Dual MPC for quasi-Linear Parameter Varying systems

TL;DR

This paper introduces a dual MPC framework for quasi-Linear Parameter Varying systems that combines online estimation and control to improve tracking and system identification.

Contribution

It proposes a robust tube-based MPC scheme that simultaneously estimates system parameters and controls, ensuring stability and feasibility.

Findings

Enhanced tracking performance demonstrated in a numerical example.

Active excitation improves parameter estimation accuracy.

Framework guarantees recursive feasibility and stability despite uncertainties.

Abstract

We present a dual Model Predictive Control (MPC) framework for the simultaneous identification and control of quasi-Linear Parameter Varying (qLPV) systems. The framework is composed of an online estimator for the states and parameters of the qLPV system, and a controller that leverages the estimated model to compute inputs with a dual purpose: tracking a reference output while actively exciting the system to enhance parameter estimation. The core of this approach is a robust tube-based MPC scheme that exploits recent developments in polytopic geometry to guarantee recursive feasibility and stability in spite of model uncertainty. The effectiveness of the framework in achieving improved tracking performance while identifying a model of the system is demonstrated through a numerical example.