Hybrid Adaptive Backstepping Sliding Mode Controller of Permanent Magnet Linear Synchronous Motors
Bibliographic record
Abstract
This paper tackles the tracking position control dilemma of permanent magnet linear synchronous motors with parameter uncertainties and load force disturbance. Adaptive nonlinear backstepping control augmented with sliding mode control (SMC) is proposed to solve the problem of load force distribution. The backstepping is a recursive control technique where its stability is ensured at each step. However, its sensitivity to uncertainties, disturbances, and electromagnetic noise leads to unwanted performances. SMC is a well-known nonlinear robust approach for uncertain dynamical systems and reduces its parametric adaptive laws. However, implementing this technique in real-time applications is stopped by its main shortcoming, the undesirable chattering phenomenon. The saturation function is used to reduce the chattering phenomenon. The incorporation of these approaches is a promising solution to provide a suitable position tracking of PMLSM in the presence of parameter uncertainties and load force disturbance. The simulation tests have been performed on the PMLSM system to prove the effectiveness and robustness of the proposed controller law. The results highlighted satisfactory position tracking performance in transient conditions and steady-state and under different load force disturbances.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".