Model Predictive Control with Models of Different Granularity and a Non-uniformly Spaced Prediction Horizon

TL;DR

This paper introduces a novel Model Predictive Control scheme that combines detailed and simplified models over different horizon segments with non-uniform spacing, improving long-term prediction accuracy and reducing computational complexity.

Contribution

It proposes a hybrid MPC approach that splits the prediction horizon into segments with different models and time steps, enhancing efficiency and performance.

Findings

The method is recursively feasible.

Simulation shows improved computational efficiency.

Long horizon benefits with simplified models.

Abstract

Horizon length and model accuracy are defining factors when designing a Model Predictive Controller. While long horizons and detailed models have a positive effect on control performance, computational complexity increases. As predictions become less precise over the horizon length, it is worth investigating a combination of different models and varying time step size. Here, we propose a Model Predictive Control scheme that splits the prediction horizon into two segments. A detailed model is used for the short-term prediction horizon and a simplified model with an increased sampling time is employed for the long-term horizon. This approach combines the advantage of a long prediction horizon with a reduction of computational effort due to a simplified model and less decision variables. The presented Model Predictive Control is recursively feasible. A simulation study demonstrates the effectiveness of the proposed method: employing a long prediction horizon with advantages regarding computational complexity.