Dynamic Control of 6-DOF AUVs and Fault Detection/Tolerance Strategies

Antonelli, Gianluca

doi:10.1007/978-3-319-77899-0_3

Cited by 4 publications

(3 citation statements)

References 78 publications

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“…Accurately identified plant parameters can be utilized model‐based state‐estimation (navigation) systems that utilize the full UV plant model in their solution 4,5 LS and AID both provide an approach for identifying plant parameters for underactuated vehicles, which generally cannot be controlled by 6‐DOF trajectory tracking controllers that require control authority (actuation) in all 6‐DOF. Online approaches for identifying plant parameters may lead to new methods for online fault‐detection and fault‐tolerant control of UVs 6 The AID approaches generally do not require instrumentation of vehicle linear or angular acceleration, in contrast to LS approaches that generally either require these signals (which are impractical to instrument directly) or estimate them online 7 …”

Section: Introductionmentioning

confidence: 99%

Stable adaptive identification of fully‐coupled second‐order 6 degree‐of‐freedom nonlinear plant models for underwater vehicles: Theory and experimental evaluation

McFarland

Whitcomb

2021

Adaptive Control & Signal

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This article reports the development, stability analysis, and experimental evaluation of a novel adaptive identification (AID) algorithm for underwater vehicles (UVs) for on‐line estimation of plant parameters (hydrodynamic mass, quadratic drag, righting moment, and buoyancy parameters) that enter linearly into 6 degree‐of‐freedom (6‐DOF) second‐order rigid‐body UV plant dynamic models. The reported UV AID method does not require instrumentation of vehicle acceleration as is required of other standard plant parameter identification methods such as conventional least squares. All but one previously reported adaptive methods for second‐order nonlinear plants have addressed the problem of model‐based adaptive tracking control—approaches in which adaptive plant model identification is performed simultaneously with model‐based trajectory‐tracking control of fully‐actuated second‐order plants; however, these approaches are not applicable when the plant is either uncontrolled, under open‐loop control, underactuated, or using any control law other than an algorithm‐specific adaptive tracking controller. The UV AID algorithm reported herein does not require simultaneous reference trajectory‐tracking control, nor does it require instrumentation of linear acceleration or angular acceleration; thus this novel approach complements previously reported adaptive tracking methods and is applicable to a broader class of UV applications for which fully‐actuated tracking control is impractical or infeasible. We report a experimental performance analysis of the UV AID algorithm in comparison to conventional least‐square identification methods, including comparison in cross‐validation where the performance of the experimentally identified plant models obtained in identification trials are compared to experimental trials differing from the identification trials.

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Section: Introductionmentioning

confidence: 99%

Stable adaptive identification of fully‐coupled second‐order 6 degree‐of‐freedom nonlinear plant models for underwater vehicles: Theory and experimental evaluation

McFarland

Whitcomb

2021

Adaptive Control & Signal

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“…17 Moreover, there still exists a very significant modeling task to get a fault detection, identification and recovery (FDIR) system to work on a vehicle. 18 In the literature for nonlinear AUV systems, FDIR systems have been reported to be conducted using both data-driven and model-driven methods. 19,20 In this study, a new method for detecting and managing faults in actuators and sensors in cylinder-shaped AUVs is developed.…”

Section: Introductionmentioning

confidence: 99%

A model-based technique for fault identification of sensors in autonomous systems using adaptive neural networks and extended Kalman filtering

Fekrmandi

Colvin

Sargolzaei

et al. 2023

Health Monitoring of Structural and Biological Systems XVII

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In this study, an adaptive scheme for autonomous underwater vehicle systems is developed that utilizes a model of the complex nonlinear dynamics and control of the vehicle to enable detection of sensor faults and failures. Our framework for design of fault identification and risk management, incorporates a neural network-based nonlinear observer to monitor the input and output of the control system for detection of a variety of faults in the sensors.The training occurs online and parameters of the recurrent neural network are updated by an Extended Kalman Filter. The fault detection and Identification system was developed and integrated for a nonlinear model of a Remus-100 underwater vehicle. The results obtained from the numerical simulation shows the system's ability for prompt detection and isolation of a variety of sensor faults. Further study is needed for development of experimental validation and verification and computational efficiency of the proposed algorithm.

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“…Real-time FD should be an early action to expose system faults [ 3 ], which probably provide information of fault severity and location for FTC to ensure the system stability [ 4 , 5 ]. Research efforts on AUV FD have been carried out for several decades facing the faults of actuators, sensors, and hardware/software, where the methods are typically assorted into categories of qualitative and quantitative analyses [ 6 ]. Various methods could be adopted to diagnose system faults with or without models qualitatively [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Active Fault Localization of Actuators on Torpedo-Shaped Autonomous Underwater Vehicles

Liu

Yan

Luo

et al. 2021

Sensors

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To ensure the mission implementation of Autonomous Underwater Vehicles (AUVs), faults occurring on actuators should be detected and located promptly; therefore, reliable control strategies and inputs can be effectively provided. In this paper, faults occurring on the propulsion and attitude control systems of a torpedo-shaped AUV are analyzed and located while fault features may induce confusions for conventional fault localization (FL). Selective features of defined fault parameters are assorted as necessary conditions against different faulty actuators and synthesized in a fault tree subsequently to state the sufficiency towards possible abnormal parts. By matching fault features with those of estimated fault parameters, suspected faulty sections are located. Thereafter, active FL strategies that analyze the related fault parameters after executing purposive actuator control are proposed to provide precise fault location. Moreover, the generality of the proposed methods is analyzed to support extensive implementations. Simulations based on finite element analysis against a torpedo-shaped AUV with actuator faults are carried out to illustrate the effectiveness of the proposed methods.

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Dynamic Control of 6-DOF AUVs and Fault Detection/Tolerance Strategies

Cited by 4 publications

References 78 publications

Stable adaptive identification of fully‐coupled second‐order 6 degree‐of‐freedom nonlinear plant models for underwater vehicles: Theory and experimental evaluation

Stable adaptive identification of fully‐coupled second‐order 6 degree‐of‐freedom nonlinear plant models for underwater vehicles: Theory and experimental evaluation

A model-based technique for fault identification of sensors in autonomous systems using adaptive neural networks and extended Kalman filtering

Active Fault Localization of Actuators on Torpedo-Shaped Autonomous Underwater Vehicles

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