A Framework for Controlled Self-optimisation in Modular System Architectures

Organic Computing tackles design issues of future technical systems by equipping them with self-x properties. A key self-x feature is self-optimisation, i.e. the system's ability to adapt its dynamic behaviour to its current environment and requirements. In this article, it is shown how self-optimisation can be realised in a safe and goal-directed way, but also why it has to be enhanced and embedded into a suitable, modular system architecture. Then, a suitable framework for controlled self-optimisation is developed, which enables the system designer to give a priori guarantees of important dynamic system properties, and which ensures the system's ability to cope dynamically with anomalies. The key features are online machine learning, which is complemented by incremental, local regularisation in a local Observer/Controller architecture as well as expressing anomalies by health signals, which are exploited to guide the learning process dynamically in order to achieve fast, but safe learning.