A Contraction-constrained Model Predictive Control for Multi-timescale Nonlinear Processes

TL;DR

This paper introduces a contraction-constrained model predictive control approach tailored for nonlinear processes with multiple timescales, enhancing stability guarantees and computational efficiency.

Contribution

It develops a reference-flexible condition based on contraction theory to ensure stability under non-uniform prediction horizons in multi-timescale control.

Findings

Provides a stability guarantee for nonlinear systems with multi-timescale dynamics.

Demonstrates improved computational efficiency through non-uniform optimization horizons.

Enhances control performance by integrating contraction theory into MPC design.

Abstract

Many chemical processes exhibit diverse timescale dynamics with a strong coupling between timescale sensitive variables. Model predictive control with a non-uniformly spaced optimisation horizon is an effective approach to multi-timescale control and offers opportunities for reduced computational complexity. In such an approach the fast, moderate and slow dynamics can be included in the optimisation problem by implementing smaller time intervals earlier in the prediction horizon and increasingly larger intervals towards the end of the prediction. In this paper, a reference-flexible condition is developed based on the contraction theory to provide a stability guarantee for a nonlinear system under non-uniform prediction horizons.