The adaptive sliding mode control based on U–K theory for foot trajectory following of hexapod robot
Bibliographic record
Abstract
This paper addresses the control of a hexapod robot’s foot trajectory tracking using an adaptive sliding mode control (SMC) approach based on Udwadia–Kalaba theory. Unlike the traditional control approach, the Udwadia–Kalaba theory allows for the transformation of the hexapod robot foot trajectory tracking control problem into a system servo binding solution problem. This method eliminates the requirement to linearize the nonlinear system. The system may contain uncertainties, such as less-than-ideal initial circumstances and vibration disturbances during operation, which have an impact on the control precision due to mistakes in modeling, measurements, and changes in operational states. To deal with the uncertainty, the adaptive SMC controller was developed. The stability analysis is carried out using the second Lyapunov function method. By modeling the hexapod robot’s legs and running simulations to compare the simulated tracking route to the planned trajectory, the precision and stability of the control approach suggested in this study are finally demonstrated, and by comparing with the simulation results of adaptive robust control strategy, the advantages of RBF neural network adaptive SMC strategy are obtained.
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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 | 0.000 | 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".