Energy-aware fault-mitigation architecture for underwater vehicles

Carolis, Valerio De; Maurelli, Francesco; Brown, Keith; Lane, David M.

doi:10.1007/s10514-016-9585-x

Cited by 12 publications

(7 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The problem defined with (11)-(12) can be interpreted as follows: given Ψ, the set of feasible control inputs that minimise Bu − v (weighted by W v ), find the true control input u that minimises u − u p (weighted by W u ). In Equations (11) and (12) u p , W u , and W v are design parameters. The choice of u p may correspond, for example, to minimum control deflections in aerospace applications.…”

Section: Geometric Insight Into Problemmentioning

confidence: 99%

“…The paper proposes the one-norm algorithm and modified singular value method to find appropriate thruster configuration and thrust allocation. Energy-aware fault-mitigation architecture for underwater vehicles has been proposed in [11]. The proposed architecture is capable of detecting faults and monitoring the performance of the thruster subsystem in modern AUVs by observing the use of the onboard resources.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Geometric Insight into the Control Allocation Problem for Open-Frame ROVs and Visualisation of Solution

et al. 2020

View full text Add to dashboard Cite

The overall control system for an open-frame Remotely Operated Vehicle (ROV) is typically built from three subsystems: guidance, navigation and control (GNC). The control allocation plays a vital role in the control subsystem. Typically, open-frame underwater vehicles have p actuators (thrusters) for the motion in the horizontal plane, and the control allocation problem, in this case, is very complex and hard to visualise, because the normalised constrained control subset is a p-dimensional unit cube. The aim of this paper is to give a clear picture and a geometric interpretation of the problem and to introduce a hybrid method, based on the integration of a weighted pseudoinverse and the fixed-point method. The main idea of the hybrid method is visualised, and the deep geometric insight is provided using a “virtual” ROV in low-dimensional control spaces, including visualisation of the attainable command set, solution lines, control energy spheres and the role of pseudoinverse and fixed-point iterations. The same concepts are then extended to higher-dimensional cases, for open-frame ROV with four X-shaped (vectored) horizontal thrusters, which is one of the most common thruster configurations for commercial ROVs. The proposed hybrid method has been developed, integrated into a generic fault-tolerant ROV control system and evaluated in virtual and real-world environments off the west coast of Ireland using observation-class ROV Latis and work-class ROV Étaín.

show abstract

Section: Geometric Insight Into Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Geometric Insight into the Control Allocation Problem for Open-Frame ROVs and Visualisation of Solution

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The sensors deployed in underwater robotics to monitor system states are limited, due to space and power limitations. Commonly the sensors include Doppler velocimeters (DVLs), inertial measurement units (IMUs), digital compasses, barometers, satellite and underwater acoustic positioning devices, current sensors, voltage sensors, pressure sensors, and rotation speed sensors [6][7][8]. Through the use of such sensing modalities, the underwater robot is able to collect important measurements, such as body velocity and heading attitude.…”

Section: Introductionmentioning

confidence: 99%

Online Learning Based Underwater Robotic Thruster Fault Detection

Guo

Zhao

et al. 2021

Applied Sciences

View full text Add to dashboard Cite

This paper presents a novel online learning-based fault detection designed for underwater robotic thruster health monitoring. In the fault detection algorithm, we build a mathematical model between the control variable and the propeller speed by fitting collected online work status data to the model. To improve the accuracy of online modeling, a multi-center PSO algorithm with memory ability is utilized to optimize the modeling parameters. Additionally, a model online update mechanism is designed to accommodate the model to the change of thruster work status and sea environment. During the operation, propeller speed of the underwater robot is predicted through the online learning-based model, and the model residuals are used for thruster health monitoring. To avoid false alarm, an adaptive fault detection strategy is established based on model online update mechanism. The proposed method has been extensively evaluated using different underwater robotics, through a sea trial data simulation, a pool test fault detection experiment and a sea trial fault detection experiment. Compared with fixed model-based method, speed prediction MAE of the online learning model is at least 37.9% lower than that of the fixed model. The online learning-based method show no misdiagnosis in experiments, while the fixed model-based method is misdiagnosed. Experimental results show that the proposed method is competitive in terms of accuracy, adaptability, and robustness.

show abstract

“…The data-driven methods classify normal and faulty situations by identifying data patterns statistically, e.g., a novel data-driven algorithm was developed recently by integrating techniques of fast Fourier transform and uncorrelated multi-linear principal component analysis, which could achieve effective space visualization for FD under actuator and sensor faults [ 13 ]. Some other methods of implementations include recursive neural networks [ 14 ], online Bayesian nonparametric technique [ 15 ], wavelet-based filtering method [ 16 ], and energy-aware architecture [ 17 ]. Within these methods, the acquired data need to represent the fault types related to the investigated objects.…”

Section: Introductionmentioning

confidence: 99%

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

Liu

Yan

Luo

et al. 2021

Sensors

View full text Add to dashboard Cite

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.

show abstract

Energy-aware fault-mitigation architecture for underwater vehicles

Cited by 12 publications

References 34 publications

Geometric Insight into the Control Allocation Problem for Open-Frame ROVs and Visualisation of Solution

Geometric Insight into the Control Allocation Problem for Open-Frame ROVs and Visualisation of Solution

Online Learning Based Underwater Robotic Thruster Fault Detection

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

Contact Info

Product

Resources

About