Nonlinear modelling of leader-follower UAV close formation flight with dynamic inversion-based control

Johnson, Y.; Ahamed, T. P. Imthias

doi:10.1504/ijmic.2018.094205

Cited by 3 publications

(1 citation statement)

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“…But such a classifier is not conducive to AUV on-line adjustment of the horizontal course, and is not intelligent enough. Horizontal scale analysis index is more suitable for small AUV tracking targets [13][14][15]. In this paper, the horizontal temperature of shallow water is taken as the important sampling data for analysis, and the horizontal temperature homogeneity index (HTHI) is defined by referring to the concept of VTHI method as a classifier to determine whether AUV reaches the front via warm and cold water masses.…”

Section: Horizontal Temperature Homogeneity Indexmentioning

confidence: 99%

An AUV Adaptive Front-Tracking Algorithm Based on Data-Driven

2019

Proceedings of the 11th International Conference on Modelling, Identification and Control (ICMIC2019)

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For the dynamic observation of oceanic fronts, a data-driven adaptive front-tracking algorithm for autonomous underwater vehicle (AUV) is proposed based on the model prediction of the ambient temperature data obtained by online sampling. Firstly, a dynamic model of front temperature is established by analyzing the temperature characteristics of fronts and water masses on both sides. Secondly, Gauss process regression (GPR) is used to process the real-time AUV observation data and predicts the current location environment model. Finally, an improved gradient search algorithm is used to plan the sampling path. The simulation results show that the proposed method can achieve continuous tracking down the front. By comparing with other front tracking algorithms, the proposed method can effectively track complex fronts, and acquisition of front area data is more efficient.

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Section: Horizontal Temperature Homogeneity Indexmentioning

confidence: 99%