Adaptive sliding mode control for robotic systems using multiple parameter models
Bibliographic record
Abstract
In the face of large scale parametric uncertainties, the existing single model (SM)-based sliding mode control (SMC) demands high controller gains to achieve good transient tracking performance. The main practical problem of having high-gains based design is that it amplifies the input and output disturbance as well as excites hidden unmodeled dynamics causing high-frequency switching and infinitely fast control action. To deal with the problem associated with high-frequency control chattering phenomenon, we introduce multiple parameters model based Lyapunov switching strategy to reduce the level of parametric uncertainty so as to reduce the control gains. This method extends the SM-based SMC approach by allowing the controllers to be reset among the finite set of the candidate controllers. The key idea is to distribute the compact set of the unknown parameter into a finite number of smaller compact subsets. Then, we design a family of candidate controllers corresponding to each of the smaller compact parameter subsets. We then use a Lyapunovbased switching-logic to identify a controller from a family of the candidate SMC controllers. Finally, implementation results on a 2-DOF robotic system is shown to demonstrate the effectiveness of the proposed method.
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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".