Navigation, guidance and control of the Hammerhead autonomous underwater vehicle

Loebis, D.; Naeem, Wasif; Sutton, Robert I.; Chudley, J.; Tiano, A.

doi:10.1049/pbce069e_ch7

Cited by 3 publications

(3 citation statements)

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“…We performed multiple variable screening on the above nine new indicators and the longitude, latitude, and gravity anomaly values for machine learning classification prediction [6,7] . We finalize to divide the data into three parts: training set, validation set and test set, with 80%, 10% and 10% of data samples, respectively.…”

Section: Adaptation Zone Classification Prediction Modelmentioning

confidence: 99%

Data-driven classification prediction of underwater navigation adaptation area

Song,

Yan,

Zhou

et al. 2024

Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024)

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This paper investigates the problem of classifying and predicting fitness zones for underwater gravity-aided navigation. Firstly, a refined baseline map is generated by interpolating and filling in missing data. Based on the methods of repetitive simulation and machine learning, we compare the machine learning KNN, SVM, PSO-BP neural network with the integrated learning XGBoost, RF, MARS and other models, and finally determine to establish the classification prediction system of SVM. Secondly, the idea of area micronutrient is applied to transform the points into rectangular area micronutrients. Through K-means clustering calculation, the calibration of the fitness area is completed, including excellent, good, average, and poor fitness area. A starting point is established based on the area microelement, and this point is geometrically processed with its 8 neighboring orientation points to produce 9 new regional characteristic attribute indicators. The most representative and critical 12 regional characteristic attribute indicators are finally determined. The AUCs of SVM, RF, and XGBoost with hyperparameter optimization of the data were 0.97, 0.90, and 0.91, and then, considering the simplicity and robustness of the processing problem, the underwater fitness zone classification prediction system based on SVM was finally established, and the prediction of the model migratory was carried out.

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Section: Adaptation Zone Classification Prediction Modelmentioning

confidence: 99%

Data-driven classification prediction of underwater navigation adaptation area

Song,

Yan,

Zhou

et al. 2024

Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024)

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“…This has been demonstrated earlier in [12] where a nonlinear neural network based model of an AUV is integrated in the MPC control strategy. More details of the algorithm can be found in [14] and the block diagram of the controller is depicted in Fig. 5.…”

Section: B) Ga-mpcmentioning

confidence: 99%

An intelligent integrated navigation and control solution for an unmanned surface craft

Naeem

Sutton

2009

IET Irish Signals and Systems Conference (ISSC 2009)

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-An adaptive navigation and control algorithm is presented in this paper based on fuzzy logic and optimal control techniques and applied on an unmanned surface vehicle platform. The navigation system consists of an extended Kalman filter with time-varying parameters. Whilst the autopilots include a fuzzy logic based linear quadratic Gaussian controller and a model predictive controller optimized using a genetic algorithm. Both the controllers use the output of the adaptive navigation system as their feedback and therefore creates an integrated system. A multiple waypoint following scenario is considered and tested in real time. Experimental results are shown that demonstrate the efficacy of the proposed system.

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“…The feedback for the controller was taken from the IMU as the IMU data are observed to G(q)= −0.042 26q−1+0.003 435q−2 1−1.765q−1+0.7652q−2 (5) be more accurate and reliable compared with the TCM2 data. For future trials, an MSDF algorithm [43] will provide an estimate of the true heading of the vehicle by where q−1 is the delay operator. This model has a pole very close to the unit circle which represents an combining the IMU and TCM2 outputs.…”

Section: Experiments Designmentioning

confidence: 99%

A genetic algorithm-based model predictive control autopilot design and its implementation in an autonomous underwater vehicle

Naeem

Sutton

Chudley

et al. 2004

Proceedings of the Institution of Mechanical Engineers, Part M:

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The control of any underwater vehicle has always been a challenging task and is certainly an important and necessary feature of an autonomous underwater vehicle (AUV ). This paper describes the implementation of a genetic algorithm (GA)-based model predictive controller for an AUV named Hammerhead which is being developed jointly by the Universities of Plymouth and Cranfield. To the present authors' knowledge, this is the first successful application of a GA in realtime optimization for controller tuning in the marine sector and thus the paper makes an extremely novel and useful contribution to control system design. The advantages of using model predictive control (MPC ) include its constraint handling and disturbance rejection properties commonly present in an underwater environment. The use of GAs generalizes MPC to employ linear as well as nonlinear process models. Furthermore, it supports the inclusion of various types of objective function without having to change the controller structure. The model required for MPC is extracted using system identification techniques on actual AUV data obtained from full-scale in-water tests. A description of Hammerhead AUV is outlined and simulation and experiment data are shown which were obtained by optimizing the cost function online using the GA. Results demonstrate good tracking behaviour despite the presence of disturbances and ever-present modelling uncertainty.

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Navigation, guidance and control of the Hammerhead autonomous underwater vehicle

Cited by 3 publications

References 0 publications

Data-driven classification prediction of underwater navigation adaptation area

Data-driven classification prediction of underwater navigation adaptation area

An intelligent integrated navigation and control solution for an unmanned surface craft

A genetic algorithm-based model predictive control autopilot design and its implementation in an autonomous underwater vehicle

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