Agent-based Control Research Articles

Comparative assessment of HVAC control strategies using personal thermal comfort and sensitivity models

Research efforts have demonstrated the potentials of improving the performance of Heating, Ventilation, and Air-Conditioning (HVAC) systems by leveraging personalized thermal comfort preferences and profiles. However, there are remaining challenges for effective control in collective conditioning in multi-occupancy scenarios. In this study, we have investigated the impact of personal thermal comfort sensitivities – distinct individual reactions to temperature variations– on collective conditioning. To this end, we have explored whether taking the thermal comfort sensitivity into account influences the selection of temperature setpoints and the overall probability of achieving comfort. We have also examined the impact of different thermostat temperature resolutions (0.1, 0.5, and 1.0 °C) on these factors with a hypothesis that finer resolutions could aid in achieving improved overall thermal comfort. In doing so, we have proposed an agent-based control mechanism to simulate the multi-occupancy space, controlled by an HVAC agent to provide air conditioning for multiple human agents using three operational strategies to compare conventional strategies with our proposed approach. The first strategy relies on majority thermal votes, the second one relies on the gap between thermal preferences (i.e., preferred temperature) and ambient temperature, and the third strategy uses thermal comfort sensitivity in addition to preferences. The investigations were conducted by using stochastically modeled comfort profiles (six actual comfort profiles and 15 mathematically synthesized profiles from actual data). These profiles were used to model the behavior of human agents in diverse multi-occupancy scenarios, modeling two to ten occupants in a space for different thermostat temperature resolutions. Our investigations demonstrated that thermal comfort sensitivity plays a statistically significant role in collective conditioning as it resulted in changes of temperature setpoint in 86% of cases and a higher probability of achieving collective comfort.

Simulation of glucose regulating mechanism with an agent-based software engineering tool

This study provides a detailed explanation of a regulating mechanism of the blood glucose levels by an agent-based software engineering tool. Repast Simphony which is used in implementation of this study is an agent-based software engineering tool based on the object-oriented programming using Java language. Agent-based modeling and simulation is a computational methodology for simulating and exploring phenomena that includes a large set of active components represented by agents. The agents are main components situated in space and time of agent-based simulation environment. In this study, we present hormonal regulation of blood glucose levels by our improved agent-based control mechanism. Hormonal regulation of blood glucose levels is an important process to maintain homeostasis inside the human body. We offer a negative feedback control mechanism with agent-based modeling approach to regulate the secretion of insulin hormone which is responsible for increasing the blood glucose levels. The negative feedback control mechanism run by three main agents that interact with each other to perform their local actions in the simulation environment. The result of this study shows the local behavior of the agents in the negative feedback loop and illustrates how to balance the blood glucose levels. Finally, this study which is thought a potential implementation of agent-based modeling and simulation may contribute to the exploration of other homeostatic control systems inside the human body.

Using distributed agents to optimize thermal energy storage

Abstract The Intelligent Building Agents Laboratory (IBAL) at the National Institute of Standards and Technology (NIST) will be used to develop and evaluate advanced control systems for commercial building heating, ventilation, and air conditioning (HVAC) applications. Advanced control techniques include model predictive control (MPC) and distributed optimization, both of which can utilize machine learning. This study is a first step in the development of more advanced techniques. MPC is used to generate an optimal schedule for the operation of a cooling plant consisting of ice-on-coil thermal energy storage and two chillers with different maximum capacities. The schedule is generated offline and then used in the IBAL. The performance of the actual system is used to validate the simulation. The simulation is then used to conduct studies relating to the effect of the time horizon and electrical tariff on the optimal schedule. The results demonstrate that an agent-based control architecture is effective in defining optimal control of thermal energy storage systems.

A Hybrid Methodology for Validation of Optimization Solutions Effects on Manufacturing Sustainability with Time Study and Simulation Approach for SMEs

The properties of small- and medium-sized enterprises (SMEs) make them one of the most important categories of enterprises for the economics of challenging world. SMEs, in most countries, are still enterprises with marketing and financial challenges. In addition, most of these challenges are related to their production and product characteristics. On the other hand, SMEs should fulfil the costumer’s demands. In order to reach these goals, SMEs must reach the highest level of production quality and quantity and successfully sustain them. Consequently, various manufacturing paradigms have been offered by an Industry 4.0 concept, which offers a variety of solutions to increase the productivity and enhance the performance of SMEs. It should be noted that implementation of these manufacturing paradigms for SMEs is quite difficult and sometimes risky for several reasons. Still, amidst all these difficulties and challenges, the benefits and idealism of the Industry 4.0 paradigms prevail. From productivity to market, it is difficult to deny that SMEs are frightened by the challenges they face and fleeing from the potential of overcoming them. This paper is an extended version of the research by Ghafoorpoor Yazdi et al. (2018) and conducts a hybrid methodology to satisfy the SMEs by validating and verifying any optimization idea before implementing the Industry 4.0 concept. To reach the study goals, an intelligent Material Handling System (MHS) with agent-based control architecture has been developed. The developed MHS has been utilized for auto parts distribution. The system performance has been evaluated, and some solutions have been provided to optimize the performance of system. To evaluate the target system’s performance, an analytical time study method has been utilized. The time study has an Overall Equipment Effectiveness (OEE) standard approach to identifying the matters that need to be resolved and optimized to increase system performance. The other part of the methodology is generating a simulation model of the real system by use of ARENA® software to evaluate the system’s performance before implementing the optimization idea and modifying the real system. Furthermore, as the sustainability strategies create many synergistic effects for SMEs, after evaluating the effects of the optimization ideas on OEE percentage, the influence of the OEE changes on manufacturing sustainability has been investigated. The results show that optimizing the OEE in SMEs with sustainability approaches can create competitive advantages, rather than simply focusing on reducing unsustainability.

Agent-based Control System: A Review and Platform for Reconfigurable Bending Press Machine

Abstract Agent paradigm is a technology recognized as potent tool for the 21st century manufacturing system. Researches are ongoing in the applications of agent technology to manufacturing enterprise, production process planning and scheduling, shop floor control, and to reconfigurable manufacturing system as an implementation case study. The article presents the results of a review of published articles on agents’ usage at manufacturing enterprise level for reconfigurable manufacturing system between year 2008 and 2018 to provide up to date pictures of the technological function of agents and multi agents within the reconfigurable conceptualized manufacturing circles. The paper is view from the angle of wide coverage, broad scope of multi-agent techniques and a focus on real world applications. The outcomes include behavioral pattern and trends of agents and multi-agent’s usage in manufacturing enterprise resource planning and supply chain managements, the gaps yet to be fill for consolidating future on industry 4.0 in reconfigurable manufacturing system, and development of mobile apps interacting platform for IoT real time database communication and coordination for RBPM controller agents.

A Multi-Objective Agent-Based Control Approach With Application in Intelligent Traffic Signal System

Agent-based approaches have gained popularity in engineering applications, but its potential for advanced traffic controls has not been sufficiently explored. This paper presents a multi-agent framework that models traffic control instruments and their interactions with road traffic. A constrained Markov decision process (CMDP) model is used to represent agent decision making in the context of multi-objective policy goals, where the policy goal with the highest priority becomes the single optimization objective and the other goals are transformed as constraints. A reinforcement learning-based computational framework is developed for control applications. To implement the multi-objective decision model, a threshold lexicographic ordering method is introduced and integrated with the learning-based algorithm. Moreover, a two-stage hybrid framework is established to improve the learning efficiency of the model. While the proposed approach is potentially applicable for different road traffic operations, this paper applies the framework for traffic signal control in a network of Stockholm based on traffic simulation. The computational results show that the proposed control approach can handle a complex case of multiple policy requirements. Meanwhile, the agent-based intelligent control has shown superior performance when compared to other optimized signal control methods.

A novel hybrid agent-based model predictive control for advanced building energy systems

The development of new energy efficient components and complex energy concepts in recent years has heightened the need to design advanced control strategies. The main objective of this research is to develop a control strategy for building energy systems to save primary energy by applying the concept of multi-agent systems. Most of the advanced control strategies have been developed to be energy efficient, while their objectives are obtained from energy analysis. However, an energy analysis is unable to provide information on the quality of energy streams flowing through a system. In this study, exergy is selected as the objective of the optimization. To reach the goal of this research, an agent-based control for building energy systems using the exergy cost functions is developed. Agent-based control, which takes into account the interactions among the components of the system, offers a promising solution to the need for more advanced control strategies for complex building energy systems. The classical agent-based control developed in this study is combined with model predictive control, which leads to a novel hybrid agent-based model predictive control for the optimization of advanced building energy systems from an exergy point of view. For evaluation purposes, a case study is defined and modeled, which is controlled by a reference control and the agent-based under the same circumstances through software-in-the-loop simulations. The results show that the agent-based control is able to reduce the primary energy consumption by 2 percent while maintaining the room air temperature at the same level of the reference case.

Swarm‐based automation of electrical power distribution and transmission system support

The increasing amount of distributed energy resources installed in distribution systems imposes new challenges on the operation and control of both distribution and transmission systems. At the same time, most distributed energy resources have the potential to support grid operation, e.g. as reactive power suppliers. To exploit these capabilities, a flexible and scalable control approach capable of dealing with large numbers of heterogeneous components is required. Many approaches are either distribution or transmission oriented and neglect the interests or constraints of the other side. Furthermore, the drawbacks of classical centralised control are often ignored. This study presents SwarmGrid-X, an agent-based control concept that considers the structure, demands and capabilities of components in the distribution system and the power requirements of the transmission system at the point of common coupling. Agents control components autonomously and determine control decisions in a decentralised way. They cooperate in swarms to achieve a common system goal while information and power flow are kept as local as possible. The cooperative nature of SwarmGrid-X transforms the distribution system into a cyber-physical power system. This study explains how SwarmGrid-X enables cyber-physical interactions of components to automate electrical power distribution and support the transmission system.

Model-based development of a multi-agent system for controlling material flow systems

Abstract The rising number of product variants requires flexible manufacturing systems, including their internal material flow systems (MFSs). An approach to design MFSs reconfigurably is the use of a decentralized control based on software agents. For implementing an agent-based control approach for MFSs this paper presents a meta model describing the knowledge base of individual agents and the overall control task to be fulfilled by the MFS.

Smart, simulation-based resource sharing in federated production networks

The rise of Industry 4.0 and the convergence of the digital and physical worlds increasingly, where not radically, transforms the production networks. The paper presents the preliminary results of a distributed collaboration framework developed with the aim to facilitate the cooperation of various production sites. The objective is to manage a network of manufacturers who can dynamically re-configure and share their resources within a pre-registered community and analyze how the cardinality of the federation influences both the cooperation and the global and local production KPI-s. The novelty of the concept is the combination of the agent-based control, the matching algorithm combined with the detailed digital simulation of the distributed production systems and the collaborative IT platform.

Bidirectional low temperature district energy systems with agent-based control: Performance comparison and operation optimization

Bidirectional low temperature networks are a novel concept that promises more efficient heating and cooling of buildings. Early research shows theoretical benefits in terms of exergy efficiency over other technologies. Pilot projects indicate that the concept delivers good performance if heating and cooling demands are diverse. However, the operation of these networks is not yet optimized and there is no quantification of the benefits over other technologies in various scenarios. Moreover, there is a lack of understanding of how to integrate and control multiple distributed heat and cold sources in such networks. Therefore, this paper develops a control concept based on a temperature set point optimization and agent-based control which allows the modular integration of an arbitrary number of sources and consumers. Afterwards, the concept is applied to two scenarios representing neighborhoods in San Francisco and Cologne with different heating and cooling demands and boundary conditions. The performance of the system is then compared to other state-of-the-art heating and cooling solutions using dynamic simulations with Modelica. The results show that bidirectional low temperature networks without optimization produce 26% less emissions in the San Francisco scenario and 63% in the Cologne scenario in comparison to the other heating and cooling solutions. Savings of energy costs are 46% and 27%, and reductions of primary energy consumption 52% and 72%, respectively. The presented operation optimization leads to electricity use reductions of 13% and 41% when compared to networks with free-floating temperature control and the results indicate further potential for improvement. The study demonstrates the advantage of low temperature networks in different situations and introduces a control concept that is extendable for real implementation.

Distributed agent-based control and estimation over unreliable networks for a class of nonlinear large-scale systems

ABSTRACTA new technique for distributed estimation and control of large-scale systems is presented in this paper. The proposed method makes use of a set of agents that implement local H∞ controllers and Luenberger-like observers, improved with consensus-based corrections that incorporate information received from other agents. The communication between agents is assumed to be unreliable, this is modelled by time-varying delays and packet dropouts. The proposed methodology is an application for a class of nonlinear system affected by external disturbances. Some simulations are presented to illustrate the performance of this technique compared to the centralised case.

Distributed multi-agent based coordinated power management and control strategy for microgrids with distributed energy resources

In this paper, a distributed peer-to-peer multi-agent framework is proposed for managing the power sharing in microgrids with power electronic inverter-interfaced distributed energy resources (DERs). Recently, the introduction of electric vehicles (EVs) has gained much popularity by offering vehicle-to-home (V2H) technologies to support the sustainable operation of microgrids. Since microgrids often exhibit volatile characteristics due to natural intermittency and uncertainty, it is necessary to maintain the balancing of generation and demand through the proper management of power sharing. Therefore, the main purpose of this paper is to design an agent-based control framework to ensure the coordinated power management within the microgrids through effective utilization of EVs. The required agent communication framework is adhered to the graph theory where the control agents interact with each other using local as well as neighboring information and their distributed coordination effectively steers the proportional sharing of real and reactive powers among the inverter-interfaced EVs to maintain the stability of microgrids. The well known Ziegler-Nichols method is used to tune the proportional-integral (PI) controller of the inner current control loop within each individual control agent to perform necessary shared control tasks. A microgrid with solar photovoltaic (PV) and V2H systems is chosen to illustrate the results and it is seen that the proposed scheme improves the system performance in a smarter way through information exchange. Furthermore, the proposed framework is also validated by a comparison with an existing traditional approach and it is found that, the proposed scheme provides excellent robust and faster performance.

A Modelica library for the agent-based control of building energy systems

Building energy systems account for one third of the primary energy demand in the OECD member countries. Due to global warming and growing energy scarcity, a higher energy efficiency of these systems is required. As a result, building energy systems become more complex and often contain multiple heat and cold suppliers. The selection of the ideal supplier based on the current system state requires new control strategies. Multi agent systems depict a promising technology for the problem. There are agent-based frameworks available for many programming languages, but not for the modelling language Modelica, which is commonly used for building simulation. In the course of this work, a Modelica library for the agent-based control of building energy systems based on market mechanisms is introduced. The structure of the library, different types of agents, the concepts of agent communication and the trading of capacity adjustments are discussed. The library offers a variety of cost functions in order to realize different optimization goals. The functionality of the concept is proven with a simulation example of a building energy system controlled with agents from the introduced library. The system depicts a plug&play solution to optimize the performance of complex building energy systems with interchangeable optimization goals. Due to communication via Ethernet, the control system can be coupled to real energy systems.

The Hierarchical Distributed Agent Based Approach to a Modern Data Center Management

This paper overviews and analyzes progressive trends in modern data center, and existing solutions to build the distributed cloud data center. Authors present the hierarchical distributed agent-based control plane architecture to build a web-scale control layer based on software-defined domains. The goal of this approach is to design the simple extensible agent that could be used for any management purposes, just by adding some specific code. Using of this approach makes easier the scalability and increases the efficiency of the management of a multi-site environment. There are five main use-cases of using this approach: distributed cloud, hybrid cloud, hyperscale data center, IoT, Continuous Integration.

Emergent adaptive behaviour of GRN-controlled simulated robots in a changing environment.

We developed a bio-inspired robot controller combining an artificial genome with an agent-based control system. The genome encodes a gene regulatory network (GRN) that is switched on by environmental cues and, following the rules of transcriptional regulation, provides output signals to actuators. Whereas the genome represents the full encoding of the transcriptional network, the agent-based system mimics the active regulatory network and signal transduction system also present in naturally occurring biological systems. Using such a design that separates the static from the conditionally active part of the gene regulatory network contributes to a better general adaptive behaviour. Here, we have explored the potential of our platform with respect to the evolution of adaptive behaviour, such as preying when food becomes scarce, in a complex and changing environment and show through simulations of swarm robots in an A-life environment that evolution of collective behaviour likely can be attributed to bio-inspired evolutionary processes acting at different levels, from the gene and the genome to the individual robot and robot population.

Agent-based Control System Methodology for Reconfigurable Bending Press Machine

In the present day technology creation, automation is a promising technology towards the achievement of constructive, innovative and sustainable designs and products in industry 4. The control system of machines that involves the application of mechatronic objects or intelligent units which are envisioned as building blocks for design of the systems are rather configured than being designed. In order to meet dynamic customer needs, within minimal time frames, thereby achieving short-time-to-market. The crucial element in this scenario is that the customer drives the pace and direction of the manufacturing entity. The reality of the matter is that the manufacturer needs to produce customer-centric designed products, and this may be achieved through the use of machinery and a system configured to satisfy the need. Realizing this scenario, in this article, an automated agent-based control system methodology (ACSME) has been proposed for Reconfigurable Bending Press Machine (RBPM) application due to ongoing research. The proposed methodology will help manufacturer of RBPM to address the need for more flexible control systems and to demonstrate their industrial flexibility in several reconfigurable machines applications.

Concept and implementation of an agent-based control architecture for a cyber-physical assembly system

Today’s production companies are challenged with: the assembly of different product variants with lot size of one on a single assembly line and the increase of product variants. The implementation of reconfigurable assembly systems with a modular design is a solution, which allows a flexible adaptation to different product variants. However, handling different assembly modules via specific communication protocols is limited by the current centralized control concepts. Therefore, this paper presents a concept for an agent-based control architecture. This concept uses coherent information starting from the planning process of the assembly system until the system is put into operation allowing a decentralized control. The decentralized agents in each station act as mediators between the modules and the assembly memory database enabling the product autonomous control throughout the assembly system. Moreover, the agent manages the data transfer for the modules with different specific syntaxes via respective communication protocols since a standardized communication protocol does not exist in industry. The concept is verified in a test assembly line for the production of small electronic components.

The development of a RFID and agent-based lot management controller for PROMIS in a client/server structure for IC assembly firm

Many integrated circuit assembly firms today are struggling in a low-profit environment. To survive in the semiconductor industry, it requires assembly firms to do their best efforts in enhancing their competitive advantages. One effective way for an assembly firm to enhance its competitiveness is the introduction of advanced technologies into its shop floor control system (SFCS) to improve assembly yield and lower assembly costs. In this study, two advanced technologies, Radio Frequency Identification and agent-based approach, were used to initiate a lot management controller (LMC), which played as a thin client in a client/server architecture to cooperate with PROMIS, a SFCS being widely used in the semiconductor industry. In addition, an eight-stage software process, which is based on Unified Modeling Language, is proposed to facilitate the development of the LMC.

IEC 61131-3-based holonic control of a reconfigurable manufacturing subsystem

Researchers in reconfigurable manufacturing systems (RMSs) have generally used the agent-based control (ABC). Due to the industry’s hesitance to adopt ABC, this paper evaluates the reconfigurability of a control system developed with the industry-accepted technologies, i.e. IEC 61131-3 programming languages, a Beckhoff embedded PC and Beckhoff’s programming software, TwinCAT. The evaluation focuses on a station controller that controls a reconfigurable subsystem in an RMS. The control system, implemented in an ADACOR-based holonic architecture, was evaluated by conducting reconfiguration experiments using a laboratory case study. This paper shows that a reconfigurable station controller can be implemented using IEC 61131-3 and industry-accepted technologies if a hardware platform is used that allows multiple virtual programmable logic controllers to be run in individual threads. The control approach presented here can be used to create station control systems that offer optimised cycle times, the benefits of an RMS and the benefits of industry-accepted technology.