Sliding mode adaptive neural network control for hybrid visual servoing of underwater vehicles. (15th September 2017)

• Record Type: Journal Article
• Authors: Gao, Jian; An, Xuman; Proctor, Alison; Bradley, Colin
• Is Part Of: Ocean engineering. Volume 142(2017)
• Page Start: 666
• Page End: 675
• Publication Date: 2017-09-15
• Subjects: Underwater vehicles -- Hybrid visual servoing -- Neural networks -- Adaptive sliding mode control -- Dynamic uncertainties; Ocean engineering -- Periodicals; Ocean engineering; Periodicals; 620.4162
• DOI: 10.1016/j.oceaneng.2017.07.015
• Languages: English
• ISSNs: 0029-8018

Abstract

Abstract: In this paper, a hybrid visual servo (HVS) controller is proposed for underwater vehicles, in which a combination of the vehicle's 3-D Cartesian pose and the 2-D image coordinates of a single feature is exploited. A dynamic inversion-based sliding mode adaptive neural network control (DI-SMANNC) method is developed for tracking the HVS reference trajectory generated from a constant target pose. A single hidden-layer (SHL) feedforward neural network, in conjunction with an adaptive sliding mode controller, is utilized to compensate for dynamic uncertainties. The adaptation laws of neural network weight matrices and control gains are designed to ensure the asymptotical stability of tracking errors and the ultimate uniform boundedness (UUB) of neural network weight matrices. The main advantage of the proposed DI-SMANNC over conventional sliding model neural network controllers lies in the fact that the knowledge of the bounds on system uncertainties and neural approximation errors is not required to be previously known. Simulation results are presented to validate the effectiveness of the developed controller, especially the robustness with respect to dynamic modeling uncertainties and camera calibration errors. Highlights: A novel hybrid visual servo controller is developed for underwater vehicles, which is more practical. The system states are constructed by visual pixel information and measured Euclidean variables. A dynamic inversion-based sliding mode adaptive neural network controller with adaptive control gains is designed. No previous knowledge of the bounds on system uncertainties and neural network weight matrices is required.