State Transformation Combined Adaptive Robust Control for Motor Driven Joint with State Constraints and Input Saturation
Bibliographic record
Abstract
The control problem of the motor driven joint system under the state and input constraints is discussed in this paper. Firstly, a state transform function is introduced to transfer the state-constrained motor driven joint system to a transformed system, which no longer has the state constraints. Secondly, an adaptive robust control (ARC) with the specified performance bounds is proposed for this transformed system, where the ARC algorithm combined with an auxiliary variable are used to ensure the semi-globally uniformly ultimately bounded of all the closed-loop signals, and a time-varying barrier Lyapunov function (BLF) is designed to constrain all the tracking errors within the specified performance bounds. Thirdly, the above results are extended to the motor driven joint system. Namely, the boundedness of the states in the transformed system are converted into the satisfaction of the state constraints in the motor driven joint system, and the fast transient response and high steady-state tracking accuracy can be achieved by designing the appropriate specified performance bounds in the time-varying BLF. Finally, a simulation is carried out, and the results demonstrate the effectiveness of the proposed method.
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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 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".