Feedback Linearized-Based and Approximated Parallel Distributed Compensation Approach: Theory and Experimental Implementation
Bibliographic record
Abstract
In this paper, the Parallel Distributed Compensation (PDC) method is used to stabilize a cart-mounted inverted pendulum and Overhead Crane model. One of the significant issues of using PDC approach for systems with nonlinear terms is finding a linear sectors. Since equations of motion (EoM) of an inverted pendulum and overhead crane include some complicated nonlinear terms, finding sectors is often impossible. Therefore, the traditional PDC has some difficulties from the practical point of view. In order to overcome such a problem, here, two different approaches have been proposed. In the first approach, prior to utilizing a PDC, complex equations have been simplified using feedback linearization method. PDC method is then applied to the obtained closed loop system. In the second strategy, named as the approximated PDC, after eliminating the small terms in the EoM, PDC method is applied. The results of simulations pertinent to PDC controller and traditional LQR method have been compared. Finally, for verification of the presented approximated approach, practical implementation on the experimental crane setup has been done and results reported and compared with simulation.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".