The data-driven approach, while promising, faces several challenges and limitations compared to the model-based Stochastic Model Predictive Control (SMPC) method.
Data Requirements: The data-driven method relies heavily on the availability of high-quality, rich input-output data to accurately capture the system dynamics. In contrast, model-based SMPC can leverage known system models, which may not require extensive data collection. Insufficient or poor-quality data can lead to inaccurate control policies and degraded performance.
Robustness to Noise: While the data-driven method aims to incorporate noise characteristics, it may still be more sensitive to noise in the data compared to model-based approaches that explicitly account for noise in the system model. The robustness of the data-driven method can be compromised if the noise is not adequately characterized or if the data is heavily corrupted.
Computational Complexity: The data-driven approach may involve complex computations for estimating system parameters and constructing auxiliary models, especially when dealing with large datasets. This can lead to increased computational burden during the online control process, potentially affecting real-time performance.
Equivalence Guarantees: Although the proposed data-driven method establishes equivalence to model-based SMPC under certain conditions, this equivalence may not hold in all scenarios, particularly in the presence of model uncertainties or when the data does not sufficiently represent the underlying system dynamics.
Generalization: The data-driven method may struggle to generalize to unseen operating conditions or system configurations, as it is heavily reliant on the specific data used for training. In contrast, model-based SMPC can adapt more readily to changes in system dynamics due to its reliance on a parametric model.