DISA

When? 5 June, 14.00-16.00
Where? Onsite: D2272 and via zoom

Abstract

Engineering Proactive Self-Adaptation in Cyber-Physical Systems under Uncertainty – Farid Edrisi
Cyber-Physical Systems (CPS) are characterized by their complex interactions between computational and physical processes, uncertainty, and continuous change. Engineering such systems is challenging because their operational circumstances cannot be fully characterized. Self-adaptation was introduced to address uncertainty and continuous change. Most existing self-adaptation approaches are reactive; the system monitors for goal violations and initiates corrective action once a violation is detected. In CPS, however, some actions are irreversible, and delayed or incorrect responses can cause safety issues. This becomes even more challenging under runtime uncertainties, such as incomplete or inaccurate information during operation, where the system may only react to visible symptoms while the cause remains undetected, leading to repeated violations instead of stable operation. 

To deal with these limitations, this thesis advances the engineering of proactive self-adaptation in CPS under uncertainty. Rather than responding to violations, proactive adaptation shifts the decision point ahead of the violation. It anticipates how conditions may evolve and acts while corrective actions are still effective and options remain open. This also gives the system the flexibility to refine adaptation decisions as new runtime information becomes available. 

Following Design Science Research methodology, the thesis introduces three architectural contributions. PANCS is a reusable reference architecture that integrates Adaptive Model Predictive Control into the MAPE-K loop, enabling anticipatory adaptation in nonlinear CPS by reasoning predictively within physical and temporal constraints while continuously refining its models as system dynamics evolve. A Digital Twin-augmented MAPE-K architecture extends this capability to the managing system level, introducing a continuously updated virtual replica of the complete system (managed system, managing system, and execution environment) that supports runtime revision of adaptation rules and reasoning under some conditions that were never anticipated at design time. The EA Blueprint Pattern addresses the collaborative CPS scale by providing an architectural pattern for constructing and evolving digital twin of the organization from existing enterprise architecture models, enabling proactive reasoning about structural dynamics, interdependencies, and organizational change at an abstraction level where dynamic models are insufficient. 

The contributions are demonstrated and evaluated across multiple CPS domains, including autonomous ground vehicles, robotic manipulators, and collaborative CPS settings. Results show that under proactive self-adaptation, when supported by predictive reasoning, continuous model maintenance, and principled architectural structure, operational integrity can be effectively sustained over time as system and environmental conditions evolve.