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Control of complex distributed systems with distributed intelligent agents
Abstract
Control of spatially distributed systems is a challenging problem because of their complex nature, nonlinearity, and generally high order. The lack of accurate and computationally efficient model-based techniques for large, spatially distributed systems leads to challenges in controlling the system. Agent-based control structures provide a powerful tool to manage distributed systems by utilizing (organizing) local and global information obtained from the system. A hierarchical, agent-based system with local and global controller agents is developed to control networks of interconnected chemical reactors (CSTRs). The global controller agent dynamically updates local controller agent’s objectives as the reactor network conditions change. One challenge posed is control of the spatial distribution of autocatalytic species in a network of reactors hosting multiple species. The multi-agent control system is able to intelligently manipulate the network flow rates such that the desired spatial distribution of species is achieved. Furthermore, the robustness and flexibility of the agent-based control system is illustrated through examples of disturbance rejection and scalability with respect to the size of the network.
... V: (35) function g * , (2)) g * (· ;λ) Sec. IV: (23), (26) Concave quadratic (g * (· ;λ)) under-approximation ofḡ * (g * ) atλ ...
... is the quadratic function defined in (23), and ϵ (k) > 0 is a bounded regularization weight. If {λ (k) } is a bounded sequence, then lim k→∞ w (k) = w * . ...
... Note that for a fixed λ, the RHS of the above inequality is quadratic in w. Minimizing both the LHS and the RHS over w ∈ R γN and using (20), we get that for all λ ∈ K * , g * (λ) ≥ g(w) + ⟨λ, ϕ(w)⟩ − ∥Dϕ(w) ⊤ (λ −λ)∥ 2 /(2m), =ḡ * (λ) + ⟨λ −λ, ϕ(w)⟩ − ∥Dϕ(w) ⊤ (λ −λ)∥ 2 /(2m), which is the same as (23). ...
In this paper, we discuss incentive design for hierarchical model predictive control (MPC) systems viewed as Stackelberg games. We consider a hierarchical MPC formulation where, given a lower-level convex MPC (LoMPC), the upper-level system solves a bilevel MPC (BiMPC) subject to the constraint that the lower-level system inputs are optimal for the LoMPC. Such hierarchical problems are challenging due to optimality constraints in the BiMPC formulation. We analyze an incentive Stackelberg game variation of the problem, where the BiMPC provides additional incentives for the LoMPC cost function, which grants the BiMPC influence over the LoMPC inputs. We show that for such problems, the BiMPC can be reformulated as a simpler optimization problem, and the optimal incentives can be iteratively computed without knowing the LoMPC cost function. We extend our formulation for the case of multiple LoMPCs and propose an algorithm that finds bounded suboptimal solutions for the BiMPC. We demonstrate our algorithm for a dynamic price control example, where an independent system operator (ISO) sets the electricity prices for electric vehicle (EV) charging with the goal of minimizing a social cost and satisfying electricity generation constraints. Notably, our method scales well to large EV population sizes.
... Required agent types and roles are identified based on the requirements for controlling the physical system. The details of the hierarchical agent-based architecture (Tatara et al., 2005b;Tatara et al., 2005a) will not be repeated in detail here. The focus will rather be on the specific agent synthesis and instantiation for the presented examples. ...
... The species that populate the reactor network are characterized by identical growth and death rates, such that one species does not have an unfair advantage over the others. The reactor network model and agent-based control system is implemented with the open source Java agent modeling and simulation environment RePast (Collier et al., 2003;Tatara et al., 2005a). ...
Large-scale spatially distributed systems provide control challenges because of their nonlinearity, spatial distribution and generally high order. The control structure for these systems tend to be both discrete and distributed. A layered control structure interfaced with complex arrays of sensors and actuators provides a flexible supervision and control system that can deal with local and global challenges. An adaptive agent-based control structure is presented whereby local control objectives may be changed in order to achieve the global control objective. Information is shared through a global knowledge environment that promotes the distribution of ideas through reinforcement. The performance of the agent-based control approach is illustrated in a case study where the interaction front between two competing autocatalytic species is moved from one spatial configuration to another. The multi-agent control system is able to effectively explore the parameter space of the network and intelligently manipulate the network flow rates such that the desired spatial distribution of species is achieved.
... The main difference between the two concepts of objects and agents is the autonomy of agents. In fact, while objects encapsulate some state on which their methods can perform actions (Tatara et al 2007), and in particular the action of invoking another object's method, an object has control over its behavior. That is, if an object is asked to perform an action, it always does so, while an agent may refuse. ...
... Solution multiplicity arises when several agents, using completely independent methods, arrive at different conclusions based on the presented data. Negotiation between agents, in the form of sharing state and decision information, is therefore required to resolve the situation (Tatara et al 2007). The design of a multi-agent system is an iterative process which aims at the identification of the parties involved (i.e., human agents, system agents, external worlds), and the processes, in addition to the types of knowledge needed (Brazier et al 2002). ...
... The complexity of distributed systems design made the researchers looking for self-organisation mechanisms, that has given rise to the agent-based control concept [28] discussed in the following section. ...
... An agent-based system includes both local and global controller agents that organise all the information obtained from the entire system. As an example in process industry, this approach is particularly useful when dealing with a system of networks of interconnected continuous stirred tank reactors (CSTRs) [28]. A framework for modelling agent-based control of serviceenabled manufacturing systems is presented in [31]. ...
This paper presents a survey on model-driven design and validation approaches for distributed automation and control systems with essentially decentralized logic. Driven by the goals of flexibility and performance improvement, researchers have explored several approaches to distributed systems design, including multiagent systems, middleware, and distributed component architectures. This also results in several international standards and reference architectures, such as IEC 61499, OpenRTM, IEC 61804, etc. Verification and validation of distributed systems is another grand challenge. This survey presents methods of using traditional and novel modeling and simulation tools in the context of distributed systems. In particular, this paper then focuses on the developments related to IEC 61499 standard, which displays a range of research directions that aim to fill the gaps in the distributed systems modeling, implementation, and validation.
... This experiment demonstrates the advantage of a knowledgebased system as IKBSC helps in scheduling for a timely, accurate and safe rolling. Due to the complex nature of distributed systems, agentbased control becomes a popular approach and a powerful tool [24]. An agent-based architecture provides robustness and flexibility and is proven to be specifically appropriate for dynamic distributed systems [8]. ...
... An agent-based system includes both local and global controller agents which organises all the information obtained from the entire system. As an example in process industry, this approach is particularly useful when dealing with a system of networks of interconnected continuous stirred tank reactors (CSTRs) [24]. In a multi-agent system (MAS), each single agent exchanges information with one another in order to achieve its own objectives. ...
This paper presents an overview of the works on design and validation of distributed control in process industry. Due to the significant market demand for distributed reconfigurable systems, not just in manufacturing industry but also in process industry, several researches are undertaken to look at the design techniques in implementation of such a decentralized control approach. It has shown that a distributed control can bring benefits such as flexibility, reconfigurability and software reusability. International standards such as IEC61499 and IEC61804 are established for such distributed application with newly introduced Function Block (FB) concepts. Intelligent control and multi-agents approach are emerging techniques for distributed control application. In particular, a process control system is generally considered as a hybrid system because it usually contains both discrete and continuous dynamics. Therefore when handling validation of distributed systems, hybrid verification and simulation will have an important role.
... In such systems waste products of some subsystems can be transferred to suitably chosen cooperating subsystems utilizing them as useful process components. This concept is connected with the tendency of the rearrangement of complex industrial production systems from an open loop form, yielding many waste products, to a closed loop form recycling waste products, and having desired ecological features [1][2][3]. The couplings of inventory type ensure a high degree of autonomy of the subsystems. ...
... The objective function (1) may represent the averaged production performance of the complex system or the averaged gain from the multiperiodic operation of the IC system, which takes into account the reduction of the utilization costs of the waste products. The τ i -periodic state equation (2) mean that each of the subsystems may be operated with its own period suitable for its particular dynamic properties. ...
This note is devoted to multiperiodically operated complex system with inventory couplings transferring waste products from some subsystems as useful components to other subsystems. The flexibility of the inventory couplings is used to force each of the subsystems with its own period and to exploit its particular dynamic properties. This enhances the performance of the complex system endowed with many recycling loops, which reduce the amount of waste products endangering the natural environment. The subsystems are characterized by generalized populations composed of the individuals (the cycles), each of them encompasses its period, its initial state, its local control, and its inventory interaction. An evolutionary optimization algorithm employing such generalized populations coordinated on the basis of the inventory interaction constraints is developed. It includes the stability requirements imposed on the cyclic control processes connected with particular subsystems. The algorithm proposed is applied to the global multiperiodic optimization of some interconnected chemical production processes.
... Multiagent applications are very well suited to complex systems with a high ratio of disturbances. Problems can be solved locally with minimum influence on the whole system [179]. The important advantage of multi-agent systems when seen from the point of view of building integrated monitoring is the possibility of dynamic management of the system [180]. ...
Monitoring is an important part of manufacturing process control and management. It plays a crucial role in ensuring agility in a manufacturing system, process robustness, responsiveness to client demands, and achievement of a sustainable production environment. Recent developments in information systems and computer technology allows for the implementation of new philosophies that integrate various monitoring applications into one complex system connected through company-wide IT systems and with systems operating throughout the whole supply chain. This paper reviews developments in the area of advanced monitoring and integration. Research on new approaches, standards, developed solutions, and company applications are presented. New directions of research and development in all areas of advanced monitoring and implementation of recent IT solutions are discussed.
... Such strategies have an intuitive motivation, as they divide control systems using knowledge of the interconnected component sub-processes, but coordination of the individual control systems introduces new challenges with respect to stable and optimal operation of the entire system. Techniques such as distributed MPC (89,90), cooperative MPC (91), and agent-based control (92) have been proposed to provide coordination between sub-system controllers. Recent works (93) have utilized concepts from network theory to detect the optimal decomposition of sub-systems for decentralized control strategies. ...
We review the impact of control systems and strategies on the energy efficiency of chemical processes. We show that, in many ways, good control performance is a necessary but not sufficient condition for energy efficiency. The direct effect of process control on energy efficiency is manyfold: Reducing output variability allows for operating chemical plants closer to their limits, where the energy/economic optima typically lie. Further, good control enables novel, transient operating strategies, such as conversion smoothing and demand response. Indirectly, control systems are key to the implementation and operation of more energy-efficient plant designs, as dictated by the process integration and intensification paradigms. These conclusions are supported with references to numerous examples from the literature.
Expected final online publication date for the Annual Review of Chemical and Biomolecular Engineering, Volume 11 is June 8, 2020. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
... in (Tatara, 2007), an application in distributed control network of interconnected chemical reactors is presented. ...
This thesis describes a methodology to deal with the interaction between MPC controllers in a distributed MPC architecture. This approach combines ideas from Distributed Artificial Intelligence (DAI) and Reinforcement Learning (RL) in order to provide a controller interaction based on cooperative agents and learning techniques. The aim of this methodology is to provide a general structure to perform optimal control in networked distributed environments, where multiple dependencies between subsystems are found. Those dependencies or connections often correspond to control variables. In that case, the distributed control has to be consistent in both subsystems. One of the main new concepts of this architecture is the negotiator agent. Negotiator agents interact with MPC agents to determine the optimal value of the shared control variables in a cooperative way using learning techniques (RL). The optimal value of those shared control variables has to accomplish a common goal, probably different from the specific goal of each agent sharing the variable. Two cases of study, in which the proposed architecture is applied and tested are considered, a small water distribution network and the Barcelona water network. The results suggest this approach is a promising strategy when centralized control is not a reasonable choice.
... Therefore, the agility of the entire system is determined by agility of manufacturing factory [5]. The capability to quickly obtain precise and real-time information from bottom manufacturing process and to deep process based on the information acquired is the basis to monitoring, diagnosis, control and management of manufacturing [6,7] and the guarantee to realize agility and flexibility [8]. Therefore, the more automated workshop is, the more important monitoring is to manufacturing equipment [9]. ...
The ability of acquiring and processing information in manufacturing influences agile of manufacturing system. According to the idea that networked field information processing is realized based on intelligent nodes of field-bus technology, distributed intelligent field information processing flow is researched to synthesize multiple functions such as information gathering, information processing, warning system and field control. The method of information represented and collected was put forward. The multilayer data fusion model of distributed intelligent field information processing is built. Then the distributed LonWorks fieldbus monitoring model (DLFMM) based on LonWorks fieldbus technology is established. A monitoring system of a rail vehicle automatic door factory is shown as an example to illustrate design strategy of monitoring system based on DLFMM. This monitoring system shows that the model of distributed intelligent field information processing, DLFMM and the extraction and representation of information flow discussed in this paper are reasonable and applicable.
... The resulting system is therefore more attuned to the corporeal world than would otherwise be possible. Basically three complementary approaches have been used to device control systems that are inherently intelligent, namely, holonic systems (Deen, 2003), agent-based systems (Brezocnik et al., 2003;Tatara et al., 2007), and artificial intelligence techniques using, eg, neural networks and fuzzy logic (Tamani et al., 2009). In the first two approaches (ie, holonic and agent-based systems) the control mechanism is distributed over a finite number of entities (the software agents) that combine their local knowledge and peculiarities with their computational resources to optimize the global solution and hence system performance. ...
... Multiagent applications are very well suited to complex systems with a high ratio of disturbances. Problems can be solved locally with minimum influence on the whole system [179]. The important advantage of multi-agent systems when seen from the point of view of building integrated monitoring is the possibility of dynamic management of the system [180]. ...
Monitoring is an important part of manufacturing process control and management. It plays a crucial role in ensuring agility in a manufacturing system, process robustness, responsiveness to client demands, and achievement of a sustainable production environment. Recent developments in information systems and computer technology allows for the implementation of new philosophies that integrate various monitoring applications into one complex system connected through company-wide IT systems and with systems operating throughout the whole supply chain. This paper reviews developments in the area of advanced monitoring and integration. Research on new approaches, standards, developed solutions, and company applications are presented. New directions of research and development in all areas of advanced monitoring and implementation of recent IT solutions are discussed.
... In a recent work [51], the issue of delays in the communication between distributed controllers was addressed. In addition to these works, control and monitoring of complex distributed systems with distributed intelligent agents were studied in [148], [27], [121]. ...
In this paper, we provide a tutorial review of recent results in the design of distributed model predictive control systems. Our goal is to not only conceptually review the results in this area but also to provide enough algorithmic details so that the advantages and disadvantages of the various approaches can become quite clear. In this sense, our hope is that this paper would complement a series of recent review papers and catalyze future research in this rapidly evolving area. We conclude discussing our viewpoint on future research directions in this area.
... The complexity associated with solving such optimization problems, and the importance of obtaining meaningful solutions have fostered a large and growing body of research work (e.g., 13,14 ). Furthermore, motivated by the increasing trends toward interconnected and tightly integrated process networks, there have been several efforts toward exploiting the underlying structure of such networks in the context of distributed control strategies (e.g., 15 ), and also toward accounting for the core network dynamics during transitions at a supervisory control level (e.g., 16,17,18 ). These developments provide promising avenues for a tighter coordination between the plant-wide optimization and supervisory control levels. ...
... However, the dynamic and adaptive response to change is now the key to competitiveness, and the traditional approaches of manufacturing control software determines the construction of monolithic and centralized systems, requiring a huge effort and higher costs for implementation, maintenance and reconfiguration of control applications. These approaches are not appropriate because do not effective ensure the support for the current requirements imposed on the manufacturing systems, particularly in terms of flexibility, cost, agility and reconfigurability [2][4]. Therefore, we need a new class of intelligent and distributed control systems for the production in order to fill the gaps created by the centralized approaches. ...
This paper presents a concrete way of linking the
JADE multi-agent system with the equipment (eg PLC, DCS,
SCADA, HMI) comprised into a distributed industrial control
system based on agents, using OPC servers. The differences
between traditional control and agent based control approach
also briefly showed. Industrial applications of multi-agent
technology are limited, among others, especially due to the
difficulties of communication between agents development
environments and heterogeneous set of control devices,
sensors and actuators which can be found in an industrial
process. The solution involved the use of OPC standard. By
using an OPC client written in Java, the connection between
JADE multi-agent system development also written in Java,
and OPC servers, was made; that allow access from JADE
agent to process variables. The concrete steps for developing
a JADE agent with the ability to connect it to the OPC server
were presented. The content of this paper refers to a part of a
dedicated application for monitoring, collection and archiving
data of a manufacturing process in the automotive industry.
The data are used by the maintenance planning system for
carrying out checks and repairs on monitored equipment and
machinery according to real functioning duration.
... The operation of such complex, spatiallydistributed dynamic systems is a formidable challenge to control engineering, to a great extent due to the intrinsic complexity, sheer size, and nonlinearities. Control engineers have turned their atten-tion to multi-agent systems whose appeal stems from their composite nature, flexibility, and scalability [24]. ...
The operation of large dynamic systems such as urban traffic networks remains a challenge in control engineering to a great extent due to their sheer size, intrinsic complexity, and nonlinear behavior. Recently, control engineers have looked for unconventional means for modeling and control of complex dynamic systems, in particular the technology of multi-agent systems whose appeal stems from their composite nature, flexibility, and scalability. This paper contributes to this evolving technology by proposing a framework for multi-agent control of linear dynamic systems, which decomposes a centralized model predictive control problem into a network of coupled, but small sub-problems that are solved by the distributed agents. Theoretical results ensure convergence of the distributed iterations to a globally optimal solution. The framework is applied to the signaling split control of traffic networks. Experiments conducted with simulation software indicate that the multi-agent framework attains performance comparable to conventional control. The main advantages of the multi-agent framework are its graceful extension and localized reconfiguration, which require adjustments only in the control strategies of the agents in the vicinity.
Use of a membrane within a bioreactor (MBR), either microbial or enzymatic, is a technology that has existed for 30 years to increase process productivity and/or facilitate the recovery and the purification of biomolecules. Currently, this technology is attracting increasing interest in speeding up the process and in better sustainability. In this work, we present the current status of MBR technologies. Fundamental aspects and process design are outlined and emerging applications are identified in both aspects of engineering, i.e., enzymatic and microorganism (bacteria, animal cells, and microalgae), including microscale aspects and wastewater treatment. Comparison of this integrated technology with classical batch or continuous bioreactors is made to highlight the performance of MBRs and identify factors limiting their performance and the different possibilities for their optimization.
Engineering Drawings: The Ancient Communications MediumCommunication Strategies Employed by OrganizationsLogistics of Project InformationChapter ReviewBibliography
To deal with the engineering complexity during the process of developing complex distributed systems, a software modeling approach for the Intelligent-agent oriented system has been proposed. The extended UML diagram and the sequence diagram are used to identify potential intelligent agents, and an agent class model is generated by specifying its mental state characters. The agent dynamic action model is developed with extended UML state diagram and Petri net graph. An example of developing a telecom network resource management system has been illustrated, which indicates that the modeling approach provides efficient solutions to manage the domain complexity.
The chapter describes several modern solutions for controlling a complex manufacturing cell, consisting of conveyors, robots, stoppers and other devices, using one programmable logical controller (PLC). The objective was to design a control system for a Holonic Planned Manufacturing System running into an Intelligent Production Cell. In order to achieve this goal, each product processed in the manufacturing cell has to be identified and its own routing / workstation jobs schedule needs to be implemented without interfering with other products. Two solutions were implemented, one using an offline planner that calculates the necessary data for processing products on pallets and the other using Intelligent Electronic Devices (IED) attached to the pallets that can take decisions by themselves in real time. A special array of data structures was used to easily access and manage the pallets processed at the four robotized stations of the cell.
Construction is the country's single largest manufacturing industry. However, it is a sector that lacks benchmarks against which to gauge performance. This modern thinking intends to provide insight to construction productivity improvement. Taking cues from manufacturing sectors such as computer, automobile and chemical companies, this book will apply the lessons learned to building construction. Supported with a range of pedagogical devices, the book will be of equal value to construction managers and civil engineers, and students with different learning methods.
To ensure the correctness of interactive behaviors in distributed software systems, an abstraction approach of modular interaction is proposed. First, system state machine diagrams and object state machine diagrams are utilized to describe state transitions while UML2.0 sequence diagrams are adopted to express the interactive behaviors. Further, the model checking approach with propositional projection temporal logic (PPTL) is applied to verify whether the interactive modular can satisfy the system properties. Using the algorithms, object state machine diagram is transformed into Promela model, and the desired property of the system is specified by a PPTL formula. As a property specification language, PPTL has the expressive power of both full regular and omega regular expressions. At the same time, it is proper for the specification of state sensitive properties. Within the model checker based on Spin, if the system cannot satisfy the property, a counter-example and the execution path can be found. Finally, a novel framework for testing distributed software is given. Based on the verified sequence diagram, test case generation method can be used to get the set of test cases. Experimental results show that the proposed method is useful in improving the effectiveness and reliability of interactive behaviors in distributed software systems.
Abstract System outputs with different sampling times may challenge traditional subspace identification methods to generate accurate process models and consequently provide model-based control systems that may not be very effective. The multi-rate identification problem is addressed by dividing the multi-rate sampled system into different subsystems, and a multi-rate distributed model predictive control technique is proposed to control such systems. The performance of the proposed method is evaluated and illustrated by modeling and controlling the Tennessee Eastman challenge problem.
Distributed control provides communication between local controllers, which facilitates the achievement of the global objective for the overall control problem. A coordinator is proposed to implement the communication of local controllers. The presence of the coordinator in the control system provides benefits in the setup of the distributed controllers, and also the calculation of target tracking without the requirement for a feasible starting point. The functionality of the coordinator is illustrated in a case study of the Tennessee Eastman challenge problem.
This paper presents the development of a distributed hybrid agent-based (ABS) discrete event simulation (DES) model within the context of emergency medical services (EMS). The existing simulation models of EMS either are considered as a single model or several standalone models that represent different system elements in isolation. The aim of this research is to demonstrate the feasibility of using distributed simulation technology to implement hybrid EMS simulation. This would provide opportunities to study holistically integrated improvement scenarios for emergency medical services and crisis management systems. The case study is based on the London EMS and consists of an ambulance service ABS model and several accident and emergency departments DES models. Both the ABS and the DES models were developed in Repast Simphony toolkit using poRTIco RTI software to achieve communication between them. The results prove that we can use distributed simulation to successfully represent the real system.
Presenting a systematic model reduction and hierarchical controller design framework for broad classes of integrated process systems encountered in practice, this book first studies process systems with large material recycle and/or with small purge streams, followed by systems with energy integration. Step-by-step model reduction procedures are developed to derive nonlinear reduced models of the dynamics in each time scale. Hierarchical control architectures consisting of coordinated levels of control action in different time scales are proposed for each class of process systems considered to enforce stability, tracking performance and disturbance rejection. Numerous process applications are discussed in detail to illustrate the application of the methods and their potential to improve process operations. MATLAB codes are also presented to guide further application of the methods developed and facilitate practical implementations.
Spatially distributed systems hosting autocatalytic species are an excellent test bed as a complex network because of their non-linear structure and ability to display a rich spectrum of behavior. The network system is designed to produce a robust, autocatalytic host species. It so happens that an invasion in the form of another autocatalytic species with higher reproduction rates disrupts normal operation. When an invading species gains foothold in the system, the concentration of the host species decreases and thereby the network elicits different behavioral patterns. The complex network is supervised and tested under an agent based architectural framework which employs a hierarchical structure for agents distributed in different layers. The generic agent structure is comprised of a variety of agents designed for monitoring and control purposes. To combat infection, multi-agent strategies are employed to explore alternatives and intelligently control the network. This choice is ideal because agent based approaches are flexible, robust and can adapt to changing conditions.
In the study, a challenge is posed to investigate a 25 reactor grid network with unequal volumes attacked by a temporary disturbance of an invading cubic autocatalytic species in inflow. The analysis shows that after the disturbance is removed, the network displays interesting patterns. Depending on the magnitude of invasion, growth and death rates of the invading species, case studies demonstrate that undesirable species can disappear from the environment, coexist with the host, flush out the host species or propagate through the network as a disturbance. Elimination of the invader or disturbance rejection can be achieved by using alternative mechanisms, namely, flushing out the invading species, starving the invading species to death or adding an external entity or antibiotic to kill the undesired species.
Owing to the complex nature of the system, an agent based strategy provides a generic detection and control scheme for detection and control of invading species. With the invading species being an immeasurable quantity, there is a need to device a specialized invasion detector agent which controls based on system knowledge. As a knowledge building exercise, sans invasion, a large number of case studies were simulated using feed flow rate, interaction flow rate and resource concentration in the feed as free parameters. The data organize themselves into a manifold which relate the consumption of resource and the production of host autocatalytic species. A control ellipse is constructed using statistical tools to encapsulate normal operating data. For data points within the control ellipse, the gradient in concentration for host species and resource is often negative. A rule based strategy is used to build agents to raise alarms when invasion occurs. What happens is, invasion originates at a specific location in a network, the concentration gradients follow dynamic trajectory and finally settle down at a steady state. Rules are designed to capture invasion patterns at an early stage based on projecting the invasion data onto the control ellipse and estimating the changes in the gradients in concentration of resource and host species. Performance of the system was tested for different operating conditions. Depending on the degree of invasion in the system, agent based control is employed using a variety of manipulated variables.
In this work, we focus on the application of an integrated fault detection and isolation and fault tolerant control (FDIFTC) framework to a catalytic alkylation of benzene process. We consider that the catalytic alkylation of benzene process is controlled by a distributed model predictive control (DMPC) system and is subjected to unknown, persistent actuator faults. The FDIFTC system monitors closed-loop process residuals in order to detect and isolate a faulty actuator. After isolation of an actuator fault, the FDIFTC system estimates the fault magnitude, recalculates a new optimal operating point, and ultimately reconfigures the DMPC system to maintain stability of the process in an optimal manner. Extensive simulations are carried out to demonstrate the performance of the FDIFTC system from closed-loop stability and performance points of view.
In this paper, a new architecture for automation in manufacturing systems is proposed. The architecture “intelligent Distributed Control System (iDCS)” is focused on the distribution of control resources so that the intelligent control and condition monitoring can be performed locally with maximum level of autonomy. In this regard, the powerful framework of Multi Agent Systems (MAS) is employed to conceptually model the manufacturing platform. Also, considering the event-driven dynamics of the plant, an automata language is used to formally represent the agents and their interactions. In order to enhance the fault tolerance of the agent-based community, a fuzzy redundancy management scheme has been introduced to the supervisory level of iDCS. Simulations have been performed to demonstrate the iDCS model and redundancy policy of a flow line comprised of four agents.
The aim of this paper is to review and to propose a classification of a number of decentralized, distributed and hierarchical control architectures for large scale systems. Attention is focused on the design approaches based on Model Predictive Control. For the considered architectures, the underlying rationale, the fields of application, the merits and limitations are discussed, the main references to the literature are reported and some future developments are suggested. Finally, a number of open problems is listed.
On-farm implementation of the concept of precision livestock farming requires the design and development of new, advanced and often complex equipment. This article proposes an agent-based simulation framework to meet the challenges of designing, testing and evaluating the performance of the new, automated precision feeding equipment for farms. In this context, an agent is a piece of software performing defined system functions such as exchanging information or requesting services to other agents via a high-level agent communication language. The proposed approach sees each main component of the actual precision feeding system represented by a virtual domain agent. The agents are then assembled within a multi-agent system that models the automatic precision feeding equipment as a whole. The operational capacity of the virtual prototype is achieved through communication and collaboration between the multiple agents that make up the system. The capacity for dynamic modification of parameters such as workload during the simulation process helps analyze the behavior, robustness and performance of the system prior to its actual use and thus providing a computer-aid tool helping in the design process. One case study shows that agent-based composable simulation can predict the behavior and performance of the system as a whole.
It is highly desirable to have a statistical process monitoring (SPM) system that detects the abnormalities in process operations quickly with as few missed and false alarms as possible while the process operates under various operating conditions. An agent-based combined monitoring and fault detection framework is proposed in this study. In this framework, different SPM techniques compete with and complement each other to enhance detection speed and accuracy. SPM techniques from literature such as principal component analysis (PCA), multiblock PCA (MBPCA), and dynamic PCA (DPCA) techniques are implemented in this agent-based process supervision system. An agent performance assessment and agent management layer provides dynamic adaptation of the supervision system and improves the performance of SPM. The statistical information coming from each of the statistical techniques is summarized through a consensus mechanism. The performance of the agent-based consensus mechanism using different consensus criteria is tested for system disturbances of various magnitudes. The effectiveness of the proposed agent-based framework with different consensus criteria is evaluated based on fault detection times and missed alarm rates and the adaptation of the supervision system is illustrated.
In this paper, we show that heterogeneous networks can be configured and reconfigured to operate in flexible spatial patterns. The concept is demonstrated by using a square grid of continuous stirred tank reactors with nearest neighbor interactions and hosting multiple cubic autocatalytic species. Asymmetry is introduced in the system by allowing for different feed flow rates in the reactors. A heterogeneous system offers expanded phase space, improved stability, and coexistence characteristics. The advantages of asymmetry are studied using bifurcation diagrams, continuation studies, and dynamics.
An adaptive agent-based hierarchical framework for fault type classification and diagnosis in continuous chemical processes is presented. Classification techniques such as Fisher’s discriminant analysis (FDA) and partial least-squares discriminant analysis (PLSDA) and diagnosis tools such as variable contribution plots are used by agents in this supervision system. After an abnormality is detected, the classification results reported by different diagnosis agents are summarized via a performance-based criterion, and a consensus diagnosis decision is formed. In the agent management layer of the proposed system, the performances of diagnosis agents are evaluated under different fault scenarios, and the collective performance of the supervision system is improved via performance-based consensus decision and adaptation. The effectiveness of the proposed adaptive agent-based framework for the classification of faults is illustrated using a simulated continuous stirred tank reactor (CSTR) network.
An adaptive hierarchical framework for process supervision and fault-tolerant control with agent-based systems is presented. The framework consists of modules for fault detection and diagnosis (FDD), system identification and distributed control, and a hierarchical structure for performance-based agent adaptation. Multivariate continuous process monitoring methodologies and several fault discrimination and classification techniques are implemented in the FDD modules to be used by multiple agents. In the process supervision layer, the continuous intramodular communication between FDD and control modules communicates the existence of an abnormality in the process, type of the abnormality, and affected process sections to the distributed model predictive control agents. In the agent management layer, the performances of all FDD and control agents are evaluated under specific process conditions. Performance-based consensus criteria are used to prioritize the best-performing agents in consensus decision making in every level of process supervision and fault-tolerant control. The collective performance of the supervision system is improved via performance-based consensus decision making and adaptation. The effectiveness of the proposed adaptive agent-based framework for fault-tolerant control is illustrated using a simulated continuous stirred-tank reactor network. Copyright © 2011 John Wiley & Sons, Ltd.
In this paper, the design and implementation of an agent-based framework to safeguard an auto-catalytic reactor network against external disturbances is discussed. The autocatalytic reactor network producing a robust, host species is attacked by an unmeasured, un-sustained disturbance of a non-robust invading species. The invading species adversely affects host species and manifests different behavioural responses depending on its magnitude and location of injection. The agent framework is employed to detect and counteract the invasion. The agent architecture consists of the physical layer and multiple invasion detection and local operator agents. Based on the operation state of the process, the detection agent raise a variety of flags (for example, “normal operation”, “severe invasion” etc.), whereas the operator agents take control actions based on the flags raised by the detection agent. The agent knowledge in the form of rule-based heuristics designed based on process knowledge gained through simulation under open-loop. The agent knowledge base consists of several parameters which affect their behaviours. Here, we propose a methodology to optimize the parameters critical to agent design. With this method, the agent system evolves in its structure, which facilitates more accurate responses.
Many agent-based systems rely for their effectiveness on the intelligence of individual agents, and interaction among agents is required simply to coordinate these individually complex decisions. Specification and design methods for such systems focus on the internal architecture of individual agents. An alternative approach, "Synthetic Ecosystems," uses relatively simple agents and draws heavily on the dynamics of the interaction among these agents as well as their internal processing to solve domain problems. The specification and design of such systems must include not only the individual agents, but also the structure and dynamics of their interaction. This paper briefly defines and motivates the Synthetic Ecosystems approach and outlines some techniques that have proven useful in specifying and designing them.
This paper provides an overview of research and development activities in the field of autonomous agents and multi-agent systems. It aims to identify key concepts and applications, and to indicate how they relate to one-another. Some historical context to the field of agent-based computing is given, and contemporary research directions are presented. Finally, a range of open issues and future challenges are highlighted.
The paradigm of cubic autocatalytic replicators with decay in coupled isothermal continuous stirred tank reactors is selected as a model to study complex behavior in population dynamics of sexually reproducing species in a heterogenous environment. It is shown that, even a setup with single species in two coupled environments may have regions in parameter space that result in chaotic behavior, hence segregation in the environment causes complexity in the system dynamics. Furthermore, partitioning is found to lead to emergence phenomena exemplified by steady states not obtainable in the equivalent homogeneous system. These phenomena are illustrated through case studies involving single or multiple species. Results show that the coupled environments can host species, that would not survive should the coupling be removed.
Scientific research and practice in multiagent systems focuses on
constructing computational frameworks, principles, and models for how
both small and large societies of intelligent, semiautonomous agents can
interact effectively to achieve their goals. This article provides a
personal view of the key application areas for cooperative multiagent
systems, the major intellectual problems in building such systems, the
underlying principles governing their design, and the major directions
and challenges for future developments in this field
. Many agent-based systems rely for their effectiveness on the intelligence of individual agents, and interaction among agents is required simply to coordinate these individually complex decisions. Specification and design methods for such systems focus on the internal architecture of individual agents. An alternative approach, "Synthetic Ecosystems," uses relatively simple agents and draws heavily on the dynamics of the interaction among these agents as well as their internal processing to solve domain problems. The specification and design of such systems must include not only the individual agents, but also the structure and dynamics of their interaction. This paper briefly defines and motivates the Synthetic Ecosystems approach and outlines some techniques that have proven useful in specifying and designing them. 1 Introduction [7] summarizes the history of agent-based systems design in two phases: a reductionist period that focused on top-down analysis of system issue...
The hype in agent research will not last forever. If the agent research community does not succeed in bringing its results into real-world application within the next two or three years it will share the fate of some other AI sections which are today thought to be just academic. Multi-agent systems applied to manufacturing are candidates for bringing the first breakthrough. To achieve that, methodologies for the design of agent systems for manufacturing by software developers are needed. This paper reports on the design process chosen in the MASCADA project, whose goal is not only to develop an algorithmic solution to manufacturing problems but also to show a transfer strategy for these results.
Intelligent agents have been in the computer industry for more than a decade. But until now it is not clear how will it revolutionize the Internet and other computer applications. Some points regarding the communities using the term, the communication languages that support the research, and the functionalities that different so-called agents provide are presented and discussed to shed some light on the confusions that the intelligent agents terminology bring about.
Piece-wise affine and mixed logical dynamical models for discrete time linear hybrid systems are reviewed. Constrained optimal control problems with linear and quadratic objective functions are defined. Some results on the structure and computation of the optimal control laws are presented. The effectiveness of the techniques is illustrated on a wide range of practical applications.
Systems with high steady-state multiplicity and rich dynamic behavior are difficult to investigate using conventional reductionist methods. A network of more than five reactors hosting cubic autocatalytic reactions may potentially have more than 102 steady states and many distinct dynamic regimes, all for the same parameter set. This paper discusses how the static complexity of such systems can be measured to give a holistic picture. To achieve this, stochastic simulations were performed to statistically determine the bifurcation structure of the system, and the gathered information is summarized using a measure akin to fractal dimension. With this measure, the growth of static complexity is investigated as a function of the network size.
The static and dynamic behavior of the autocatalytic reaction R + 2P → 3P with decay P → D is studied in networks of coupled continuous stirred tank reactors (CSTRs). Numerical bifurcation studies of the system are performed, resulting in rich steady-state bifurcation structures with multiple steady states and isolas. The heterogeneity of the networks is influenced by the number of reactors as well as the network topology. It is shown that the number of steady states of the network increases with heterogeneity, thereby allowing those autocatalytic species to exist in the network that would normally not exist in the homogeneous environment of a single CSTR. Spatial patterns of stable steady states are evident in reactor networks. Dynamic simulation studies are performed to illustrate the transition from one stable state configuration to another or from stable steady states to periodic regimes.
Two types of nonlinear feedback control schemes are introduced and analyzed for their capability of recovering the original state of an isothermal continuous-flow stirred tank reactor with one robust cubic autocatalytic species, perturbed by a temporary disturbance of an invading cubic autocatalytic species in the inflow. The control objectives are to eliminate the invading species from the system and to restore the original state of the host species. The extent of applicability of the control design to different nonrobust invading species is studied, when the controller is tuned for a specific invader. Moreover, a time-delay feature is suggested in one of the control schemes developed to achieve the control objectives in systems with poor detection of invading species.
A robust hybrid control strategy for a broad class of hybrid nonlinear processes with actuator constraints and uncertain dynamics is proposed. These variable-structure processes comprise a finite family of constrained uncertain continuous nonlinear dynamical subsystems, together with discrete events that trigger the transition between the continuous subsystems. The proposed control strategy is predicated on the idea of coordinating the hierarchical tasks of lower-level feedback-controller synthesis and upper-level switching logic design. Using multiple Lyapunov functions, a family of bounded robust, nonlinear feedback controllers are initially designed to robustly stabilize the constituent modes of the hybrid process, subject to uncertainty and constraints. The region of guaranteed closed-loop stability is then explicitly characterized for each mode in terms of the magnitude of actuator constraints and the size of the uncertainty. A set of stabilizing switching laws that track the energy evolution of the constituent modes are then derived to orchestrate safe transitions between the stability regions of the constituent modes and their respective controllers, in a way that respects actuator constraints and guarantees robust stability of the overall uncertain hybrid closed-loop system. This hybrid control method is applied through computer simulations to robustly stabilize an exothermic chemical reactor with switched dynamics, model uncertainty, and actuator constraints at an unstable steady-state and to design a fault-tolerant control system for chemical reactors through switching between multiple constrained control configurations.
A methodology for the design of fault-tolerant control systems for chemical plants with distributed interconnected processing units is presented. Bringing together tools from Lyapunov-based nonlinear control and hybrid systems theory, the approach is based on a hierarchical architecture that integrates lower-level feedback control of the individual units with upper-level logic-based supervisory control over communication networks. The local control system for each unit consists of a family of control configurations for each of which a stabilizing feedback controller is designed and the stability region is explicitly characterized. The actuators and sensors of each configuration are connected, via a local communication network, to a local supervisor that orchestrates switching between the constituent configurations, on the basis of the stability regions, in the event of failures. The local supervisors communicate, through a plant-wide communication network, with a plant supervisor responsible for monitoring the different units and coordinating their responses in a way that minimizes the propagation of failure effects. The communication logic is designed to ensure efficient transmission of information between units, while also respecting the inherent limitations in network resources by minimizing unnecessary network usage and accounting explicitly for the effects of possible delays due to fault-detection, control computations, network communication and actuator activation. The proposed approach provides explicit guidelines for managing the various interplays between the coupled tasks of feedback control, fault-tolerance and communication. The efficacy of the proposed approach is demonstrated through chemical process examples. © 2005 American Institute of Chemical Engineers AIChE J, 2005
Agent-based computing represents an exciting new synthesis both for Artificial Intelligence (AI) and, more generally, Computer Science. It has the potential to significantly improve the theory and the practice of modeling, designing, and implementing computer systems. Yet, to date, there has been little systematic analysis of what makes the agent-based approach such an appealing and powerful computational model. Moreover, even less effort has been devoted to discussing the inherent disadvantages that stem from adopting an agent-oriented view. Here both sets of issues are explored. The standpoint of this analysis is the role of agent-based software in solving complex, real-world problems. In particular, it will be argued that the development of robust and scalable software systems requires autonomous agents that can complete their objectives while situated in a dynamic and uncertain environment, that can engage in rich, high-level social interactions, and that can operate within flexible organisational structures.
Schelling [Schelling, T.C., 1969. Models of Segregation. American Economic Review, Papers and Proceedings, 59, 488-493, Schelling, T.C., 1971a. Dynamic Models of Segregation. Journal of Mathematical Sociology, 1 (2), 143–186, Schelling, T.C., 1971b. On the Ecology of Micromotives. The Public Interest, 25, 61–98, Schelling, T.C., 1978. Micromotives and Macrobehavior. New York: Norton.] presented a microeconomic model showing how an integrated city could unravel to a rather segregated city, notwithstanding relatively mild assumptions concerning the individual agents' preferences, i.e., no agent preferring the resulting segregation. We examine the robustness of Schelling's model, focusing in particular on its driving force: the individual preferences. We show that even if all individual agents have a strict preference for perfect integration, best-response dynamics may lead to segregation. This raises some doubts on the ability of public policies to generate integration through the promotion of openness and tolerance with respect to diversity. We also argue that the one-dimensional and two-dimensional versions of Schelling's spatial proximity model are in fact two qualitatively very different models of segregation.
This paper presents a methodology for agent-oriented analysis and design. The methodology is general, in that it is applicable to a wide range of multi-agent systems, and comprehensive, in that it deals with both the macro-level (societal) and the micro-level (agent) aspects of systems. The methodology is founded on the view of a system as a computational organisation consisting of var- ious interacting roles. We illustrate the methodology through a case study (an agent-based business process management system). Progress in software engineering over the past two decades has pri- marily been made through the development of increasingly power- ful and natural abstractions with which to model and develop com- plex systems. Procedural abstraction, abstract data types, and, most recently, objects, are all examples of such abstractions. It is our belief that agents represent a similar advance in abstraction: they may be used by software developers to more naturally understand, model, and develop an important class of complex distributed sys- tems.
Spatially distributed systems such as reactor networks hosting multiple autocatalytic species demonstrate a rich spectrum of complex behavior. From a control systems perspective, spatially distributed systems offer a difficult control challenge because of their distributed nature, nonlinearity, and high order. Furthermore, manipulation of the network states may require simultaneous control actions in different parts of the system and may need transients through several operating regimes to achieve the desired operation. The lack of accurate and computationally efficient model-based techniques for large, spatially distributed systems results in complications in controlling the system, either in disturbance rejection or changing the operational regimes of the system. A hierarchical, agent-based control structure is presented whereby local control objectives may be changed in order to achieve the global control objective. The performance of the control system is demonstrated for several global objectives. The challenge posed is to control the spatial distribution of autocatalytic species in a network of five reactors hosting two species using the interaction flow rates as the manipulated variables. The multi-agent control system is able to effectively explore the parameter space of the network and intelligently manipulate the network flow rates such that the desired spatial distribution of species is achieved.
The problem of pattern is considered in terms of how genetic information can be translated in a reliable manner to give specific and different spatial patterns of cellular differentiation. Pattern formation thus differs from molecular differentiation which is mainly concerned with the control of synthesis of specific macromolecules within cells rather than the spatial arrangement of the cells. It is suggested that there may be a universal mechanism whereby the translation of genetic information into spatial patterns of differentiation is achieved. The basis of this is a mechanism whereby the cells in a developing system may have their position specified with respect to one or more points in the system. This specification of position is positional information. Cells which have their positional information specified with respect to the same set of points constitute a field. Positional information largely determines with respect to the cells' genome and developmental history the nature of its molecular differentiation. The specification of positional information in general precedes and is independent of molecular differentiation. The concept of positional information implies a co-ordinate system and polarity is defined as the direction in which positional information is specified or measured. Rules for the specification of positional information and polarity are discussed. Pattern regulation, which is the ability of the system to form the pattern even when parts are removed, or added, and to show size invariance as in the French Flag problem, is largely dependent on the ability of the cells to change their positional information and interpret this change. These concepts are applied in some detail to early sea urchin development, hydroid regeneration, pattern formation in the insect epidermis, and the development of the chick limb. It is concluded that these concepts provide a unifying framework within which a wide variety of patterns formed from fields may be discussed, and give new meaning to classical concepts such as induction, dominance and field. The concepts direct attention towards finding mechanisms whereby position and polarity are specified, and the nature of reference points and boundaries. More specifically, it is suggested that the mechanism is required to specify the position of about 50 cells in a line, relatively reliably, in about 10 hours. The size of embryonic fields is, surprisingly, usually less than 50 cells in any direction.
The plant innate immune response includes the hypersensitive response (HR), a form of programmed cell death (PCD). PCD must be restricted to infection sites to prevent the HR from playing a pathologic rather than protective role. Here we show that plant BECLIN 1, an ortholog of the yeast and mammalian autophagy gene ATG6/VPS30/beclin 1, functions to restrict HR PCD to infection sites. Initiation of HR PCD is normal in BECLIN 1-deficient plants, but remarkably, healthy uninfected tissue adjacent to HR lesions and leaves distal to the inoculated leaf undergo unrestricted PCD. In the HR PCD response, autophagy is induced in both pathogen-infected cells and distal uninfected cells; this is reduced in BECLIN 1-deficient plants. The restriction of HR PCD also requires orthologs of other autophagy-related genes including PI3K/VPS34, ATG3, and ATG7. Thus, the evolutionarily conserved autophagy pathway plays an essential role in plant innate immunity and negatively regulates PCD.
In this paper we show how hybrid system theory can be used to obtain a state-feedback optimal control law for an electronic throttle. After modelling the electronic throttle as a piece wise affine (PWA) system, we derive an optimal control law for such a hybrid system via dynamic programming. Results indicate that constrained finite time optimal control of small/medium sized PWA systems with fast sampling times can be successfully implemented.
There is so much talk these days about software agents, and close
relatives with names such as softbots, knobots, and interface agents,
that we are reminded of the early days of AI. The ideas are creative,
early-stage, and all over the map. At Stanford University alone you will
find agents that sort your mail, adaptively recommend Web pages, assist
with scheduling, find people with interests similar to your own,
translate between different knowledge bases, and have individual
electronic personality and graphical depiction. Elsewhere, you can also
find agents that help manage your network, shop for you, migrate in the
network, have a natural-language understanding capability, and much
more. The author discusses some of the properties that characterize
software agents
The integration of a supervisory knowledge-based system (KBS) with a multivariable control system is examined to provide robust multivariable control of a chemical reaction process. The supervisory KBS is capable of monitoring the process to detect system faults as well as assessing control system performance. If a control system performance deficiency is detected, the KBS formulates and implements the necessary corrective controller tuning. This adaptive capability reduces the conservatism of the robust control system. The underlying mechanisms are discussed and the re-tuning ability of the KBS is illustrated by using rigorous simulations of a chemical reaction process.< >
Patterning in Vertebrate Develop-mentFrontiers in Molecular Biology
- L Wolpert
- M Kerszberg
L. Wolpert, M. Kerszberg, Patterning in Vertebrate Develop-mentFrontiers in Molecular Biology, Oxford University Press, 2003 (Chapter 1).
Onward and upward: the transition to repast 2.0, in: First Annual North American Association for Computational Social and Organizational Science Conference
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- M North
N. Collier, T. Howe, M. North, Onward and upward: the transition
to repast 2.0, in: First Annual North American Association for
Computational Social and Organizational Science Conference, Pittsburgh, PA, 2003.
Large scale failure in the network prompts reconfiguration of functioning reactors with red (dark gray) representing species 1, blue (black) species 2, green (light gray) species 3, and very light gray representing disabled reactors
- Fig
Fig. 12. Large scale failure in the network prompts reconfiguration of functioning reactors with red (dark gray) representing species 1, blue (black) species
2, green (light gray) species 3, and very light gray representing disabled reactors. (For interpretation of the references in color in this figure legend, the
reader is referred to the web version of this article.)
Designing agents for manufacturing control
- S Brueckner
- J Wyns
- P Peeters
- M Kollingbaum
S. Brueckner, J. Wyns, P. Peeters, M. Kollingbaum, Designing agents
for manufacturing control. in: Proceedings of the second AI &
Manufacturing Research Planning Workshop, Albuquerque, NM,
1998.
- N Jennings
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- M Wooldridge
N. Jennings, K. Sycara, M. Wooldridge, A roadmap of agent research
and development, Autonomous Agents and Multi-Agent Systems 1
(1998) 7-38.