Anomaly-Aware Adaptation Approach for Self-Adaptive Cyber-Physical System of Systems Using Reinforcement Learning

A cyber-physical system of systems (CPSoS) is a system composed of multiple constituent systems that interact with both physical and cyber environments. Self-adaptivity is essential for CPSoS because it works on both cyber and physical uncertainties in various environments. Main obstacles to achieving self-adaptive CPSoS are time constraints and system anomalies. An adaptation should be processed within a certain period and it should consider anomalies caused by system changes due to mechanical faults, cyber-attacks, or emergent behaviors. However, since existing adaptation approaches cannot fully handle both aspects, this paper proposes an advanced approach, A⁴, for a self-adaptive system that can handle known anomalies in runtime. This approach learns the known anomalies before runtime and mitigates their impact when they are detected. We evaluated the A⁴ approach for virtual and physical CPSoS and showed that A⁴ was more efficient than other approaches.