Designing image-based control systems considering workload variations

We consider the problem of designing an Image- Based Control (IBC) application mapped to a multiprocessor platform. Sensing in IBC consists of compute-intensive image processing algorithms whose execution times are dependent on image workload. The challenge is that the IBC systems have a high (worst-case) workload with significant workload variations. Designing controllers for such IBC systems typically consider the worst-case workload that results in a long sensing delay with suboptimal quality-of-control (QoC). The challenge is: how to improve the QoC of IBC for a given multiprocessor platform allocation? We present a controller synthesis method based on a Markovian jump linear system (MJLS) formulation considering workload variations. Our method assumes that system knowledge is available for modelling the workload variations as a Markov chain. We compare the MJLS-based method with two relevant control paradigms - LQR control considering worst-case workload, and switched linear control - with respect to QoC and available system knowledge. Our results show that taking into account workload variations in controller design benefits QoC. We then provide design guidelines on the control paradigm to choose for an IBC application given the requirements and the system knowledge.