Safety-Critical Adaptive Control with Nonlinear Reference Model Systems

TL;DR

This paper introduces a novel adaptive control architecture for uncertain nonlinear systems that guarantees safety by ensuring system trajectories stay within prescribed bounds of a reference model, eliminating the need for ad-hoc tuning.

Contribution

It proposes a new adaptive control framework that enforces safety constraints in nonlinear systems without prior bounds on uncertainties, enhancing reliability and safety.

Findings

Achieves prescribed safety performance in uncertain nonlinear systems.

Eliminates the need for tuning adaptation rates through safety constraints.

Demonstrates effectiveness via numerical example.

Abstract

In this paper, a model reference adaptive control architecture is proposed for uncertain nonlinear systems to achieve prescribed performance guarantees. Specifically, a general nonlinear reference model system is considered that captures an ideal and safe system behavior. An adaptive control architecture is then proposed to suppress the effects of system uncertainties without any prior knowledge of their magnitude and rate upper bounds. More importantly, the proposed control architecture enforces the system state trajectories to evolve within a user-specified prescribed distance from the reference system trajectories, satisfying the safety constraints. This eliminates the ad-hoc tuning process for the adaptation rate that is conventionally required in model reference adaptive control to ensure safety. The efficacy of the proposed control architecture is also demonstrated through an illustrative numerical example.