Decoupled MPC with Constrained Optimization for Enhanced Robot Manipulator Trajectory Tracking
Bibliographic record
Abstract
The growing requirements of Industry 4.0 and the healthcare sector are driving the need for ever-greater precision in robotic control. Model Predictive Control with Decoupling (MPC-Decoupling) is emerging as an effective solution to meet this challenge while optimizing system performance. Ensuring precise tracking of position, velocity, and acceleration is crucial for these applications. This paper introduces an innovative MPC-Decoupling approach that significantly reduces errors in these variables while integrating acceleration constraints to further enhance performance. Simulations conducted on the UR5e manipulator using the Pinocchio library reveal notable improvements: a 48 % reduction in position error and a 92 % decrease in velocity error compared to conventional MPCDecoupling position-tracking methods. Moreover, incorporating acceleration constraints further refines accuracy, yielding errors on the order of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$10^{-6}\text{rad} / \mathrm{s}$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{rad} / \mathrm{s}^{2}$</tex>.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".