Encrypted extremum seeking for privacy-preserving PID tuning as-a-Service

TL;DR

This paper introduces a privacy-preserving PID tuning method using homomorphic encryption combined with extremum seeking, enabling confidential, data-driven control optimization in cloud-based or wireless environments.

Contribution

It develops a novel encrypted extremum seeking algorithm that adapts traditional methods for privacy preservation in cloud-based control tuning.

Findings

Successfully tunes PID controllers without revealing sensitive data.

Works effectively across various plant models.

Maintains performance without excessive conservatism.

Abstract

Wireless communication offers many benefits for control such as substantially reduced deployment costs, higher flexibility, as well as easier data access. It is thus not surprising that smart and wireless sensors and actuators are increasingly used in industry. With these enhanced possibilities, exciting new technologies such as Control-as-a-Service arise, where (for example) controller design or tuning based on input-output-data can be outsourced to a cloud or mobile device. This implies, however, that sensitive plant information may become available to service providers or, possibly, attackers. Against this background, we focus on privacy-preserving optimal PID tuning as-a-Service here. In particular, we combine homomorphic encryption with extremum seeking in order to provide a purely data-driven and confidential tuning algorithm. The encrypted realization requires several adaptions of established extremum seekers. These encompass relative parameter updates, stochastic gradient approximations, and a normalized objective function. As a result, and as illustrated by various numerical examples, the proposed encrypted extremum seeker is able to tune PID controllers for a wide variety of plants without being too conservative.