Safe Adaptation with Multiplicative Uncertainties Using Robust Safe Set Algorithm

TL;DR

This paper introduces a robust safe control method for adaptive robotic systems with uncertainties, ensuring safety through set-based constraints and energy functions, validated by simulations on a two-link manipulator.

Contribution

It presents a novel control approach combining safety indices and optimization to guarantee safety during adaptation in systems with parametric uncertainties.

Findings

Effective safe control demonstrated in simulations

Provably safe operation with parametric uncertainties

Robustness to model variations confirmed

Abstract

Maintaining safety under adaptation has long been considered to be an important capability for autonomous systems. As these systems estimate and change the ego-model of the system dynamics, questions regarding how to develop safety guarantees for such systems continue to be of interest. We propose a novel robust safe control methodology that uses set-based safety constraints to make a robotic system with dynamical uncertainties safely adapt and operate in its environment. The method consists of designing a scalar energy function (safety index) for an adaptive system with parametric uncertainty and an optimization-based approach for control synthesis. Simulation studies on a two-link manipulator are conducted and the results demonstrate the effectiveness of our proposed method in terms of generating provably safe control for adaptive systems with parametric uncertainty.