Current estimation and path following for an autonomous underwater vehicle by using a model-based nonlinear observer
Notice bibliographique
Résumé
The objective of this thesis is to contribute to Autonomous Underwater Vehicle (AUV) navigation by estimating the prevalent ocean currents and improving the path following guidance system. The Inertial Navigation System (INS) based localisation solution is vulnerable to uncertainties derived from double integration of the inherent errors within the INS acceleration measurements. This can be aided by the velocity measurement using the Doppler Velocity Log (DVL), but DVL aid is unavailable when the distance between the AUV to the seabed is larger than the DVL range. However, the vehicle's velocity can be estimated by a model-aided observer which predicts a dynamic motion response of the vehicle. The benefits from using a more precise AUV model is compelling as the application of underwater vehicles expand into more complicated and harsher environments. In terms of the AUV motion model, however, the effect of current on the vehicle dynamics is often ignored when the AUV motion model is used for control, navigation and estimation. As a solution for this, an AUV dynamic model-based, nonlinear observer design is introduced, which is complemented with the development of an AUV dynamic model in nonuniform and unsteady current and the high-gain observer (HGO). The gain for the HGO is obtained by solving a Linear Matrix Inequality (LMI) representing the estimation error dynamic. The HGO is a robust tool for observer design and well-used in nonlinear feedback control, which has been studied for several decades. Motivated by the design method of the HGO, the current disturbance is considered as the uncertainties of the vehicle dynamic system, and current velocity is estimated in an indirect way. The current velocity is determined by calculating the differences between the vehicle velocities over the ground and the vehicle velocities through the water estimated by the model-based observer. The HGO based on the AUV dynamic model established in this research was validated using experimental data from a set of field manoeuvres using a Gavia class AUV and the performance was compared against other commonly used navigation methods. In terms of the path following problem, an accurate path following guidance system plays an important role for an AUV in oceanic surveys and exploration. The path-following guidance system includes a guidance law, an update law and a proportional and integral controller. This thesis presents a three-dimensional path following guidance logic which ensures the vehicle path converges into the predefined desired path. The desired straight and curved path re represented by using a Serret-Frenet frame which propagates along the curve. The path following guidance system, 'PPNAPG (Pure Proportional Navigation and Pursuit Guidance)' is developed by combining the pure proportional navigation guidance (PPNG) law and pursuit guidance (PG) law, which are widely used in the missile community. The performance of the proposed path-following guidance system, PPNAPG is validated via both simulation and experiment using the MUN (Memorial University of Newfoundland) Explorer AUV. One of the primary benefits of the nonlinear observer based on the AUV dynamic model and the path following guidance system is that it could be employed on any type of AUV plat forms without any elaboration. Furthermore, the proposed techniques require no additional sensors beside the typically available AUV navigation sensors such as global positioning, accelerometers and gyroscopes in the Initial Measurement Unit (IMU). Overall, this thesis suggests that the nonlinear observers based on an AUV dynamic model and the PPNAPG is an effective combination to estimate and compensate for the current and complete a path-following mission. These outcomes and methods enable other researchers and students in the field of AUV control and navigation systems to adapt and extend the methods to other AUV models without using an ADCP to measure the current. The insight gained from this study can also be of assistance to an oceanographic mission by optimising the guidance law to reduce mission completion time.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
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 tête enseignante, 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 ».