Nonlinear Model Predictive Control of Robot Manipulators Using Quasi-LPV Representation
Bibliographic record
Abstract
Nonlinear optimization techniques often suffer from time-consuming computational load, which impedes them to be implemented as controller of fast plans, or when a fast action like trajectory tracking is required. In this paper, a Nonlinear Model Predictive Control (NMPC) approach is used to perform the trajectory tracking problem in a robot manipulator in the presence of input saturation and un-modeled dynamics, using the Quasi-Linear Parameter Varying (Quasi-LPV) representation. In this method, instead of the nonlinear state difference equations of the system, a sequence of linearized state equations about a nominal state-control history, over the prediction horizon, is used. By so doing, standard Quadratic Programming (QP) optimization algorithms could be used for the online optimization problem, therefore, speed and efficiency of convergence to the optimal solution would be enhanced. Efficacy of this method is shown by simulation study of a 2-DOF robot manipulator.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".