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Online control optimization using load driven scheduling

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 becomes a popular alternative to the use of embedded sensors. However, the execution time of visual tracking varies greatly. In this paper, we integrate load driven online scheduling with direct digital designs to optimize the control performance as a function of varying workload.