Notice bibliographique
Résumé
This chapter has presented some methods of advanced nonlinear control for robot systems. However, to cover all issues related to nonlinear control in detail will demand more than a chapter. The study has focused on model based control where a model dynamic of the process is needed to carry out the control law. Two nonlinear control approaches have been detailed in this work. A feedback linearization control based on input-output linearization has been developed using differential-geometric methods for nonlinear systems. Then, a model based predictive control has been discussed for a nonlinear control design to robot manipulators. The predictive control law minimizes a cost function for the control trajectory over a future time horizon. The control solution has been analytically derived, with no need of an online optimization, which enables fast realtime implementation. Because of the uncertainties present in the system, a robustness strategy has been studied to enhance the tracking response of the system. Two methods have been investigated to deal with system uncertainties. One method is based on the theory of guaranteed stability of uncertain systems, which results to an observer taking information from the system tracking errors. The other one is an observer derived from the nonlinear model control law. It contains an integral action on system tracking errors. This type of control strategy is robust with respect to modeling errors, very effective in disturbance rejection, and gives no steady error caused by either parameters uncertainties or external disturbances. The development of these control strategies is related to the dynamic model of the process. In case of missing information about the system states, a version of control law based on state has been carried out with the quantities, angular positions and velocities, issued from a nonlinear state estimator. It has been shown that the tracking performance is achieved successfully when the uncertainties are well compensated. The issue of global stability of the closed loop system has been proved analytically via Lyapunov stability theory. The nonlinear control laws developed in this chapter are based on a dynamic model of the process. However, it is well known that mathematical representation of a dynamic model does not refer accurately to the reality. This is why it is very important to add to the control strategy a robustness analysis in order to compensate the uncertainties present in the dynamic model. As an alternative of this approach, intelligent control based on the process behavior can be considered as a solution for tracking motion of robot manipulators. Intelligent control achieves automation via the emulation of behavioral intelligence such as biological intelligence (e.g., the use of neural networks and genetics for control); the use of human’s knowledge to design a smart control methodology (fuzzy control). This research
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».