A robust model predictive control algorithm for input–output LPV systems using parameter extrapolation

Abstract

In this paper, we introduce a novel robust Model Predictive Control (MPC) algorithm for Linear Parameter Varying (LPV) systems represented in the Input–Output (IO) form. The proposed scheme embeds integral action and ensures output reference tracking for piece-wise constant signals. The algorithm is based on the online extrapolation of the LPV scheduling parameters, which are generated recursively based on a simple Taylor expansion argument. Closed-loop asymptotic stability, recursive feasibility of the online optimisation, as well as robustness towards bounded disturbances and scheduling parameter prediction uncertainties are demonstrated. Two distinct nonlinear multi-input multi-output benchmarks are used to illustrate the effectiveness of the proposed method: a numeric simulation example, used to compare the method to state-of-the-art techniques, and a high-fidelity twin rotor system, for which the method is further validated. Real-time capabilities of the proposed scheme are highlighted, since it only requires one Quadratic Program to be evaluated per discrete-time sample, during the implementation.