Robust Precision Motion Control of Dual-Drive Gantry-Type Cartesian Robot With Workspace Constraints
Bibliographic record
Abstract
Automated motion control tasks would typically arise as essential and critical parts of the operations for complex industrial systems when there exists multiple necessary requirements, such as high precision, low energy consumption, and effective collision avoidance. These motion control tasks invariably also involve workspace constraints to the system. Also as an inevitably encountered challenge, various factors bring model uncertainty and external disturbance to the system, leading to the deviations between the obtained solution and the optimum, particularly for systems with nonlinear characteristics. Considering these issues, this article proposes a robust precision motion control scheme via iterative linear quadratic regulator for planar motion tasks of a flexure-joint gantry-type Cartesian robot. Essentially in the methodology developed here, the model uncertainty and external disturbance of the gantry robot are suppressed through an integral adaptive sliding-mode controller, and a projection scheme is deployed to deal with various constraints from the workspace so that the gantry robot can automatically adjust its trajectories in planar motion tasks. In addition, the desired convergence outcome of the proposed method and also the resulting closed-loop stability of the system are rigorously proved. This approach, thus, renders it possible to achieve improved system performance even when there does not exist a fully accurate system model (which is rather typical in practical situations). Moreover, real-time experiments in two types of motion tasks are designed to validate the effectiveness and applicability of the proposed method.
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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) | 0.000 | 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 | 0.000 |
| 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".