Feasibility-Guided Learning for Constrained Optimal Control Problems

摘要

Optimal control problems with constraints ensuring safety can be mapped onto a sequence of real time optimization problems through the use of Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs). One of the main challenges in these approaches is ensuring the feasibility of the resulting quadratic programs (QPs) if the system is affine in controls. In this paper, we improve the feasibility robustness (i.e., feasibility maintenance in the presence of time-varying and unknown unsafe sets) through the definition of a High Order CBF (HOCBF); this is achieved by a proposed feasibility-guided learning approach using machine learning techniques. The effectiveness of the proposed feasibility-guided learning approach is demonstrated on a robot control problem.