Towards Real-Time Robust Adaptive Control for Non-Stationary Environments

Robust adaptive control (RAC) approaches have many state-of-the-art capabilities; however, they cannot provide near-real-time performance, especially for novel fault conditions. To address such issues, we introduce a framework called Intelligent Robust Adaptive Control (IRAC) for switched systems, which is based on randomized blending of controllers' actions. We show how our approach provides guarantees for stability, robustness, adaptivity, and nearreal-time performance. We empirically compare worst-case performance of our approach with other methods, using a \hypothetical baseline" that takes some time for control inference but provides exact control outputs. This shows the impact on near-real-time control of the time for inference, and illustrates the benefits of IRAC.