Model Predictive Control and Moving Horizon Estimation using Statistically Weighted Data-Based Ensemble Models

TL;DR

This paper introduces a novel MPC framework using statistically weighted ensemble models and a new MHE-based state observer, demonstrated on a benchmark energy system with multiple conditions.

Contribution

It proposes a new combination rule for ensemble models based on Mahalanobis distance and a novel MHE-based state observer for improved control and estimation.

Findings

Ensemble weights vary adaptively across the prediction horizon.

The methodology improves control accuracy on a benchmark energy system.

The approach effectively handles multiple operating conditions.

Abstract

This paper presents a model predictive control (MPC) framework leveraging an ensemble of data-based models to optimally control complex systems under multiple operating conditions. A novel combination rule for ensemble models is proposed, based on the statistical Mahalanobis distance, enabling the ensemble weights to suitably vary across the prediction window based on the system input. In addition, a novel state observer for ensemble models is developed using moving horizon estimation (MHE). The effectiveness of the proposed methodology is demonstrated on a benchmark energy system operating under multiple conditions.