Positive position feedback-based methods for active vibration control of carbon fibre structures (aka Killing the vibes)
Bibliographic record
Abstract
Unwanted mechanical vibrations can result in wear, damage and failure, incurring increased maintenance, repair, and replacement costs, and reducing the overall efficiency of systems.In an attempt to mitigate these undesirable vibrations, engineers have developed numerous vibration control techniques, ranging from passive damping methods to active control methods.Designed to tackle vibration problems that passive methods failed to solve, active vibration control (AVC) involves the participation of sensors, actuators, and a control algorithm, powered by an external source.While many control algorithms exist in the AVC field, one of the most popular ones is positive position feedback (PPF), which has been used on both linear and nonlinear systems.Still, despite its wide use, PPF remains an important topic of research, with much to be explored.The purpose of this work is to expand the knowledge of AVC through studies of PPF and PPF-based controllers, by focusing on two key topics: (1) multi-mode control via multi-input multi-output architectures and (2) nonlinear vibration control.These are explored using two crucial tools, experimentation and optimization.This work is further split into four chapters, covering PPFbased controllers with multi-input multi-output (MIMO) architecture for multi-mode control of linear vibrations, nonlinear MIMO PPF controllers for controlling nonlinear vibrations of a hardening structure, multi-mode control of hardening nonlinear vibrations using nonlinear MIMO PPF controllers and finally nonlinear MIMO PPF control of a softening structure's nonlinear vibrations.These studies aim to demonstrate the versatility of PPF as a control algorithm, and the improvements that can be made to it by modifications.In each chapter, a vibration problem was defined, a PPF-based controller algorithm was developed using optimization techniques, and an experimental set-up was constructed, using a sandwich beam as a test structure.The controllers were tested experimentally, and the results were analyzed.Following the four research papers, a discussion chapter is presented, which discusses the observations gleaned from these works.It was determined that PPF controllers are capable of adequately suppressing a wide range of different vibrations, both linear and nonlinear, provided they are correctly tuned.Modifying the traditional PPF however allowed for improved control effects.The importance of experimental tuning and parameter optimization was also highlighted throughout these studies.Overall, these studies illustrated the success of PPF and PPF-based controllers in mitigating multi-mode vibrations both in linear and nonlinear cases, and the importance of using appropriate tuning techniques, based on experimentation and optimization, to achieve the best controller results.have been accepted into his research group at McGill University.I also extend my gratitude to Dr. Giovanni Ferrari, who has been an integral part of my degree, introducing me to the laboratory and teaching me many of the experimental skills I needed to complete my work.I must also acknowledge Prof. Rosaire Mongrain, for stepping into the role of co-supervisor for
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".