Document Type
Conference Proceeding
Abstract
A question that faces data-driven autonomous systems is verification that they will perform in a safe manner despite changes in the environment on which they act over time or incomplete knowledge of the system model. This work analyzes closed-loop stability of nonlinear systems under Lyapunov-based economic model predictive control (LEMPC) with data-driven models in the case where it is desirable to have the ability to detect when the data-driven model is or becomes insufficiently accurate for maintaining the closed-loop state in an expected region of state-space. Implications of the results for false sensor measurement cyberattacks seeking to impact the fidelity of models derived from model identification are discussed and illustrated through a chemical process example.
Disciplines
Controls and Control Theory | Process Control and Systems
Recommended Citation
H. Durand, "Anomaly-Handling in Lyapunov-Based Economic Model Predictive Control via Empirical Models," 21st IFAC World Congress, Berlin, Germany, 11–17 July 2020, IFAC-PapersOnline 53(2), 2020, pp. 6911-6916. https://doi.org/10.1016/j.ifacol.2020.12.385.
Comments
Copyright © 2020 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/). Originally published at https://doi.org/10.1016/j.ifacol.2020.12.385.