Integrating autoencoder with Koopman operator to design a linear data‐driven model predictive controller
Bibliographic record
Abstract
Abstract Non‐linear model predictive control (NMPC) is increasingly seen as a promising tool to tackle the problem of handling process nonlinearity and achieve optimal operation. One roadblock to NMPC implementation, however, is the lack of a good model, whether a first‐principles‐based or a non‐linear data‐driven‐based model such as artificial neural networks (ANN). This manuscript proposes a data‐driven modelling approach that integrates an autoencoder‐like network and dynamic mode decomposition (DMD) methods to result in a non‐linear modelling technique where the non‐linearity in the model stems from the modelling of the measured variables. The proposed method results in a semi‐linear state‐space model where the mapping between the model state and outputs are non‐linear (via the autoencoder‐like network) while the model dynamics are linear. In the subsequent model predictive controller (MPC) implementation, the autoencoder translates setpoints and outputs to the states of a state space model. The proposed approach is illustrated using a continuously stirred tank reactor simulation example. For comparison, a linear MPC and non‐linear MPC based on a traditional neural network (NN) model, a classic Koopman operator‐based MPC, and (to benchmark) a perfect model‐based NMPC are implemented and tested on several setpoint tracking tasks. The proposed MPC design outperforms the other data driven MPCs, and has similar performance as the first‐principles‐based NMPC while requiring less computational time and without requiring first principles knowledge.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".