We formulate a reference governor algorithm for hybrid systems modeled as hybrid equations, in which the continuous dynamics are governed by a constrained differential equation and the discrete dynamics by a constrained difference equation. Basic definitions, models, and properties of the proposed hybrid reference governor approach are introduced and a time-based implementation is formulated. We apply the methodology to hybrid equations with linear right-hand side in both the differential and difference equations and with explicit logic variables. We illustrate the approach in examples
Where: Atlanta, GA
MERL Contacts: Karl Berntorp; Scott A. Bortoff; Ankush Chakrabarty; Stefano Di Cairano; Christopher R. Laughman; Marcel Menner; Rien Quirynen; Abraham P. Vinod; Avishai Weiss
Research Areas: Control, Machine Learning, OptimizationBrief
Date: June 8, 2022 - June 10, 2022
- At the American Control Conference in Atlanta, GA, MERL presented 9 papers on subjects including autonomous-vehicle decision making and motion planning, realtime Bayesian inference and learning, reference governors for hybrid systems, Bayesian optimization, and nonlinear control.