Analysis and Design of Uncertain Cyber-Physical Systems

TL;DR

This paper discusses the complexities and uncertainties in the analysis and design of modern cyber-physical systems, emphasizing the need for new models and methods to handle diverse sources of uncertainty in evolving, AI-driven environments.

Contribution

It highlights the various sources of uncertainty in CPS and advocates for the development of new models and analysis techniques to better manage these uncertainties in complex, AI-enabled systems.

Findings

Uncertainty sources include manufacturing tolerances, sensor noise, and non-determinism.

Evolving CPS features increased complexity due to connectivity and AI.

New models are needed to analyze systems with high environmental uncertainty.

Abstract

Several sources of uncertainty have to be taken into account in the analysis and design of CPS. The set of parameters used in the model of the physical plant of a CPS may be uncertain due, for example, to manufacturing processes that are precise up to some bounded tolerance. Physical quantities are sensed by electronic components that add noise to the sensed signals. Abstraction of the physical world, which is often necessary to limit the complexity of the models used in analysis and at run-time in decision-making, leads to non-determinism. The cyber side of a CPS, which includes both hardware and software components, exposes several types of uncertainty such as failures, latency, and implementation errors. Design processes and tools allow engineers to minimize the impact of these types of uncertainty, and to deliver systems which can be operated with an acceptable level of risk. In the past several years, cyber-physical systems have evolved, primarily due to pervasive connectivity, miniaturization, cost-effectiveness of hardware, and advances in the area of Artificial Intelligence. This new class of applications features an environment that is much more complex to model than traditional physical systems due not only to their scale, but also to new sources and types of uncertainty. Consider, for example, the typical case of echo chambers which is attributed to the effect that machine learning algorithms have on the bias of people. Such behavior is not easily predictable because of high uncertainty in the environment (people), which is only approximately represented by machine learning models, but that is inherently due to lack of knowledge. New models and analysis methods are therefore needed to capture different types of uncertainties, and to analyze these new classes of systems.