Handling execution overruns in hard real-time control systems
In many real-time control applications, the task periods are typically fixed and worst-case execution times are used in schedulability analysis. With the advancement of robotics, flexible visual sensing using cameras has become a popular alternative to the use of embedded sensors. Unfortunately, the execution time of visual tracking varies greatly. In such environments, control tasks have a normally short computation time, but also an occasional long computation time; therefore, the use of worst-case execution time is inefficient for control performance optimization. Nevertheless, to maintain the control stability, we still need to guarantee the schedulability of the task set, even if the worst case arises. In this paper, we propose an integrated approach to control performance optimization and task scheduling for control applications where the execution time of each task can vary greatly. We present an innovative approach to overrun management that allows us to fully utilize the processor for optimizing the control performance and yet guaranteeing the schedulability of all tasks under worst-case conditions.
overrun management rate adaptation real-time scheduling