Indirect data-driven predictive control and the state-space predictor

We define trajectory predictive control (TPC) as a class of indirect data-driven predictive control (DDPC) methods that represent future outputs as linear in past inputs/outputs and future inputs.

TPC unifies many DDPC variants with different predictor structures.

We introduce a predictor with a state-space representation and show that with it, TPC inherits the mature theory of linear model predictive control.

In numerical experiments, the state-space predictor outperforms existing predictors, especially for small training datasets.