Trajectory Control of an ASEA IRb-6 Manipulator With Singularity Configuration
Bibliographic record
Abstract
In this paper, the trajectory control of an AESA RIb-6 manipulator is addressed. In order to solve the problem of singular inverse kinematics, the normal form approach is employed for computing the joint trajectories from the desired trajectory in the task space. The basic idea of the normal form approach is introduced, and the detailed algorithm is presented and verified. Based on the inverse kinematics results, a group of proportional-integral-derivative (PID) controllers are developed to control the manipulator trajectory. Simulation results are presented which show that the PID controllers are unable to track the desired trajectory if measurement noise exists. In order to overcome the noise problem, an LQG (Linear Quadratic Gaussian) controller is designed for the trajectory control of the manipulator. The simulation results show that the LQG controller exhibits excellent tracking performance and robustness in the presence of measurement noise.
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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".