Safe Adaptation with Multiplicative Uncertainties Using Robust Safe Set Algorithm

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 system dynamics, questions regarding how develop guarantees such continue interest. We propose a novel robust safe control methodology that uses set-based constraints make robotic with dynamical uncertainties safely adapt operate in its environment. The method consists designing scalar energy function (safety index) adaptive parametric uncertainty optimization-based approach synthesis. Simulation studies on two-link manipulator are conducted results demonstrate effectiveness our proposed terms generating provably uncertainty.