Event-Triggered Safety-Critical Control for Systems with Unknown Dynamics

TL;DR

This paper proposes an event-triggered safety-critical control framework for systems with unknown dynamics, utilizing adaptive high order control barrier functions and quadratic programming to ensure safety and stability in real-time.

Contribution

It introduces a novel adaptive control approach with event-triggered updates for safety-critical control of systems with unknown dynamics using HOCBFs.

Findings

Effective safety guarantees demonstrated on adaptive cruise control

Outperforms classical time-driven control approaches

Real-time adaptive dynamics improve safety and stability

Abstract

This paper addresses the problem of safety-critical control for systems with unknown dynamics. It has been shown that stabilizing affine control systems to desired (sets of) states while optimizing quadratic costs subject to state and control constraints can be reduced to a sequence of quadratic programs (QPs) by using Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs). Our recently proposed High Order CBFs (HOCBFs) can accommodate constraints of arbitrary relative degree. One of the main challenges in this approach is obtaining accurate system dynamics, which is especially difficult for systems that require online model identification given limited computational resources and system data. In order to approximate the real unmodelled system dynamics, we define adaptive affine control dynamics which are updated based on the error states obtained by real-time sensor measurements. We define a HOCBF for a safety requirement on the unmodelled system based on the adaptive dynamics and error states, and reformulate the safety-critical control problem as the above mentioned QP. Then, we determine the events required to solve the QP in order to guarantee safety. We also derive a condition that guarantees the satisfaction of the HOCBF constraint between events. We illustrate the effectiveness of the proposed framework on an adaptive cruise control problem and compare it with the classical time-driven approach.