Causality-Informed Data-Driven Predictive Control

As a useful and efficient alternative to generic model-based control scheme, data-driven predictive control (DDPC) is subject to bias–variance tradeoff and is known to not perform desirably in face of uncertainty. Through the connection between direct data-driven control and subspace predictive control (SPC), we gain insight into the reason being the lack of causality as a main cause for their high variance of implicit prediction. In this brief, we derive a new causality-informed formulation of DDPC and its regularized form that balances between control cost minimization and implicit identification of a causal multistep predictor. Since the proposed causality-informed formulations only call for block-triangularization of a submatrix in the generic noncausal DDPC based on LQ factorization, our causality-informed formulation of DDPC enjoys computational efficiency. Its efficacy is investigated through numerical examples and application to model-free control of a simulated industrial heating furnace. Empirical results corroborate that the proposed method yields obvious performance improvement over existing formulations in handling stochastic noise and process nonlinearity.