Tighter Dimensioning of Heterogeneous Multi-Resource Autonomous CPS with Control Performance Guarantees

Abstract

In modern autonomous systems, there is typically a large number of connected components realizing complex functionalities. For example, in autonomous vehicles (AVs), there are tens of millions of lines of code implemented on hundreds of sensors, controllers, and actuators. AVs have been deployed, mostly in trials and restricted environments, showing that substantial progress has been made in functionality development. However, they are still faced with two major challenges: (i) performance guarantee of safety-critical functions under all possible scenarios; (ii) functionality implementation with limited resources. These two challenges are conlicting because safety guarantees necessitate a worst-case analysis that is often very pessimistic for complex hardware/software systems, and thus require more resources. To address this, we study an abstraction of a heterogeneous cyber-physical systems architecture consisting of a mix of high-and low-quality resources, such as time-and eventtriggered resources, or wired and wireless resources. We show that by properly managing such a mix of resources and formulating a formal veriication (model checking) problem, it is possible to tightly dimension the high-quality resource to the minimum (50% in certain cases) while providing control performance guarantees.

Control Performance Optimization for Application Integration on Automotive Architectures

“…Each application represents a control function for which the plant model is derived from the automotive domain. These plants represent DC motor speed control [32], DC motor position control [31], car suspension [32] and cruise control systems [32]. Given a sampling period (equal to the application task and message repetition periods), we design a controller assuming a specific delay.…”

“…Scheduling of control applications with the goal of control performance optimization has drawn significant attention in recent times [2], [31], [32].…”