On the coupling of Model Predictive Control and Robust Kalman Filtering

TL;DR

This paper introduces a robust MPC framework that integrates a recently developed robust Kalman filter to enhance control performance under uncertainties, tested on a nonlinear servomechanism system.

Contribution

It extends a robust Kalman filter to feedback control, enabling robust MPC without increasing computational complexity.

Findings

Improved control performance under model uncertainties.

Maintains MPC advantages like constraint handling.

Effective on nonlinear servomechanism system.

Abstract

Model Predictive Control (MPC) represents nowadays one of the main methods employed for process control in industry. Its strong suits comprise a simple algorithm based on a straightforward formulation and the flexibility to deal with constraints. On the other hand it can be questioned its robustness regarding model uncertainties and external noises. Thus, a lot of efforts have been spent in the past years into the search of methods to address these shortcomings. In this paper we propose a robust MPC controller which stems from the idea of adding robustness in the prediction phase of the algorithm while leaving the core of MPC untouched. More precisely, we consider a robust Kalman filter that has been recently introduced and we further extend its usability to feedback control systems. Overall the proposed control algorithm allows to maintain all of the advantages of MPC with an additional improvement in performance and without any drawbacks in terms of computational complexity. To test the actual reliability of the algorithm we apply it to control a servomechanism system characterized by nonlinear dynamics.