Agent-Based Supervision for Service-Oriented Industrial Cyber-Physical Systems

Abstract

As industrial cyber-physical systems (ICPSs) consolidate as mature automation solutions, questions about leveraging their flexibility and resilience to maintain performance have arisen. This article presents a methodology that employs industrial agents (IAs) to supervise and reconfigure service-oriented ICPSs, thereby enabling adaptability and improving performance. The main tasks of the IAs include: 1) selecting the best service for the ICPS, from a predefined component library, based on performance metrics and computational load; and 2) monitoring the performance of the chosen service online, carrying out a new selection process if poor performance is detected, thus enabling dynamic service selection. A key contribution is the introduction of a dynamic reconfiguration capability to the system, allowing it to adapt in real-time to changing conditions, thereby addressing a limitation of existing service-oriented architectures. Results show an improvement in system adaptability and performance, demonstrating the potential of agent-based supervision to support the operation of ICPSs and positioning the proposed methodology as an initial step toward the development of more resilient and efficient ICPSs.