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$10^{-6}\text{rad} / \mathrm{s}$and$\text{rad} / \mathrm{s}^{2}$.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".