Adaptive Tracking Control of a Class of Constrained Euler–Lagrange Systems by Factorization of Dynamic Mass Matrix
Bibliographic record
Abstract
Due to the uncertain parameters and/or the coupled matrices in a majority of Euler-Lagrange (EL) systems among multiple inputs and outputs, the controller designs for the constrained robots with unknown nonlinearities and disturbances are still challenging and difficult. In this paper, a new adaptive motion tracking control method for a class of constrained EL systems is presented. The main feature of the presented control is that high-dimensional vector-based integral Lyapunov function combined with a disturbance observer is presented for a class of EL systems with the nonsymmetric nonlinearity of input of the actuators. As long as the error trajectories deviate from or approach the sliding surface, it allows the disturbance estimation to adjust its value. The errors of tracking will converge to a small zone. Thus, stability of a closed-loop system can be ensured. When the designed parameters of the controller are chosen appropriately, the size of the tracking errors in stable state can be ensured. The applicability of this control method has been verified by the experiments with a planar robotic manipulator.
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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".