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Pipelined image-based control uses parallel instances of its image-processing algorithm in a pipelined fashion to improve the quality of control. A performance-oriented control design improves the controller settling time with each additional processing resource, which creates a resources-performance trade-off. In real-life applications, it is common to have a continuous-time model with additive uncertainties in one or more parameters that may affect the controller performance and the aforementioned trade-off. We present a robustness analysis framework for performance-oriented pipelined controllers with additive model uncertainties. We present a technique to obtain discrete-time uncertainties based on the continuous-time uncertainties for given uncertainty bounds. To benchmark such uncertainty bounds for a real system, we consider uncertainties in one element of the system, potentially caused by multiple uncertain parameters in the model. Robustness and its impact in the trade-off analysis are studied. We also provide a robustness-oriented pipelined controller design that takes into account the benchmarked uncertainties. Our results show that in performance-oriented designs, the tolerable uncertainties for a pipelined controller decrease when increasing the number of pipes. In robustness-oriented designs, the controller robustness is enhanced with each newly added pipe. We show the feasibility of our technique by implementing a realistic example in a Hardware-in-the-Loop simulation.

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Abstract-In this work, we propose a design flow for efficient implementation of embedded feedback control systems targeted for multi-core platforms. We consider a composable tile-based architecture as an implementation platform and realise the proposed design flow onto one instance of this architecture. The proposed design flow implements the feedback loops in a datadriven fashion leading to time-varying sampling periods with short average sampling period. Our design flow is composed of two phases: (i) representing the timing behaviour imposed by the platform by a finite and known set of sampling periods, which is achieved exploiting the composability of the platform, and (ii) a linear matrix inequality (LMI) based platform-aware control algorithm that explicitly takes the derived platform timing characteristics and the shorter average sampling period into account. Our results show that the platform-aware implementation outperforms traditional control design flows (i.e., almost 2 times) in terms of quality of control (QoC).

Comparing platform-aware control design flows for composable and predictable TDM-based execution platforms. ACM Transactions on Design Automation of Electronic Systems, 24(3), [32].