Implementing Optimization-Based Control Tasks in Cyber-Physical Systems With Limited Computing Capacity

TL;DR

This paper presents a robust early termination optimization method for cyber-physical systems that allows control tasks to operate with smaller sampling periods despite limited computing resources, improving control performance.

Contribution

It introduces a feasible early stopping approach for optimization in control tasks, enabling better real-time performance under computational constraints.

Findings

Optimization iterations can be stopped at any time with guaranteed feasibility.

Control tasks can operate with smaller sampling periods.

Improved control performance despite limited computing capacity.

Abstract

A common aspect of today's cyber-physical systems is that multiple optimization-based control tasks may execute in a shared processor. Such control tasks make use of online optimization and thus have large execution times; hence, their sampling periods must be large as well to satisfy real-time schedulability condition. However, larger sampling periods may cause worse control performance. The goal of our work is to develop a robust to early termination optimization approach that can be used to effectively solve onboard optimization problems involved in controlling the system despite the presence of unpredictable, variable, and limited computing capacity. The significance of the developed approach is that the optimization iterations can be stopped at any time instant with a guaranteed feasible solution; as a result, optimization-based control tasks can be implemented with a small sampling period (and consequently with a minimum degradation in the control performance).