Networked Online Learning for Control of Safety-Critical Resource-Constrained Systems based on Gaussian Processes

TL;DR

This paper introduces a cloud-based Gaussian process online learning method for safety-critical control systems, ensuring bounded prediction errors and efficient data management under resource constraints.

Contribution

It presents a novel networked learning framework that combines Gaussian processes with remote data management, guaranteeing bounded errors and addressing bandwidth and delay issues.

Findings

Successfully demonstrated in simulation

Guarantees high-probability bounded tracking error

Efficient data transmission scheme implemented

Abstract

Safety-critical technical systems operating in unknown environments require the ability to quickly adapt their behavior, which can be achieved in control by inferring a model online from the data stream generated during operation. Gaussian process-based learning is particularly well suited for safety-critical applications as it ensures bounded prediction errors. While there exist computationally efficient approximations for online inference, these approaches lack guarantees for the prediction error and have high memory requirements, and are therefore not applicable to safety-critical systems with tight memory constraints. In this work, we propose a novel networked online learning approach based on Gaussian process regression, which addresses the issue of limited local resources by employing remote data management in the cloud. Our approach formally guarantees a bounded tracking error with high probability, which is exploited to identify the most relevant data to achieve a certain control performance. We further propose an effective data transmission scheme between the local system and the cloud taking bandwidth limitations and time delay of the transmission channel into account. The effectiveness of the proposed method is successfully demonstrated in a simulation.