This paper presents a control/scheduling co-design framework that integrates 5G Configured Grant (CG) scheduling with networked control systems (NCS) design. The objective is to minimize the hyperperiod induced by multiple, application-specific sampling periods, which determines the schedule table size and memory footprint at the base station, subject to control quality and wireless resource limits. Hyperperiod minimization under control and resource constraints is nontrivial due to the combinatorial nature of discrete sampling period choices. To address this challenge, we propose a two-stage Hyperperiod-Minimization-oriented Period Assignment (HMPA) method. In the first stage, HMPA performs a feasibility-oriented period search within candidate period sets constructed from restricted primes and exponents, which bound the hyperperiod. In the second stage, a hyperperiod refinement procedure exploits remaining resource slack to further reduce the hyperperiod while preserving feasibility. Experiments demonstrate the efficiency of the proposed framework in terms of finding solutions with significantly reduced hyperperiods.

This is the Replicated Computational Results (RCR) Report for the article “ Testing Abstractions for Cyber-Physical Control Systems .” The article empirically studies how substituting different components in Cyber-Physical Systems (CPSs) testing with simulators impacts the fault-exposition. This RCR report describes the artefacts used in the article, how to use the testing setups used in the article and how to reproduce the empirical results of the article.

Control systems are ubiquitous and often at the core of Cyber-Physical Systems, like cars and aeroplanes. They are implemented as embedded software that interacts in closed loop with the physical world through sensors and actuators. As a consequence, the software cannot just be tested in isolation. To close the loop in a testing environment and root causing failure generated by different parts of the system, executable models are used to abstract specific components. Different testing setups can be implemented by abstracting different elements: The most common ones are model-in-the-loop, software-in-the-loop, hardware-in-the-loop, and real-physics-in-the-loop. In this article, we discuss the properties of these setups and the types of faults they can expose. We develop a comprehensive case study using the Crazyflie, a drone whose software and hardware are open source. We implement all the most common testing setups and ensure the consistent injection of faults in each of them. We inject faults in the control system and we compare with the nominal performance of the non-faulty software. Our results show the specific capabilities of the different setups in exposing faults. Contrary to intuition and previous literature, we show that the setups do not belong to a strict hierarchy, and they are best designed to maximize the differences across them rather than to be as close as possible to reality.