Open AccessHybrid Adaptive Neural Network AUV controller design with Sliding Mode Robust Term
Author(s) -
Behdad Geranmehr,
Kamran Vafaee
Publication year - 2017
Publication title -
international journal of maritime technology
Language(s) - English
Resource type - Journals
eISSN - 2476-5333
pISSN - 2345-6000
DOI - 10.18869/acadpub.ijmt.7.49
Subject(s) - control theory (sociology) , artificial neural network , controller (irrigation) , sliding mode control , lyapunov function , nonlinear system , computer science , control engineering , adaptive control , trajectory , term (time) , robust control , radial basis function , lyapunov stability , mode (computer interface) , engineering , control system , control (management) , artificial intelligence , physics , quantum mechanics , agronomy , biology , operating system , electrical engineering , astronomy
Article History: Received: 26 Oct. 2016 Accepted: 15 Mar. 2017 This work addresses an autonomous underwater vehicle (AUV) for applying nonlinear control which is capable of disturbance rejection via intelligent estimation of uncertainties. Adaptive radial basis function neural network (RBF NN) controller is proposed to approximate unknown nonlinear dynamics. The problem of designing an adaptive RBF NN controller was augmented with sliding mode robust term to improve trajectory tracking and regulation in presence of uncertainties. Moreover, stability proof of proposed control scheme was shown with Lyapunov theory. Furthermore, the control, design and simulation results are provided without any simplification of the entire system. Although the design approach of this paper is implemented on REMUS this point of view can be applied on any AUV using the same technique.
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