Dynamic Modeling and Adaptive Robust Synchronous Control of Parallel Robotic Manipulator for Industrial Application
Bibliographic record
Abstract
Control of parallel manipulators is very hard due to their complex dynamic formulations. If part of the complexity is resulting from uncertainties, an effective manner for coping with these problems is adaptive robust control. In this paper, we proposed three types of adaptive robust synchronous controllers to solve the trajectory tracking problem for a redundantly actuated parallel manipulator. The inverse kinematic of the parallel manipulator was firstly developed, and the dynamic formulation was further derived by mean of the principle of virtual work. Furthermore, linear parameterization regression matrix was determined by virtue of command function “equationsToMatrix” in MATLAB. Secondly, the three adaptive robust synchronous controllers (i.e., sliding mode control, high gain control, and high frequency control) are developed, by incorporating the camera sensor technique into adaptive robust synchronous control architecture. The stability of the proposed controllers was proved by utilizing Lyapunov theory. A sequence of simulation tests were implemented to prove the performance of the controllers presented in this paper. The three proposed controllers can theoretically guarantee the errors including trajectory tracking errors, synchronization errors, and cross-coupling errors asymptotically converge to zero for a given trajectory, and the estimated unknown parameters can also approximately converge to their actual values in the presence of unmodeled dynamics and external uncertainties. Moreover, all the simulation comparative results were presented to illustrate that the adaptive robust synchronous high-frequency controller possess a much superior comprehensive performance than two other controllers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".