Robust Predictive Output-Feedback Safety Filter for Uncertain Nonlinear Control Systems

TL;DR

This paper introduces a robust output-feedback safety filter for uncertain nonlinear control systems, ensuring safety despite disturbances and noisy measurements, with theoretical guarantees and practical validation.

Contribution

It proposes a novel RPOF-SF framework combining a stable observer and predictive safety filter that minimally modifies arbitrary controllers to guarantee safety under uncertainties.

Findings

Guarantees constraint satisfaction under disturbances

Successfully applied to uncertain mass-spring-damper system

Provides safety certification without requiring full state measurements

Abstract

In real-world applications, we often require reliable decision making under dynamics uncertainties using noisy high-dimensional sensory data. Recently, we have seen an increasing number of learning-based control algorithms developed to address the challenge of decision making under dynamics uncertainties. These algorithms often make assumptions about the underlying unknown dynamics and, as a result, can provide safety guarantees. This is more challenging for other widely used learning-based decision making algorithms such as reinforcement learning. Furthermore, the majority of existing approaches assume access to state measurements, which can be restrictive in practice. In this paper, inspired by the literature on safety filters and robust output-feedback control, we present a robust predictive output-feedback safety filter (RPOF-SF) framework that provides safety certification to an arbitrary controller applied to an uncertain nonlinear control system. The proposed RPOF-SF combines a robustly stable observer that estimates the system state from noisy measurement data and a predictive safety filter that renders an arbitrary controller safe by (possibly) minimally modifying the controller input to guarantee safety. We show in theory that the proposed RPOF-SF guarantees constraint satisfaction despite disturbances applied to the system. We demonstrate the efficacy of the proposed RPOF-SF algorithm using an uncertain mass-spring-damper system.