Development of software component architecture for motor-driven power steering control system using AUTOSAR methodology

Kim, Jae-Woo; Lee, Kyung-Jung; Ahn, Hyun-Sik

doi:10.1109/iccas.2015.7364695

Cited by 7 publications

(1 citation statement)

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“…AUTOSAR is the de facto standard software architecture for automobile control systems, covering a wide range of applications such as engine management, motor-driven power steering, and advanced driver assistance systems [1][2][3]. In the AUTOSAR standard, a control system is designed as a set of software components, which are the units of software packaging and deployment.…”

Section: Introductionmentioning

confidence: 99%

AUTOSAR Runnable Periods Optimization for DAG-Based Complex Automobile Applications

Daeho¹,

Kim

2020

Applied Sciences

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When developing an automobile control application, its scheduling parameters as well as the control algorithm itself should be carefully optimized to achieve the best control performance from given computing resources. Moreover, since the wide acceptance of the AUTOSAR standard, where finer-granular scheduling entities (called runnables) rather than the traditional real-time tasks are used, the number of scheduling parameters to be optimized is far greater than the traditional task-based control systems. Hence, due to the vast problem space, it is not feasible to reuse existing time-consuming search-based optimization methods. With this motivation, this paper presents an analytical codesign method for deciding runnable periods that minimize given control cost functions. Our solution approach, based on the Lagrange multiplier method, can find optimized runnable periods in polynomial times due to its analytical nature. Moreover, our evaluation results for synthesized applications with varying complexities show that our method performs significantly better (12% to 59% of control cost reductions) than a state-of-the-art evolutionary algorithm. To the best of our knowledge, this study is one of the first attempts to find runnable periods that maximize a given system’s control performance.

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Section: Introductionmentioning

confidence: 99%