Feasibility-guided learning for constrained optimal control problems
Files
Accepted manuscript
Date
2020-12-14
Authors
Xiao, Wei
Belta, Calin
Cassandras, Christos G.
Version
Accepted manuscript
OA Version
Citation
Wei Xiao, Calin A Belta, Christos G Cassandras. 2020. "Feasibility-Guided Learning for Constrained Optimal Control Problems." 2020 59th IEEE Conference on Decision and Control (CDC). 2020 59th IEEE Conference on Decision and Control (CDC). 2020-12-14 - 2020-12-18. https://doi.org/10.1109/cdc42340.2020.9303857
Abstract
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.