Safe Adaptation with Multiplicative Uncertainties Using Robust Safe Set Algorithm
This work addresses safety guarantees for autonomous systems adapting to dynamic uncertainties, which is an incremental improvement in robust control for robotics.
The paper tackles the problem of ensuring safety for adaptive robotic systems with parametric uncertainties by proposing a robust safe control methodology using set-based constraints and an optimization-based control synthesis. The method is validated through simulations on a two-link manipulator, demonstrating effectiveness in generating provably safe control.
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.