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Enregistrement W4386304134 · doi:10.32920/24058704.v1

Modeling and Control of Aerial Manipulation Systems: From Conventional to Continuum Manipulation

2023· preprint· en· W4386304134 sur OpenAlexafffundabout
Zahra Samadikhoshkho

Notice bibliographique

Revuenon disponible
Typepreprint
Langueen
DomaineComputer Science
ThématiqueRobotic Path Planning Algorithms
Établissements canadiensToronto Metropolitan University
Organismes subventionnairesMinistry of Advanced EducationMinistry of Advanced Education and Skills Development
Mots-clésControl theory (sociology)Nonlinear systemRobustness (evolution)Linear-quadratic regulatorControl engineeringControl systemSliding mode controlSystem dynamicsEngineeringComputer scienceControl (management)PhysicsArtificial intelligence

Résumé

récupéré en direct d'OpenAlex

<p>Aerial manipulation systems (AMSs) are highly coupled nonlinear systems which have attracted significant attention of researchers and industries due to their applications. However, the progress has been slow in part due to the extreme level of nonlinearities which makes their modeling and control quite challenging. </p> <p>In the first phase, to get insight into the dynamics and control of AMSs, conventional AMSs with rigid-link arms were modeled. Next, different control approaches for conventional AMSs control were proposed and the behavior of the system in the presence of different control schemes was compared in terms of accuracy, efficiency, stability and robustness. Four proposed control methods for conventional AMSs included (i) inverse dynamic, (ii) hierarchical linear-quadratic regulator (LQR), (iii) sliding mode, and (iv) semi-optimal nonlinear control techniques. Based on this preliminary study, a controller was selected for its formulation for the next phase of the project. </p> <p>In the next phase, the research focused on modeling and control of aerial continuum manipulation systems (ACMSs) that are distinguished from conventional aerial manipulation systems (AMSs). In ACMS, typical rigid-link arms are replaced with continuum robotic arms to boost their advantages. Using continuum arm extends the capability of AMSs by increasing their compliance and dexterities. Also, AMSs with continuum arms are more compatible to work in cluttered and less structured environments. However, modeling and control of such complex and nonlinear system is much more challenging compared to those of conventional rigid AMSs. </p> <p>The reported research in this thesis is continuation of the ACMS initiative in Robotics, Mechatronics and Automation Laboratory at Ryerson University. In this research, a decoupled model for ACMS is formulated for the first time followed by a decoupled control technique for this system. Cosserat rod theory was adopted for decoupled dynamic modeling of ACMS. Also, a robust adaptive control approach was proposed to cope with the problem of complexity and high level of modeling uncertainties. The stability of the proposed control method was proven using Lyapunov stability theorem. </p> <p>Subsequently, to consider interactions between aerial vehicle and continuum arm, coupled model and control for ACMS were developed. Coupled dynamic modeling for ACMSs was formulated based on Euler-Lagrange theory. For this purpose, a general vertical take-off and landing (VTOL) vehicle equipped with a tendon-driven continuum arm was considered. The modeling approach was complemented with a control technique to demonstrate the validity of the proposed method for such a complex system. Both simulation and experimental results were reported to verify the effectiveness of the proposed modeling technique. </p> <p>Finally, design of the first vision-based adaptive control for ACMSs circumventing the need for a priori knowledge of system dynamic model was proposed. For this purpose, a vision based reduced-order adaptive control scheme was developed. It was shown that using vision feedback in combination with adaptive control method enables effective treatment of nonlinearities, coupling and uncertainties present in typical ACMSs. The method was verified using simulation results. </p>

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Méthodes · Signal consensuel: aucune
Score de désaccord entre enseignants0,831
Score d'incertitude au seuil0,970

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,070
Tête enseignante GPT0,282
Écart entre enseignants0,212 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreMéthodes

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission3
Résumé présentoui

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