Learning-based Funnel-MPC for output-constrained nonlinear systems

2019/12/04 by Thomas Berger, Berger, Thomas, Carolin Kästner +3 · 1 citation
Mathematics · #FOS: Mathematics #Optimization and Control (math.OC) #math.OC

paper · pdf · doi:10.48550/arxiv.1912.01843

arxiv created 2019/12/04 · arxiv updated 2019/12/05

Abstract

We exploit an adaptive control technique, namely funnel control, in order to establish both initial and recursive feasibility in Model Predictive Control (MPC) for output-constrained nonlinear systems. Moreover, we show that the resulting feedback controller outperforms the funnel controller both w.r.t. the required sampling rate for a zero-order-hold implementation and required control action. We further propose a combination of funnel control and MPC, exploiting the performance guarantees of the model-free funnel controller during a learning phase and the advantages of the model-based MPC scheme thereafter.

Cited by

• On Model Predictive Funnel Control with Equilibrium Endpoint Constraints

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