Vibration Control of Flexible Joint Robots Using a Discrete-Time Two-Stage Controller Based on Time-Varying Input Shaping and Delay Compensation
Bibliographic record
Abstract
Abstract Most industrial serial robots use decentralized joint controllers assuming rigid body dynamics. To prevent exciting the flexible mode, gains are kept low, resulting in poor control bandwidth and disturbance rejection. In this paper, a two-stage flexible joint discrete controller is presented, in which the decentralized approach is extended with a stiffness to take into account the dominant coupling mode. In the first-stage, an input shaping feedforward shapes the rigid closed-loop dynamics into desired dynamics that does not produce link vibrations. Robotic dynamic computation based on a recursive Newton–Euler algorithm is conducted to update the feedforward link inertia parameter during robot motion. A second-stage is added to increase disturbance rejection. A generalized Smith predictor (GSP) is developed to compensate for delay and feedback sensor filtering. An effective methodology is presented to optimize the control loop gains. Numerical simulations and experiments on a six-joint robot manipulator confirm that the proposed controller improves control performances in terms of bandwidth, vibration attenuation, and disturbance rejection.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".