New Methodology to Design Learning Control for Robots Using Adaptive Sliding Mode Control and Multi-Model Neural Networks
Bibliographic record
Abstract
In this paper, a new methodology is proposed to design a learning control for robots.Since, the advanced robots need to work in an unstructured and dynamic environment such as human environment (e.g.assistive robots).They need to learn how to interact with people and manipulate the different objects and payloads.Additionally, due to the nonlinearity, the uncertainty of the parameters, external disturbances, and time-varying effects such as tear and wear, the accurate analytical models are too complex to derive for control applications.In this paper, a learning control is developed by effectively combining the adaptive continuous sliding mode control (ACSMC) presented in [1] with multi-model neural network techniques (MMNN).The controller consists of an online adaptation mechanism and an online learning mechanism.It is shown that learning capability allows to realize the controller with less or no prior information of robot inverse dynamic model.The robustness, performance and learning capability of the control system is demonstrated and evaluated trough simulation study and experimentally, using a two-degrees of freedom robot.
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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 | 0.001 | 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) | 0.002 | 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".