.png){kind=icon}
Return
Koopman operator-based multi-model for predictive control
.png){kind=icon}
OA
AI
DOI：10.1007/s11071-024-09615-7.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This work describes a new model structure developed for prediction in Model Predictive Control (MPC). The model has a multi-model structure in which independent sub-models are employed for the consecutive sampling instants. The model lifts process states into a high-dimensional space in which a linear process description is applied. Depending on the influence of the manipulated variables on lifted states, three general model versions are described and model identification algorithms are derived. As a result of the multi-model structure, model parameters are found analytically from computationally uncomplicated least squares problems using the Extended Dynamic Mode Decomposition algorithm, but the evolution of states over the horizon used in MPC is taken into account. Next, the MPC algorithm for the described model is derived. It requires solving online simple quadratic optimisation tasks. The effectiveness of three considered model configurations and three versions of the lifting functions is examined for a nonlinear DC motor benchmark. Their impact on model accuracy, complexity, possible control accuracy and MPC calculation time is thoroughly discussed. Finally, a more complex polymerisation reactor process is considered to showcase the practical applicability of the presented approach to modelling and MPC.
Keywords:
Model predictive control
Multi-models
Extended dynamic mode decomposition
Koopman operator
Journal
IF:
6
Papers:
1.4W
Citations:
4.1W
Organization
Cited Papers
Nonlinear predictive control of a polymerization reactor based on piecewise linear Wiener model
PREAI
ShareSaveLinear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control.png)
AUTOMATICA
OAAI
AUTOMATICAIF5.9
OAAIShareSaveExpectation-maximization algorithm for bilinear systems by using the Rauch-Tung-Striebel smootherAUTOMATICA
AUTOMATICAIF5.9
PREAI
ShareSaveShareSave
View more