The prediction model is the most important part of an MPC strategy. The accuracy of such a
model influences the quality of predictions and control performance of the algorithm. In some …

This paper considers the extension of data-enabled predictive control (DeePC) to nonlinear
systems via general basis functions. Firstly, we formulate a basis-functions DeePC …

We present the design and in-silico evaluation of a multi-step-ahead-loop insulin delivery
algorithm to treat type 1 diabetes (T1D) consisting in a data-driven multi-step-ahead blood …

Indirect data-driven predictive control (DPC) algorithms for nonlinear systems typically
employ multi-step predictors, which are identified from input-output data using neural …

Data-enabled predictive control (DeePC) for linear systems utilizes data matrices of
recorded trajectories to directly predict new system trajectories, which is very appealing for …

In this paper, we present a direct data-driven approach to synthesize model reference
controllers for constrained nonlinear dynamical systems. To this aim, we employ a …

In this paper, we consider the design of data-driven predictive controllers for nonlinear
systems from input-output data via linear-in-control input Koopman lifted models. Instead of …

Since the traditional Model Predictive Control (MPC), which is widely used for trajectory
tracking of autonomous vehicle, cannot analyze and determine specific parameters of the …

This work describes a nonlinear affine model developed especially for prediction in Model
Predictive Control (MPC). A multi-model structure is used in which independent sub-models …

Indirect data-driven nonlinear model predictive control (DD-NMPC) algorithms typically
employ multi-step predictors, which are identified from input-output data using neural …