|InterJournal Complex Systems, 902
|Manuscript Number: |
Submission Date: 2004
|Dynamic Reconfiguration of Complex Systems to Avoid Failure|
There are increasing pressures for low-cost, reliable automation systems even though system applications are becoming increasingly complex and deployed in a broader range of critical applications. Advanced automation techniques employing intelligent agent technology promises to provide important capabilities for managing complexity and to meet critical application requirements for operating performance, fault-tolerance, reliability, and survivability. Multi-agent Systems in a Distributed Artificial Intelligence (DAI) framework provides important new capabilities to enhance automation system performance across a large class of applications. A multi-agent system approach encapsulates the fundamental behavior of intelligent devices as autonomous components. These components exhibit primitive attitudes to act on behalf of equipment or complex processes to realize local agent goals as well as agent cluster goals or system-level overarching goals. Goals may emerge dynamically and are agreed upon by the agents through negotiation. For example, through agent collaboration (i.e. distributed diagnostics) a leaking pipe, degraded bearing or worn pump impellor may detected. This may establish a new goal to reconfigure the flow path and the associated controllers to avoid using these components under extreme conditions. The sequence of actions required to establish the required sub-goals and transition to the new operating regime is performed automatically through agent collaboration. Using this approach, we have implemented an agent-based chilled water system modeled after a shipboard system. This laboratory system is comprised of over 50 valve, load, and pump agents and operates in a highly distributed framework. There is no central controller and the system has been shown to dynamically establish new goals and automatically re-configure system operation to minimize damage and to meet critical cooling needs. New operating goals may emerge based on equipment prognostics or predicted component failure to avoid reaching a predicted or probable state that is undesirable (e.g. catastrophic component failure). The potential undesirable states may be efficiently avoided while continuing to satisfy critical system needs (e.g. radar cooling). This 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, and dynamic re-configuration.
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