Authors - Azad Mohammed Shaik Abstract - A real-time operating system (RTOS) should be able to recover from interruptions. Since RTOS systems are used in safety-critical environments, this function is essential for ensuring system availability and reliability. However, while many of the current anomaly detection techniques can detect faults, they do not provide any means for recovery. Therefore, in this paper, I propose a self-repairing RTOS framework that utilizes reinforcement learning (RL) to automatically select the best course of action to take when an anomalous event arises. I propose a Q-Learning agent that learns to recover from six types of common faults, including: sensor degradation, stuck sensor, priority inversion, memory leaks, sporadic overloads, and task starvation. The framework is built on FreeRTOS, and the agent utilizes an 8-dimensional state space and the six different types of recovery options available for each fault. The overall success rate of the system was 99.2 % after 5,000 training episodes, with average success rates of 98.0 % and 99.9 % when handling individual faults. The RL agent completely prevented system crashes and returned the system to normal operation within an average of 0.06 ms after an interruption occurred. The training results provide strong evidence that the model learned to operate effectively and consistently, with its success rate improving from 97.0 % during early training stages to 100 % after training was completed. Therefore, this study demonstrates a practical, production-ready method to implement autonomous fault recoveries in RTOSs in automotive applications. To our knowledge, this is the first successful implementation of RL for autonomous, self-repairing behaviors in this area.
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