Hierarchical Model Predictive Image-Based Visual Servoing of Underwater Vehicles With Adaptive Neural Network Dynamic Control

Abstract

This paper proposes a hierarchical image-based visual servoing (IBVS) strategy for dynamic positioning of a fully actuated underwater vehicle. In the kinematic loop, the desired velocity is generated by a nonlinear model predictive controller, which optimizes a cost function of the predicted image trajectories under the constraints of visibility and velocity. A velocity reference model, representing the desired closed-loop vehicle dynamics, is integrated with an IBVS kinematic model to predict the future trajectories. In the dynamic velocity tracking loop, a neural-network-based model reference adaptive controller is designed to ensure the convergence of the velocity tracking error in the presence of uncertainties associated with vehicle dynamic parameters, water velocity, and thrust forces. Comparative simulations with different control and system configurations are performed to verify the effectiveness of the proposed scheme and to illustrate the influences of the prediction horizon, cost function, closed-loop vehicle dynamics, and predictive velocity reference model on the IBVS system performance.

Robust time‐varying formation control for multiple underwater vehicles subject to nonlinearities and uncertainties

“…The nominal AUV model of vehicle i can be obtained by removing the equivalent disturbance q i in (5). The real vehicle model (3) of AUV i can be considered the nominal model added by the equivalent disturbances.…”

“…These vehicles are revolutionizing the human abilities to explore and monitor the oceanic environments and have a wide range of potential applications in scientific research, mine countermeasures, oceanography, payload delivery, and reconnaissance of oceanic activities. Therefore, the research and development of unmanned underwater vehicles have attracted considerable attention in the ocean and control engineering communities …”

“…Therefore, the research and development of unmanned underwater vehicles have attracted considerable attention in the ocean and control engineering communities. [1][2][3][4][5] In many large-scale oceanic applications, a given mission may be too complex to be fulfilled by an individual underwater vehicle, and the usage of multiple underwater vehicles working cooperatively thereby becomes an efficient and effective solution to complete the mission. Furthermore, the multiple AUV system is more robust than the single vehicle system because a group of autonomous marine vehicles can provide redundancy when an underwater vehicle malfunctions.…”

Neural Network Predictive Control for Autonomous Underwater Vehicle with Input Delay

“…Based on nonlinear control theory, several control methods have been proposed, such as sliding mode control [4][5][6], adaptive control [7][8][9][10][11], and predictive control [12][13][14][15]. However, a common problem of the above literatures is that the time-delays are not taken into account.…”

Predictor-based LOS guidance law for path following of underactuated marine surface vehicles with sideslip compensation

“…Guidance is of critical importance for marine vessels to accomplish the desired motion scenarios such as target tracking, path following, path tracking, and path maneuvering (Fossen, 2002;Breivik and Fossen, 2009;Chen et al, 2013;Cui et al, 2008;Peng et al, 2013Peng et al, , 2015Peng et al, , 2016Xiang et al, 2015a;Gao et al, 2015). For the path following problem, the control objective is to follow a parameterized path without involving any requirements pertaining to temporal constraints (Chen and Tian, 2015;Xiang et al, 2015b).…”