Sensor-Based Hybrid Translational Observer For Underwater Navigation

Bremnes, Jens Einar; Brodtkorb, Astrid H.; Sørensen, Asgeir J.

doi:10.1016/j.ifacol.2019.12.336

Cited by 3 publications

(4 citation statements)

References 6 publications

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“…On the other hand, velocity feedback becomes beneficial in environments where the aforementioned measurements are not possible (e.g. underwater) [38].…”

Section: Precompensation For Unmodelled Thruster Dynamicsmentioning

confidence: 99%

Robustifying Dynamic Positioning of Crane Vessels for Heavy Lifting Operation

Roy

Godjevac³

et al. 2021

IEEE/CAA J. Autom. Sinica

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Construction crane vessels make use of dynamic positioning (DP) systems during the installation and removal of offshore structures to maintain the vessel's position. Studies have reported cases of instability of DP systems during offshore operation caused by uncertainties, such as mooring forces. DP 'robustification' for heavy lift operations, i.e. handling such uncertainties systematically and with stability guarantees, is a long-standing challenge in DP design. A new DP method, composed by an observer and a controller, is proposed to address this challenge, with stability guarantees in the presence of uncertainties. We test the proposed method on an integrated cranevessel simulation environment, where the integration of several subsystems (winch dynamics, crane forces, thruster dynamics, fuel injection system etc.) allow a realistic validation under a wide set of uncertainties.

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“…On the other hand, velocity feedback becomes beneficial in environments where the aforementioned measurements are not possible (e.g. underwater) [38].…”

Section: Precompensation For Unmodelled Thruster Dynamicsmentioning

confidence: 99%

Robustifying Dynamic Positioning of Crane Vessels for Heavy Lifting Operation

Roy

Godjevac³

et al. 2021

IEEE/CAA J. Autom. Sinica

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“…A sensitivity analysis on the observer parameters was conducted in [5]. Here, open-loop motions were simulated for different values of the number of states N 1 , N 2 and N 3 and the discount factors γ 1 , γ 2 and γ 3 .…”

Section: Sensitivity Analysis On Observer Parametersmentioning

confidence: 99%

“…This paper is a continuation of the authors' previous work in [5]. Here, a method for the design of a sensor-based translational observer using the framework of hybrid dynamical systems [6] applied to UUVs, accounting for noisy, asynchronous and sporadically available sensor measurements was developed.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Observer Concept for Sensor Fusion of Sporadic Measurements for Underwater Navigation

Bremnes

Brodtkorb

Sørensen

2020

Int. J. Control Autom. Syst.

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Accurate underwater navigation systems are required for closed-loop guidance and control of unmanned underwater vehicles (UUV). This paper proposes a sensor-based hybrid translational observer concept for underwater navigation using the hybrid dynamical systems framework, accounting for noisy, asynchronous and sporadic sensor measurements. Sensor measurements from an acoustic positioning system, a Doppler Velocity Log (DVL), an Inertial Measurement Unit (IMU) and a pressure gauge are used in the proposed observer. A method for filtering high-frequency noise is proposed, where the estimated states are obtained by taking a weighted discounted average of a finite number of previous measurements predicted forwards to the current time. The attitude of the vehicle is assumed known, and the acceleration measurements are assumed to be continuously available. Measurements of position, depth and linear velocity are assumed to be asynchronous and sporadically available, that is, they do not arrive at the same time, and their sampling rates are not constant. Uniform global asymptotic stability (UGAS) is established using Lyapunov theory for hybrid systems. Results from simulations are presented in order to demonstrate the performance of the proposed method.

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“…Even with the correction of INS from a lag and information processing by the Kalman filter, errors of the navigation information increase as the model of INS errors in the Kalman filter becomes inadequate for the real process.Considering the prospective applications, scientists have been interested in AUUVs and all the particular constraints in different media have been formulated into mathematical problems. Wu et al[12] generated the optimal paths based on the Particle Swarm Optimization (PSO) algorithm and the Kalman filter to finish an underwater target strike mission; Batista et al [13] proposed a filtering method with applications to estimate the linear motion of underwater vehicles, taking into considertion both environmental disturbances and realistic measurement noise; Jens et al proposed a sensor-based method with hybrid dynamical systems for underwater navigation; further, the observer performance should be evaluated in closed-loop with a feedback controller, an attitude observer, and a guidance scheme [14]. In order to improve the accuracy of navigation definitions of heavy UUVs for long-term autonomous operation, it is advisable to use more accurate nonlinear error models of INS in the algorithmic support.…”

mentioning

confidence: 99%

Correction Algorithm for the Navigation System of an Autonomous Unmanned Underwater Vehicle

Chen

Neusypin

Selezneva

2020

Sensors

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More accurate navigation systems are always required for autonomous unmanned underwater vehicles (AUUV)s under various circumstances. In this paper, a measuring complex of a heavy unmanned underwater vehicle (UUV) was investigated. The measuring complex consists of an inertial navigation platform system, a Doppler lag (DL) and an estimation algorithm. During a relatively long-term voyage of an UUV without surfacing and correction from buoys and stationary stations, errors of the measuring complex will increase over time. The increase in errors is caused by an increase in the deviation angles of the gyro platform relative to the accompanying trihedron of the selected coordinate system. To reduce these angles, correction is used in the structure of the inertial navigation system (INS) using a linear regulator. To increase accuracy, it is proposed to take into account the nonlinear features of INS errors; an adaptive nonlinear Kalman filter and a nonlinear controller were used in the correction scheme. Considering that, a modified nonlinear Kalman filter and a regulator in the measuring complex are proposed to improve the accuracy of the measurement information, and modification of the nonlinear Kalman filter was performed through a genetic algorithm, in which the regulator was developed by the State Dependent Coefficient (SDC) method of the formulated model. Modeling combined with a semi-natural experiment with a real inertial navigation system for the UUV demonstrated the efficiency and effectiveness of the proposed algorithms. Doppler lag, which are combined into a measuring complex (MC). The INS and lag signals are processed together using the Kalman filter [8][9][10][11].When the UUV is working under ice fields in the Arctic, there is no possibility of periodic ascent to the surface of the sea; thus, INS correction from a gyro-stabilized platform (GSP) is not provided. In the case of long-term autonomous navigation with the use of a strapdown INS, errors increase over time due to the instability of sensitive elements. Moreover, when UUVs perform maneuvers to complete tasks with long-term autonomous navigation, even for platform INS, errors will reach large values. This is due to an increase in the deviation angles of the gyro-stabilized platform (GSP) relative to the accompanying coordinate system (SC). Even with the correction of INS from a lag and information processing by the Kalman filter, errors of the navigation information increase as the model of INS errors in the Kalman filter becomes inadequate for the real process.Considering the prospective applications, scientists have been interested in AUUVs and all the particular constraints in different media have been formulated into mathematical problems. Wu et al.[12] generated the optimal paths based on the Particle Swarm Optimization (PSO) algorithm and the Kalman filter to finish an underwater target strike mission; Batista et al. [13] proposed a filtering method with applications to estimate the linear motion of underwater vehicles, taking into considert...

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Sensor-Based Hybrid Translational Observer For Underwater Navigation

Cited by 3 publications

References 6 publications

Robustifying Dynamic Positioning of Crane Vessels for Heavy Lifting Operation

Robustifying Dynamic Positioning of Crane Vessels for Heavy Lifting Operation

Hybrid Observer Concept for Sensor Fusion of Sporadic Measurements for Underwater Navigation

Correction Algorithm for the Navigation System of an Autonomous Unmanned Underwater Vehicle

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