Real Time Implementation of an Improved Hybrid Fuzzy Sliding Mode Observer Estimator
Bibliographic record
Abstract
This paper extends some of our research results disseminated in the most recent awarded international conference paper concerning the implementation in real time of a sliding mode observer state estimator.For the same case study developed in the conference paper, more precisely a DC servomotor angular speed control system, we extend the proposed concept of sliding mode observer state estimator to a fuzzy sliding mode observer version, more suitable in control applications field such as fault detection of the possible faults that might take place inside the actuators and sensors.The hybrid architecture implemented in a real time MATLAB/SIMULINK simulation environment consists of an integrated control loop structure with a switching bench of two sliding mode observers, one built by using a new approach that improves slightly the proposed sliding mode observer for the conference paper, and second one is an improved intelligent fuzzy version sliding mode observer estimator.The both estimators are implemented in SIMULINK to work independently by using a manual switch.The simulation results for the experimental setup show the effectiveness of the improved fuzzy version of sliding mode observer compared to the standard one, as well as its high accuracy and robustness.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".