Computationally Aware Switching Criteria for Hybrid Model Predictive Control of Cyber-Physical Systems

Kun Zhang, Jonathan Sprinkle, Ricardo G. Sanfelice

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

This paper describes hybrid model predictive controllers that switch between two predictor functions based on the uncontrollable divergence metric. The uncontrollable divergence metric relates the computational capabilities of the model predictive controller, to the error of the system due to model mismatch of the predictor function during computation of the solution. The contribution of this paper is in its treatment of the model predictive controller to permit optimization to take multiple timesteps to occur, but still rely on the uncontrollable divergence metric. The results demonstrate the approach for control of a vertical takeoff-and-landing aerial vehicle.

Original languageEnglish (US)
Article number7426423
Pages (from-to)479-490
Number of pages12
JournalIEEE Transactions on Automation Science and Engineering
Volume13
Issue number2
DOIs
StatePublished - Apr 2016

Keywords

  • Cyber-physical systems (CPS)
  • hybrid control
  • model predictive control (MPC)
  • vehicle control

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Computationally Aware Switching Criteria for Hybrid Model Predictive Control of Cyber-Physical Systems'. Together they form a unique fingerprint.

Cite this