Funnel Model Predictive Control for Nonlinear Systems with Relative Degree One
Thomas Berger, Dario Dennstädt, Achim Ilchmann, Karl Worthmann
Abstract
.We show that Funnel MPC, a novel model predictive control (MPC) scheme, allows tracking of smooth reference signals with prescribed performance for nonlinear multi-input multioutput systems of relative degree one with stable internal dynamics. The optimal control problem solved in each iteration of funnel MPC resembles the basic idea of penalty methods used in optimization. To this end, we present a new stage cost design to mimic the high-gain idea of (adaptive) funnel control. We rigorously show initial and recursive feasibility of funnel MPC without imposing terminal conditions or other requirements like a sufficiently long prediction horizon.Keywordsmodel predictive controlfunnel controlreference trackingnonlinear systemsinitial feasibilityrecursive feasibilityMSC codes34H0549J3093B4593C10