Robust Model Predictive Control Design for Fault-Tolerant Control of Process Systems
Bibliographic record
Abstract
This work considers the problem of stabilization of nonlinear systems subject to constraints, uncertainty and faults in the control actuator. We first design a robust model predictive controller that allows for an explicit characterization of the set of initial conditions starting from where feasibility of the optimization problem and closed-loop stability is guaranteed. The main idea in designing the robust model predictive controller is to employ Lyapunov-based techniques to formulate constraints that (a) explicitly account for uncertainty in the predictive control law, without making the optimization problem computationally intractable, and (b) allow for explicitly characterizing the set of initial conditions starting from where the constraints are guaranteed to be initially and successively feasible. The explicit characterization of the stability region, together with the constraint handling capabilities and optimality properties of the predictive controller, is utilized to achieve fault-tolerant control of nonlinear systems subject to uncertainty, constraints, and faults in the control actuators. The implementation of the proposed method is illustrated via a chemical reactor example.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".