Robust constrained predictive control using comparison model

  • Authors:

  • Hiroaki Fukushima;Robert R. Bitmead

  • Affiliations:

  • Department of Mechanical Engineering and Intelligent Systems, The University of Electro-Communications, Chofu, Tokyo 182-8585, Japan;Department of Mechanical & Aerospace Engineering, University of California San Diego, La Jolla, CA 92093-0411, USA

  • Venue:
  • Automatica (Journal of IFAC)
  • Year:
  • 2005

Quantified Score

Hi-index 22.15

Visualization

Abstract

This paper proposes a quadratic programming (QP) approach to robust model predictive control (MPC) for constrained linear systems having both model uncertainties and bounded disturbances. To this end, we construct an additional comparison model for worst-case analysis based on a robust control Lyapunov function (RCLF) for the unconstrained system (not necessarily an RCLF in the presence of constraints). This comparison model enables us to transform the given robust MPC problem into a nominal one without uncertain terms. Based on a terminal constraint obtained from the comparison model, we derive a condition for initial states under which the ultimate boundedness of the closed loop is guaranteed without violating state and control constraints. Since this terminal condition is described by linear constraints, the control optimization can be reduced to a QP problem.