|InterJournal Complex Systems, 553
|Manuscript Number: |
Submission Date: 20517
|Managed Complexity in An Agent-based Vent Fan Control System Based on Dynamic Re-configuration|
Category: Brief Article
Developments in advanced control techniques are occurring in parallel with advances in sensors, algorithms, and architectures that support next-generation condition-based maintenance (CBM) 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 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, re-configurable 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. 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.
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