Model Predictive Control and Moving Horizon Estimation using Statistically Weighted Data-Based Ensemble Models
Laura Boca De Giuli (Politecnico di Milano)
Samuel Mallick (TU Delft - Mechanical Engineering)
Alessio La Bella (Politecnico di Milano)
Azita Dabiri (TU Delft - Mechanical Engineering)
Bart De Schutter (TU Delft - Mechanical Engineering)
Riccardo Scattolini (Politecnico di Milano)
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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.
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