Autonomous systems face significant challenges due to fluctuating resources and unstable environments, where traditional redundancy strategies for resilience can be inefficient. We present the Reflex pattern, inspired by biological reflexes, promoting system resilience by dynamically adapting to changing resource conditions. By switching between complex and resource-efficient algorithms based on availability, the pattern optimizes efficient resource utilization without extensive redundancy, ensuring essential functionalities remain operational under constraints. To facilitate adoption, we introduce ReflexLang, a domain-specific language (DSL) enabling automated code generation for reflex-pattern-based systems. We validate the pattern's effectiveness in a drone image processing scenario, demonstrating its potential to enhance operational integrity and resilience.

The dynamic reconfiguration of vehicular control and management systems to adapt to different scenarios, particularly those with conflicting design goals, remains a challenging task. In this context, we propose a reference architecture and a generic process to integrate advanced gain scheduling with a feedback loop that continually updates configuration lookup tables to generate scenario-related configurations at runtime. For demonstration purposes, our approach is applied to a Hyperloop vehicle's magnetic suspension system to guarantee simultaneously optimized accuracy of control, energy consumption and passenger comfort for a multitude of scenarios. Additionally, the feedback mechanism increases resilience against mechanical failures, marking an advancement in vehicular system adaptability and reliability.

Vehicular systems operate under diverse and often unpredictable operating conditions, necessitating robust adaptability to manage these uncertainties effectively. Traditionally, adaptability is linked to software capabilities, with hardware platforms frequently overlooked as variables for enhancement. This paper introduces a novel optimization framework aimed at improving the adaptability of vehicular systems during the hardware design phase. We develop an Adaptability Score and integrate it into a Multi-objective Optimization (MOO) framework, presenting its mathematical formulation and its application in hardware optimization. The efficacy of this framework is evaluated through its application to a real-world Hyperloop magnetic levitation system. Our results demonstrate that, compared to a baseline hardware configuration optimized without considering the Adaptability Score, our approach maintains multi-objective system performance across various anomalous scenarios by enhancing the system’s capability to adapt through improved hardware-software synergy.

In the very recent past, there has been a trend for passenger transport towards electrification of the vehicles to reduce greenhouse gas emissions. However, due to the low energy density of battery technology, electrification of airplanes is not possible with current technologies. Here, Hyperloop systems can offer a climate-friendly alternative to short-haul flights but face some technical challenges to be resolved. In contrast to conventional rail systems, the Hyperloop concept uses magnetic propulsion and levitation to operate and has no physical contact with the environment. Consequently, mechanical backup solutions do not suffice to avoid catastrophic events in case of failure. Software solutions must, therefore, ensure fail-operational behavior, which requires autonomous adaptability to uncertain states. The MAPE-K approach offers a solution to achieve such adaptability. In this paper, we present a hierarchical architecture that combines the MAPE-K concept with the Simplex concept to achieve self-adaptive behavior. We impose our autonomous architecture on the controller design for the levitation system of a Hyperloop pod and show that this controller, designed using our methodology, outperforms a conventional PID controller by up to 76%.