Distributed Control of Multiple Flexible Manipulators With Unknown Disturbances and Dead-Zone Input
Bibliographic record
Abstract
Multiple flexible manipulators can be used to complete some repeatable missions. Each flexible manipulator can be described as an underactuated Lagrangian system based on the assumed modes method. Also, the actuator nonlinearity may deteriorate the system performance. Hence, this article aims to develop a distributed controller to solve the leader–follower consensus of multiple flexible manipulators with uncertain parameters, unknown disturbances, and actuator dead zones. The disturbances are classified as repeatable and nonrepeatable ones. The adaptive, iterative learning, and sliding-mode control techniques are used to handle uncertain parameters, repeatable, and nonrepeatable disturbances, respectively. Based on a dead-zone inverse and a finite-time observer, a distributed controller is developed to drive the flexible manipulators to track a moving leader and keep the flexible vibrations bounded simultaneously. Experimental results are presented to verify the effectiveness of the proposed controller.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".