Robust data-based predictive control of systems with parametric uncertainties: Paving the way for cooperative learning

Eva Masero, José M. Maestre, José R. Salvador, Daniel R. Ramirez, Quanyan Zhu

Research output: Contribution to journalArticlepeer-review

Abstract

This article combines data and tube-based predictive control to deal with systems with bounded parametric uncertainty. This integration generates robustly feasible control sequences that can also be exploited in cooperative scenarios where controllers learn from each other's data. In particular, the approach is based on a database that contains information from previous executions of the same and other controllers handling similar systems. By the combination of feasible histories plus an auxiliary control law that deals with bounded uncertainties, which only needs to be stabilizing for at least one of the system realizations within the uncertainty set, this scheme provides a finite-horizon predictive controller that guarantees exponential stability and robust constraint satisfaction. The validity and benefits of the proposed scheme are shown in case studies with linear and non-linear dynamics.

Original languageEnglish (US)
Article number103109
JournalJournal of Process Control
Volume132
DOIs
StatePublished - Dec 2023

Keywords

  • Cooperative learning
  • Data-driven control
  • Predictive control
  • Robustness
  • Tube-based control

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Computer Science Applications
  • Industrial and Manufacturing Engineering

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