A risk analysis framework for real-time control systems

TL;DR

This paper introduces a Monte Carlo simulation framework and a robustification algorithm for analyzing and enhancing the safety and reliability of real-time control systems in critical applications.

Contribution

It presents a general, control algorithm-agnostic framework for risk analysis and robustness enhancement in real-time control systems.

Findings

Effective risk analysis of non-linear model predictive control algorithms.

Framework applicable to various control algorithms and safety-critical systems.

Demonstrated robustness improvements through the proposed techniques.

Abstract

We present a Monte Carlo simulation framework for analysing the risk involved in deploying real-time control systems in safety-critical applications, as well as an algorithm design technique allowing one (in certain situations) to robustify a control algorithm. Both approaches are very general and agnostic to the initial control algorithm. We present examples showing that these techniques can be used to analyse the reliability of implementations of non-linear model predictive control algorithms.