ABSTRACT The existing scheduling algorithms cannot adequately support modern embedded real-time applications. An important challenge for future research is how to model and introduce control mechanisms to real-time systems to improve real-time performance, and to allow the system to adapt to changes in the environment, the workload, or to changes in the system architecture due to failures. In this paper, we pursue this goal by formulating and simulating new real-time scheduling models that enable us to easily analyse feedback scheduling with various constraints, overload and disturbance, and by designing a robust, adaptive scheduler that responds gracefully to overload with robust H∞ and feedback error learning control.
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