Output Feedback Model Predictive Control of Interval Type-2 T–S Fuzzy System With Bounded Disturbance
Bibliographic record
Abstract
In this paper, the problem of output feedback model predictive control (MPC) for interval Type-2 Takagi-Sugeno fuzzy systems with bounded disturbance is investigated. The output feedback MPC approach includes an offline design of the state observer to estimate true states and predict bounds of future estimation error sets, and an online problem that optimizes the controller gains to stabilize the closed-loop observer system. The dynamics of the estimation error system is determined by the offline designed observer gain, and bounds of which are online refreshed by scaling a minimal robust positively invariant (RPI) set via a scalar. The optimized controller gains steer the current estimated state from an RPI set into another one such that future estimated states are invariant in the subsequent RPI set. Convergence of the estimation error system and stability condition on the closed-loop observer system in terms of linear matrix inequalities are derived using the technique of S-procedure. The estimation error and estimated state converge within the corresponding time-varying RPI sets, and therefore, recursive feasibility of the optimization problem and input-to-state stability of the closed-loop observer system with respect to the estimation error and bounded disturbance are ensured. For reducing the online computational burden, a lookup table that stores the offline calculated controller gains with corresponding regions of attraction is offline constructed for online searching real-time controller gains. A simulation example is given to show the effectiveness of the approach.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".