscillatory network conditions potentially leading to network collapses. In this paper a general modeling and stability analysis approach for networked nodes is presented and applied on a marketplace of an agent-based smart grid system to distribute an energy demand between producers. Finally the analytical results are evaluated on a smart grid simulation.

Coordination of the Charging of Electric Vehicles Using a Multi-Agent System

An agent-based control system that coordinates the battery charging of electric vehicles in distribution networks is presented. The objective of the control system is to charge the electric vehicles at times of low electricity prices within distribution network technical constraints. Search techniques and neural networks are used for the decision making of the agents. The ability of the control system to work successfully when the distribution network is operated within its loading limits and when the loading limits are violated is demonstrated through experimental validation .

Traffic Signal Control and Simulation of Single Intersection Based on Fuzzy Control

In this paper, they are proposed that the Agent-based single intersection traffic control and analysis of the basic structure of a single intersection based on Agent, modeling them. Single intersection is the basic control unit of the entire transportation system. The superior single intersection control is protecting transport network uptime. Agent-based traffic control combines the advantage of fuzzy control in complex transportation system; the fuzzy control technology used in traffic lights control decisions

Multiagent Stochastic Simulation of Minute-to-Minute Grid Operations and Control to Integrate Wind Generation Under AC Power Flow Constraints

The variability and uncertainty inherent to wind generation, which is rapidly increasing, could significantly impact operations efficiency in the future, particularly frequency regulation reserves. This paper addresses these issues from both analytical and curative standpoints through operational impact studies which combine a transmission grid representation with a distributed agent-based control architecture that mimics industry organization charts and follows NERC reliability management rules. As simulating a control area over many years of recorded historical operating conditions is a massive computation problem, the new scheme uses distributed computing with 228 computing nodes to maintain a reasonable simulation time. The simulator-derived automatic generation control and load following generated with a data set characterizing 3000 MW of wind generation integrated in the Quebec interconnection were compared with statistical analysis-based results. The results of the simulation of a full year of minute-by-minute operations suggest that wind integration will increase the number of generating unit start-ups and shut-downs by approximately 5%. The simulator was also able to estimate import/export opportunity losses, as well as several impacts related to voltages and reactive power attributable to increased wind penetration.

Comprehensive Real-Time Microgrid Power Management and Control With Distributed Agents

Advances in smart grid technology have yet to coalesce into a comprehensive solution integrating the landscape of future power systems. The microgrid concept may offer a solution for combining advanced components and enabling technologies within an infrastructure that must expand to meet emerging demands. As autonomous power system entities, microgrids require robust real-time power management and control to simultaneously operate jointly with the utility, provide reliable service, and help achieve customer-driven objectives utilizing local power system assets. In this paper, a decentralized control architecture for microgrids is presented, along with a simulation environment appropriate for on-going investigations into real-time, agent-based decision-making. Challenges faced by operating self-organizing multi-agent system (MAS) are presented, as well as results for a representative power management scenario for a multi-asset microgrid capable of operating in grid-interconnected or islanded mode. The system and formulations presented demonstrate the viability and capability of decentralized agent-based control for microgrids and illustrate their potential towards achieving smart grid goals.

Coordinated agent-based control for online voltage instability prevention

In this paper, a new method for online voltage instability control suitable for smart grid application is proposed. The method uses the Maximum Loadability Index to determine the amount of load relief necessary to restore stability. This measure can be determined based on local measurements. The load relief is obtained by: increase of local generation, virtual load shedding via adjusting the Load Tap Changers (LTCs) set-point, intelligent load control and the increase of reactive power compensation from Static Var Compensators. For the local generation, the control of two types is described: for micro Combined Heat and Power (micro-CHP) units and for large synchronous generators. In order to obtain constant power supply from a group of micro-CHPs, a Virtual Power Plant coordination method is proposed. The control of the actuators is outlined, and a method is described to coordinate their actions. Based on simulations it is demonstrated that the proposed method works properly: voltage collapse can be avoided, and voltage stability is restored. A sensitivity analysis is performed to determine the optimal settings of the coordinated controller. Furthermore, the merit of including reactive power control in the proposed control strategy is investigated. Compared to classical controls, such as Under-Voltage Load Shedding and LTCs tap blocking, the method performs better. Copyright © 2013 John Wiley & Sons, Ltd.

Self-Representation for Self-Configuration and Monitoring in Agent-Based Flexible Automation Systems

Automation systems become more and more complex, and their traditional hierarchical and centralized control may not be able to deal with this ever-increasing complexity satisfactorily. Decentralized approaches are considered promising, but they are not yet sufficiently understood for widespread industrial application. In particular, agent-based control has not yet achieved its potential, so that more research is still required. Our approach is based on the concept of an automation agent composed of a hardware (HW) component and a software (SW) component, where the HW component can be viewed as an embodiment of this SW component. These agents represent the parts of a flexible automation system. In particular, we propose that each such agent has an explicit symbolic representation of itself and of its relations to its environment. More precisely, this new agent architecture contains a reflective world model that includes a representation of this same agent and of its relations to its environment. In addition, the world model contains representations of situations and activities. We show that and how such a self-representation can be used for self-configuration, as well as monitoring, including failure detection in automation systems. The agent performs this self-configuration without any human intervention and just based on a brief specification of the HW configuration. The monitoring is based on automatically raised expectations for the system's behavior and allows failure detection without any specific failure detection code.

Multi-agent Systems vs IEC 61499 for Holonic Resource Control in Reconfigurable Systems

Reconfigurable Manufacturing Systems hold promise to satisfy the requirements of dynamic and competitive modern manufacturing. A holonic control architecture is often used for the control of such reconfigurable systems. In this research, software agents and IEC 61499 function blocks are evaluated as alternative strategies for implementing holonic control for a modular feeder subsystem of an experimental Reconfigurable Assembly System. The strategies are evaluated through four reconfiguration experiments. The evaluation is based on qualitative and quantitative performance measures. The results show that agent-based control is more suitable in this specific case study.

A closed-loop feedback simulation for RFID-based manufacturing planning and control system

Nowadays, the performance of customer service level in manufacturing industries highly depends on an effective manufacturing planning and control system MPCS. An agent-based manufacturing planning and control system AMPCS allows negotiation-based decision-making to achieve the global objective. With the advantage of radio frequency identification RFID technology, agents in AMPCS may effectively control and identify the production progress of all raw materials, work-in-progress WIP, and products across the entire manufacturing supply chain. However, the production schedule may become inefficient or even infeasible due to uncertainties, such as machine breakdowns, processing time delays, rush order, quality problems, and unavailable material. Therefore, this research proposes a closed-loop feedback simulation CLFS approach based on RFID-enabled real-time manufacturing information for scheduling and rescheduling in an agent-based manufacturing system in response to the dynamically changing manufacturing activities and exceptions. From the simulation experiment, the scheduling results proposed by RFID-based MPCS combined with CLFS performed much better than the results obtained from previous researches.

Model-based tracking for agent-based control systems in the case of sensor failures

The study on artificial intelligence (AI) methods for tuning of particle accelerators has been reported in many literatures. This paper presents tuning method for agent-based control systems of transport lines in the case of sensor/actuator failures. The method uses model-based tracking concept to relax the demand on sensor data. The condition for successful operation of the stated scheme is derived, and the concept is demonstrated through simulation by applying it to the model of microtron, transport line-1 and booster of indus accelerator. The results show that this approach is very effective in transport line control during sensor/actuator failures.

Agent-based manufacturing control based on distributed bid selection and publish-subscribe messaging: a simulation case study

Bidding-based negotiation schemes play a major role in multi-agent manufacturing systems research. Despite some concerns with message congestion, researchers have been proposing and studying negotiation schemes based on the contract net protocol (CNP). On the other hand, research in robotics has considered a variant of CNP based on publish-subscribe messaging designed for multi-robot coordination. A distinct feature of this variant involves distributing the bid evaluation and selection functions among robot agents. This paper discusses our adaptation of this design variant for multi-agent manufacturing systems and examines its performance implications. Using discrete-event simulation, we study how the adapted CNP design can help address the message congestion problem by cutting down on negotiation slack time. Our case study results show that it can enhance the resilience of the agent negotiation process to message congestion, thereby contributing to the overall performance of a multi-agent manufacturing system.

A New Intelligent Agent-Based AGC Design With Real-Time Application

Automatic generation control (AGC) is one of the important control problems in electric power system design and operation, and is becoming more significant today because of increasing renewable energy sources such as wind farms. The power fluctuation caused by a high penetration of wind farms negatively contributes to the power imbalance and frequency deviation. In this paper, a new intelligent agent-based control scheme, using Bayesian networks (BNs), is addressed to design AGC system in a multiarea power system. Model independence and flexibility in specifying the control objectives identify the proposed approach as an attractive solution for AGC design in a real-world power system. The BN also provides a robust probabilistic method of reasoning under uncertainty, and moreover, using multiagent structure in the proposed control framework realizes parallel computation and a high degree of scalability. The proposed control scheme is examined on the 10-machine New England test power system. An experimental real-time implementation is also performed on the aggregated model of West Japan power system.

An agent-based framework for collaboration among critical mechanical devices working in a cluster

Critical mechanical devices, such as high-pressure air compressors, are crucial for industrial production. Critical mechanical devices are independently distributed devices that work in a cluster, but have high power requirements and need large amounts of space. Thus, the efficient control and coordination of a cluster of critical mechanical devices to achieve task goals is beset with difficulties. Motivated by an example of high-pressure air compressors in a cluster, this paper proposes an agent-based framework for realizing collaboration among critical mechanical devices that work in a cluster. The proposed agent-based framework endows critical mechanical devices with abilities such as autonomy, interaction and collaboration. The framework comprises three core components: (i) an agent-based control approach to the design of critical mechanical device agents to create autonomy; (ii) a type of critical mechanical device agent communication language to create interaction among critical mechanical devices; and (iii) a total working time-based collaboration algorithm that fully considers the physical characteristics of critical mechanical devices to create collaboration among critical mechanical device clusters. A type of critical mechanical device agent controller is implemented using the proposed approach. The agent-based framework has been successfully applied and evaluated in a real-world project. The results obtained in that case study show that the proposed framework can achieve collaboration among critical mechanical device clusters allowing them to realize their predefined goals efficiently and safely.

A Survey on Applications of Agent Technology in Industrial Process Control

The agents and multiagent systems technology is actively researched by the academia and industrial community. However, the technology is particularly popular in the manufacturing domain, while the applications in other domains of industrial control are scarce. This survey focuses exclusively on the technology applications in the automation of continuous industrial processes, as the differences in the technology adoption between the process automation and manufacturing are significant. A large part of the literature on the subject is reviewed. The analysis of the literature is provided from several points of view, the main trends of research are described, including the shift of the researchers' interest from the agent-based supervisory control to the low-level agent-based control algorithms. Conclusions are provided regarding the lack of the technology support on the part of control instrumentation vendors; probable directions of the development are indicated, which turn out to be especially promising in the domain of biotechnological processes.

Agent Based Infrastructure with RFID Technology for Autonomous Shop Floor Control System

Automatic shop floor control system in distributed computing context is one of the most important links in Computer Integrated Manufacturing (or CIM) system. For most agent-based manufacturing control systems, a real-time product data available mechanism is not considered. In this paper, an integrated approach of RFID technology and multi-agent system of shot-floor control is presented. The proposed framework uses RFID for tracking products in the system and also for attaching product manufacturing information. This shift from off-line planning systems to on-line control systems will facilitate implementations of real-time scheduling requirement in autonomous manufacturing environment.

Semantic technologies: latest advances in agent-based manufacturing control systems

The manuscript reports on the latest advancements in the field of multi-agent based industrial control systems. It is declared that this area is becoming strongly influenced by the recent massive proliferation of semantic technologies in various fields of software engineering. The application of ontologies for expressing semantics of data does not restrict any longer exclusively on semantic web or semantic web services. Ontologies are being applied for advanced handling, exchanging and reasoning about knowledge in the area of industrial automation, especially in the case of production systems based on multi-agent technology. The paper provides a comprehensive survey of the application of ontologies in agent-based control systems and discusses the common characteristics of such integration. The generic ontology designed for discrete manufacturing, which covers ordering, production planning and material handling aspects is presented. In addition, the framework for integration of this ontology in distributed, agent-based control solutions is given. A proof-of-concept demonstrating the advantages and drawbacks of such integration in a real manufacturing scenario is provided.