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Published May 2013 | public
Journal Article

A robust model predictive control algorithm augmented with a reactive safety mode

Abstract

A reactive safety mode is built into a robust model predictive control algorithm for uncertain nonlinear systems with bounded disturbances. The algorithm enforces state and control constraints and blends two modes: (I) standard, guarantees re-solvability and asymptotic convergence in a robust receding-horizon manner; (II) safety, if activated, guarantees containment within an invariant set about a reference. The reactive safety mode provides robustness to unexpected, but real-time anticipated, state-constraint changes during standard mode operation. The safety-mode control policy is designed offline and can be activated at any arbitrary time. The standard-mode control has feedforward and feedback components: feedforward is from online solution of a finite-horizon optimal control problem; feedback is designed offline to provide robustness to system uncertainty and disturbances and to establish an invariant "state tube" that guarantees standard-mode re-solvability at any time. The algorithm design is shown for a class of systems with incrementally-conic uncertain/nonlinear terms and bounded disturbances.

Additional Information

© 2013 Elsevier Ltd. © 2013 California Institute of Technology. Government sponsorship acknowledged. Received 29 November 2010; Received in revised form 21 July 2012; Accepted 12 November 2012; Available online 16 March 2013. This research was funded by AFOSR MURI grant FA9550-06-1-0303 and JPL internal R&D. The material in this paper was partially presented at the 17th IFAC World Congress, July 6–11, 2008, Seoul, South Korea. This paper was recommended for publication in revised form by Associate Editor Dragan Nesic under the direction of Editor Andrew R. Teel. Publication support was provided by the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration.

Additional details

Created:
August 22, 2023
Modified:
October 24, 2023