Neural network‐based offset‐free model predictive control for nonlinear systems
Bibliographic record
Abstract
Abstract This paper proposes an offset‐free model predictive control (MPC) framework for nonlinear systems modeled using neural network‐based nonlinear autoregressive models with exogenous inputs (NARX). To address plant‐model mismatch and ensure offset‐free tracking, the NARX model is augmented with an integrating disturbance model, resulting in an extended state‐space suitable for MPC. A nonlinear observer is developed to estimate both system and disturbance states in real time. The impact of training data quality on control performance is examined through two modeling scenarios: one with rich excitation data and another with limited excitation data, reflecting practical constraints. For both cases, offset‐free MPC controllers are designed using the proposed framework. The approach is validated through simulations on a nonlinear chemical reactor and compared with a benchmark NARX‐based offset‐free MPC method employing bias correction from output prediction errors. Results show that the proposed method improves tracking performance, particularly when training data are limited.
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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 | 0.000 |
| 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".