2021 L4DC L4DC 2021

Data-driven design of switching reference governors for brake-by-wire applications

Abstract

Nowadays, data are ubiquitous in control design and data-driven approaches are in constant evolution. By following such a trend, in this paper we propose an approach for the direct data-driven design of switching reference governors for nonlinear plants and we apply it within a brake-by-wire application. The braking system is assumed to be pre-stabilized via a simple unknown controller attaining unsatisfactory performance in terms of output tracking and actuator effort. Hence, the reference governor is used to improve the overall closed-loop behavior, resulting into safer maneuvering. Preliminary results on a simulation setup show the effectiveness of the proposed strategy, thus motivating further investigation on the topic.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Machine Learning and Robotics
🧭 Keyword Pioneer — switching control
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Machine Learning, Mathematics & Optimization, Reinforcement Learning, Robotics