MétaCan
Menu
Back to cohort
Record W7133049204

Dynamics and vibration-suppression control of flexible-payload manipulator systems

2002· dissertation· W7133049204 on OpenAlexfundno aff
Tong Zhou

Bibliographic record

VenueTSpace · 2002
Typedissertation
Language
FieldEngineering
TopicDynamics and Control of Mechanical Systems
Canadian institutionsnot available
FundersUniversity of Toronto
KeywordsPayload (computing)Input shapingControllabilityControl theory (sociology)VibrationRobotFeed forwardVibration controlControl systemManipulator (device)
DOInot available

Abstract

fetched live from OpenAlex

Recently, there has been a growing interest in manipulating, transporting and positioning flexible payloads (for example, metal sheets) in various industrial applications, such as robotic assembly of automobile sheet-metal body parts. The system tackled in this thesis consists of a rigid robot manipulator and a piece of flexible sheet metal that is grasped at several points by the robot gripper. A major concern related to the performance of such a system is the vibration of the payload and its effect on the robot position. This vibration has to be eliminated before the payload can be precisely positioned for further processing on it. Thus, the main objective of this thesis is to develop an effective robot control strategy for payload vibration suppression that is practical in implementation and robust against model uncertainties. Prior to developing a control scheme, it is useful to obtain an accurate and practical dynamics model of the system. To achieve this, a new method is presented in this thesis for modeling the entire robot-payload system. Using this dynamic model, the vibration controllability issue is examined in order to configure the robot-payload system properly so that all the critical vibration modes of the payload are controllable. Explicit criteria are provided to determine the configurations that result in uncontrollable vibration, and hence these situations can be avoided at the system design stage. A feedforward/feedback control strategy is proposed in this thesis for payload vibration suppression. The feedforward loop consists of an input-preshaping technique and a computed-torque scheme. A new approach is developed for designing an input shaper. It utilizes a structure in which the modal forces of payload vibration are shaped in parallel. This approach relaxes the requirement of dynamic linearity in traditional shaper designs, and for the nonlinear robot-payload system, it improves the shaper's performance in vibration suppression. Given a precise dynamic model, the feedforward control can effectively achieve vibration-free movements of the payload. To enhance the control robustness against model uncertainties, a model-independent feedback loop is proposed, in which a robust vibration-suppression control is developed by using the reaction force of payload vibration at the robot gripper. The feedback control can accomplish further reduction of any residual vibration on the payload due to model uncertainties. This proposed control strategy is practical as it does not require direct measurement of payload vibration, and also has a simple structure with low on-line computational load. Extensive numerical simulations are presented in this thesis to validate the developed dynamic models and to illustrate the effectiveness and robustness of the vibration-suppression control strategy. Rigorous stability and performance analyses of the control strategies are also conducted.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.762
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.244
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2002
Admission routes1
Has abstractyes

Explore more

Same venueTSpaceSame topicDynamics and Control of Mechanical SystemsFrench-language works237,207