Autonomic Computing for Decentralised Production Systems (ACDPS)
Production systems cover a large variety of industrial automation systems, from manufacturing systems over individual robotic systems to large-scale networks of sensors and actuators. Modern production systems need to address severe demands originating from growing eco-nomic competition, intensifying environmental concerns and emerging technological opportunities. This renders the operation of production systems a technological challenge in which the most recent advances in information technology and insights in complex adaptive systems must be applied.
A key technological issue is the closed and rigid nature of the prevailing solutions, particularly regarding software. The coordination and control software systems are unable to adapt themselves autonomously in an environment that is dynamic by nature, in terms of configuration as well as in terms of the software entities that need to cooperate and be integrated. As such, software in production systems shares today?s challenges with mainstream computing in which the complexity of the software forces developers to build systems that are intrinsically able to manage themselves autonomously. A widely accepted research tendency, looking into these issues, is in the domain of autonomic computing. In autonomic systems, the requirement of self-management is paramount. In this context, the term self-X is a common denominator for system properties such as self-optimizing, self-organizing, self-healing and self-recovering. The terms autonomic systems and self-X systems are used as synonyms hereafter.
The goal of this project is to address self-X in decentralized production systems and to address software technology allowing the autonomic systems and components to self-manage and adapt autonomously. The resulting systems will be open such that integration of components and partial solutions, originating from various sources and with diverse levels of maturity and sophistication, becomes feasible. Moreover, these systems autonomously support their deployment (ramp-up and tuning) and co-evolution with their environment (adaptation), significantly reducing external system maintenance efforts. Likewise, the developed technology enables the effective design, rapid deployment and sustainable operation of systems that are more complex and sophisticated than currently achievable, possibly comprising huge numbers of (small and simple) components.