Modelling of Safety-Related Timing Constraints for Automotive Embedded Systems

Ljungkrantz, Oscar; Lönn, Henrik; Blom, Hans W.; Ekelin, Cecilia; Karlsson, Daniel

doi:10.1007/978-3-642-33675-1_17

Cited by 3 publications

(1 citation statement)

References 4 publications

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“…For example, in electric motor control, the magnitude of the input current is always limited. Third, when an application C i is safety-critical, there is usually a maximum settling time (i.e., settling deadline) s max i that cannot be violated [11]. This is the reason why the worst-case settling time is considered as discussed above.…”

Section: Constraintsmentioning

confidence: 99%

Cache-aware task scheduling for maximizing control performance

Chang

Roy

et al. 2018

2018 Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE)

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Embedded control applications are widely implemented on small, low-cost and resource-constrained microcontrollers, e.g., in the automotive domain. Conventionally, control algorithms are designed using model-based approaches, without considering the details of the implementation platform. This leads to inefficient utilization of the resources. With the emergence of the cyber-physical system (CPS)-oriented thinking, there has lately been a strong interest in co-design of control algorithms and their implementation platforms. Some recent efforts have shown that a schedule on multiple applications with more onchip cache reuse is able to improve the control performance. However, it has not been studied how the control performance can be maximized for a given schedule and how an optimal schedule can be computed. In this work, we propose a twostage framework to compute the schedule maximizing the overall control performance of all the applications. First, a holistic controller design taking all the sampling periods and sensingto-actuation delays in a schedule into account is presented, aiming to maximize the overall control performance. Second, a hybrid search algorithm for discrete decision space is reported to efficiently compute an optimal schedule. Experimental results on a case study with multiple automotive applications show that a significant improvement of 10-20% in control performance can be achieved by the proposed cache-aware scheduling approach.

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Section: Constraintsmentioning

confidence: 99%