Managed Complexity in An Agentbased Vent Fan Control System
Based on Dynamic
Re-configuration
Fred M. Discenzo
Rockwell Automation, USA
Fmdiscenzo@ra.rockwell.com
Francisco P. Maturana
Rockwell Automation, USA
Fpmaturana@ra.rockwell.com
Dukki Chung
Rockwell Automation, USA
Dchung@ra.rockwell.com
1. Introduction
Developments in advanced control techniques are occurring in parallel with advances in
sensors, algorithms, and architectures that support next-generation condition-based
maintenance systems. The emergence of Multi-agent Systems in the Distributed Artificial
Intelligence arena is providing important new capabilities that can significantly improve
automation system performance, survivability, adaptability, and scalability. The new
capabilities provided by multi-agent systems has shifted control system research into a very
challenging and complex domain. A multi-agent system approach enables us to encapsulate
the fundamental behavior of intelligent devices as autonomous components. These
components exhibit primitive attitudes to act on behalf of equipment or complex processes.
Using this approach, we have implemented a set of cooperating systems that manage the
operation of an axial vent fan.
We have implemented a laboratory vent fan system
(Figure 1) that operates autonomously as a fan
agent in the context of a chilled water system
comprised of other agents such valve, load, and
pump agents.
This paper presents the foundation technologies
that are essential to realizing an adaptive, reconfigurable automation system. The vent fan
system serves to validate the agent methodology to
manage the inherent complexity of highly
distributed systems while responding dynamically
to changes in operating requirements, degraded or
failed components through prognostics, control
alteration, and dynamic re-configuration.
Figure 1. Vent Fan
Demonstration System
The concepts above and new engineering developments have helped achieve new and
important capabilities for integrating CBM technologies including diagnostics and prognostics
with predictive and compensating control techniques. Integrated prognostics and control
systems provide unique opportunities for optimizing system operation such as maximizing
revenue generated for capital assets, maximizing component lifetime, insuring machinery
survival or mission success, or minimizing total life-cycle costs.
2. Intelligent Machines
2.1. Machine Intelligence
Beginning with the famous Turing Paper about 50 years ago there has been an ongoing effort
to understand cognition and intelligence in order to make machines more useful [Charniak
1985] [Nilsson 1980]. With a goal of enhancing the capability of machines, Artificial
Intelligence (AI) includes techniques such as expert systems, fuzzy logic, artificial neural
networks, and related model-based and model-free techniques [Charniak 1985]. Many of the
automation successes reported apply biologically inspired adaptive and knowledge-based
techniques to solve well-targeted, specific automation problems such as adaptive control,
defect classification, and job scheduling to name a few [Zurada 1994].
The capabilities which may be provided by intelligent machines may be categorized based on
the degree of embedded knowledge with the most capable systems employing real-time goal
adjustment, cooperation, pre-emption, and dynamic re-configuration [Discenzo(a)
2000][Discenzo(b) 2000]. These capabilities may be effectively integrated in an agent-based
system employing intelligent machines in a distributed automation system. This architecture
built on a foundation of a society of locally intelligent cooperating machines provides a
effective framework for the efficient and robust automation of complex systems.
2.2. Multi-Agent Systems
Our approach is to encapsulate the fundamental behavior of intelligent devices as autonomous
components. The components employ models of primitive device behavior and enable agents
to act on behalf of physical devices or complex processes.
The approach of establishing application-specific agent behavior in a reusable and scalable
manner finds counterparts in other research activities such as Multi-agent Systems (MAS),
Autonomous Agents, Flexible Manufacturing, and Virtual Enterprise [Shen 2001][Vasko
2000][Zhang 1999]. The evolution of web-based systems, which pushes Internet
communication to accommodate agent-like services, also coincides with industrial automation
requirements. Language and protocols can also serve as the basis to explore information
exchange and resource discovery for agents (e.g., XML, SOAP, and UDDI [XML&DTD
Specification][Xerces XML Parser][Universal Description UDDI White Paper, 2000][UDDI
2000][Simple Object Access Protocol, 2000]). In this paper, we focus on the requirements for
the controller level.
The Foundation for Intelligent Physical Agents (FIPA) [FIPA][FIPA-OS] provides welldefined and widely accepted standards for multi-agent systems development that coincide
with several of the premises established in this work. FIPA implementations accelerate the
development of multi-agent systems.
Previous results demonstrated for autonomous control have been extended to incorporate
agents that do planning, communication, diagnostics, and control [Vasko 2000][Maturana
2000][Maturana 2002]. Our
focus is on the aggregation of
OSA/CBM
autonomous behaviors and
Inter-Agent communication
TRANSDUCER
coordinated coalitions of
Agent
DATA ACQUISITION
smart
resources.
The
Diagnostic Diagnostics Planner
Module
Module
approach we have taken is to
SIGNAL PROCESSING
establish
a
general
Execution
Evaluate Configuration
CONDITION
MONITOR
architecture
to
deploy
Equipment
Execution
Model Module
Control
information
agents
for
HEALTH ASSESMENT
Module
resource discovery in a
DECISION SUPPORT
highly distributed system.
PROGNOSTICS
Hardware equipment
Resource capability may be
PRESENTATION
established using an open
system
architecture
that
Figure 2. OSA-CBM in an Agent Framework
permits utilizing existing or
future prognostic algorithms (Figure 2).
3. Chilled Water Demonstration System
3.1. Vent Fan Hardware Configuration
Fans and related air handling applications are often critical applications that occur in a wide
range of automation, commercial, and military systems.
We
have
defined
and
implemented a vent fan system
employing
an
integrated
diagnostic
/
prognostic
/
controller system in an agentbased representation.
This
system has been implemented as
one component in a hardware-inthe-loop simulation of a chilled
water system. The vent fan
system shown in Figure 3 is
operated
with
a
variable
frequency drive (VFD) and
programmable logic controller
(PLC). During operation the
system dynamically adjusts air
flow based on state changes or
new requirements and in
collaboration with other agents
operating in related parts of a
chiller system.
Figure 3. Vent Fan System Schematic
The system in Figure 3 includes a 3-phase, 2 hp, 230 volt, a-c induction motor coupled to a
fan. The motor is an “Intelligent Motor” that includes embedded current, voltage,
temperature, and vibration sensors and a processor to enable the motor to continually assess
its own health. The motor and fan are mounted inside a 20” diameter steel tube as an
integrated axial vent fan and the VFD motor controller is mounted on top of the structure.
This hardware system is operated as an intelligent sub-system in a chilled water system.
3.2. Chilled Water System Configuration
3.2.1 Agent types and capabilities
The chilled water system is simulated on
a PLC and other than the fan system and
a water pump system, all other
components such as valves, pipes, and
loads are simulated in a programmable
logic controller. A model of the chilled
water system is shown in Figure 4.
The CWS is comprised of a community
of agents that control the physical
equipment. In this system, the Chillers
(Chiller 1 and 2) are chilled water
plants, each containing a chiller unit,
one or two pumps, an expansion tank,
regulation valves, and flow and pressure
Cold valve
Chiller1
Hot valve
Load1
Load2
Load3
Load4
Chiller2
Figure 4. Chilled Water System Model
sensors. The Loads (1, 2, 3, and 4) are associated with a heat generator and correspond to
some vital or non-vital operation.
In addition, the Loads have an internal water circulation system that also provides heatexchanging services. In this manner, the heat is evacuated from the Load area using
convection heat exchange. Cold water is transmitted from the chillers into the loads through
the main supply water pipes. Hot water is transmitted from the loads back to the chillers
through the main return water pipes. The main water circulation system has valves to control
water flow and to isolate pipe ruptures.
The function of Chiller1 is to provide water cooling. Inside the Chiller1 module there is a data
repository and a connection to the physical vent fan. The vent fan increases the heat
exchange capability of the chiller. The intelligent component contained in Chiller1 is a hybrid
system comprised of simulation modules and the physical device. The vent fan system was
connected to the simulation PLC using a wireless (IEEE 802.11b) network. Chiller2 provides
water cooling capacity in simulation form only.
The main purpose of the agent system is to react to the physical system (simulation) changes
to regulate the temperature of the load components. There are several considerations in this
simple model:
• The agents need to be goal-oriented in specifying cold water requirements. This permits
agents to avoid equipment damage while optimizing cold water usage.
• The agents need to cooperate to resolve priorities since cold water may not be 100%
available to all units.
• The agents need to adapt their set points locally to respond to limited availability of cold
water.
• The agents should be aware of dangerous situations occurring in the heating process to
enable actions based on urgency and context.
• The agents should have a set of self-regulating algorithms to do low-level automatic
control.
3.3. Chilled Water System Operation
The vent fan system with VFD may be operated in several modes one of which is under the
control of an autonomous agent. In this mode, system operation changes dynamically and
without regard to the set point speed or flow specified on the VFD.
The communication layer of the vent fan system handles the cooperation with the agent-based
system and enables the vent fan system to be interrogated or controlled by agents. When the
agent-based system starts up, the axial vent fan system is registered as a resource that can
provide cooling capability. If an agent decides to use the vent fan system, it can start the fan
and command to provide airflow. The low-level controller implemented in the DSP maintains
air flow at a prescribed level based on system need or operating objectives. Fan speed is
automatically increased or decreased to maintain flow and protect the system (e.g. selfdiagnostics / prognostics) based on input from the vent fan agent. Later, the agent can check
whether the fan system is providing the airflow it is asked to provide. The agent can also use
the additional diagnostic information the fan system provides, such as system blockage. This
diagnostic information is published using OSA/CBM compliant XML pages. These XML
pages are served by the web server which is running on the vent fan system.
3.4. Dynamic Virtual Clusters
We propose the study and implementation of a self-emerging organization based on the
dynamic gathering of system capabilities. We conceive the system capabilities as fine
granular and distributed throughout the system. The dynamic gathering of capabilities is a
mechanism to achieve organizational reconfiguration. The mechanism uses dynamic
clustering of agents representative of system capabilities for short periods. User and system
tasks trigger the dynamic clustering. The task complexity determines the size and
configuration of the clusters. Gathering multiple clusters forms a cooperation domain. An
important characteristic of this mechanism is the capability of the system to aggregate
resources as needed into the emergent organizations. This organizational feature provides an
architecture that is robust and survivable. Several different mechanisms may be used to
prescribe and manage the formation of the clusters and to coordinate communication among
the components.
4. Dynamic Re-configuration
4.1. Open Systems Architecture for Condition-Based Maintenance
The utilization of open, industry standards for asset registry provides important capabilities
for integrating operating information across a manufacturing plant and even across facilities.
Recent developments have resulted in an Open Systems Architecture for Condition-Based
Maintenance that provides a framework for the real-time integration of machinery health and
prognostic information with decision support activities (Figure 2). This framework spans the
range from sensor input to decision support (www.osacbm.org). This architecture
specification is open to the public and may be implemented in a DCOM, CORBA, or
HTTP/XML environment.
4.2.
Integrated State Assessment and Control
There are significant operational and econimic benefits possible by closely coupling
machinery health (e.g. diagnostics) and anticipated health (e.g. prognostics) information with
real-time automatic control. Given a current operating state for both the machinery and the
process we can drive the system to achieve a prescribed operating state at a particular time in
the future. This future operating state may an improved state than would occur if we did not
alter the control based on machinery health information. Furthermore, the future state we
achieve is chosen to be optimal in some sense such as machinery operating cost, machinery
lifetime, or mean time before failure. The prescribed operating state of a particular machine
may be sub-optimal however, as part of an overall system, the system-wide operating state
may be optimal with regard to energy cost, revenue generation, or asset utilization. Dynamic
re-configurable agents are an effective framework for realizing the benefits of integrated
prognostics and control.
4.3 Agent Organization
The chilled water simulation system includes fluid mechanics equations to represent the water
circulation in the pipe network for both the cold and hot transmission lines. Each simulated
component generates input and output (I/O) data for the equipment.
The simulation data is stored in the controllers inside data-tag symbols. The agents use the
data tags as their interface with the physical equipment, The agents run in the controllers and
utilize data-tags to manage the operating system and agent behavior using middleware
[Maturana 2002]. Ladder logic algorithms synchronize the control events initiated by the
simulation such as cooling load requirements, for response by the suitable suite of agents.
Given a specific cooling demand on the chillers, the Chiller1 Agent may require an increase
in the speed of the vent fan to increase the water cooling rate. This action corresponds to a
first level reconfiguration. There are other levels of reconfiguration possible in this system.
For example, the agents have been programmed to react to broken pipes events.
4.4 Dynamically Created Clusters
In order to form the emergent organization, the agents register their respective capabilities in
the organizational knowledge agents known as “Directory Facilitators”.
To create decision-making
organizations, agents need
DF 1
DF 3
the ability to find suitable
DF 2
agents for particular tasks.
Agent1
Agent2
Agent5
Agent6
There
are
several
architectures to accomplish
Agent3
Agent4
this search. Examples of such
Figure 5. Distributed Structure of Directory Facilitators
approaches can be found in
the Blackboard, Matchmaker,
Brokering System, Mediator, Federated, and Acquaintance Models Architectures [Shen
2001]. Consistent with the FIPA standard, we use a Matchmaker-based architecture which is
based on Directory Facilitators (DF). On each capability request, the DF agent provides a list
of agents that coincide with the requested capability. For this model, it is required that each
capability provider register its capabilities with its local DF agent. Later, when a request for
services is initiated, the DF agent acts as a passive agent because it only provides information
services. The DF agent organization may hierarchial and agent capability registration may be
made in a breadth-first or depth-first manner. Alternatively, to avoid propogation,
information may only be propagated locally and new capabilitity requests will be processed
by local DF agents who carry out meta-level communications to discover needed capabilities.
DF agents then provide location and service capability information of remote agents to the
initial requesting agent. Relevant information may be stored locally to enhance the mount of
organization learning (Figure 5).
5. Opportunities & Challenges
Automation systems are being applied to more complex and safety-critical systems and there
is a commensurate need for increased safety and reliable operation. The software content and
complexity of automation systems continues to increase rapidly while software problems
represent a leading cause of production breakdowns [Salimen 1992].
The new paradigm of Multi-agent / Holonic systems can provide significant benefits such as
scalability, reliability, and survivability for complex critical systems. The broadscale
deployment of intelligent devices, such as an intelligent vent fan system, utilizing this
paradigm serves to reinforce these benefits and demonstrate an extremely flexible, adaptive,
and robust system. Some of the challenges that remain include the need for a consistent
framework and information model that will encompass integrating disparate agents, operating
constraints, mission planning, dynamic optimization criteria, adaptive learning, and selforganizing behavior.
The technological developments cited above combined with intelligent devices implemented
in an agent-based / Holonic framework promise to provide unprecedented capabilities for the
automation of a broad class of complex systems. A society of autonomous, cooperating
agents are well-suited to address dynamic, complex systems of the type that were previously
only the domain of biological systems.
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