In this paper, we propose a neural network (NN)-based tracking control method for underactuated autonomous underwater vehicles (AUVs) with model uncertainties. In order to solve the difficulties in designing the controller for underactuated AUVs, the additional virtual control input is developed, and the approach angle, which generates the desired yaw angle to track any reference trajectory, is introduced. Moreover, the NNs are used to deal with model uncertainties in the hydrodynamic damping terms of AUVs. Finally, the proposed controller is designed based on the dynamic surface control (DSC) method, and the boundedness of all tracking errors is proved by using the Lyapunov stability theory. Some simulation results demonstrate the performance of the proposed control method.
Neural Network-Based Tracking Control of Underactuated Autonomous Underwater Vehicles With Model Uncertainties
Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received January 13, 2014; final manuscript received June 24, 2014; published online September 10, 2014. Assoc. Editor: Hashem Ashrafiuon.
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Seok Park, B. (September 10, 2014). "Neural Network-Based Tracking Control of Underactuated Autonomous Underwater Vehicles With Model Uncertainties." ASME. J. Dyn. Sys., Meas., Control. February 2015; 137(2): 021004. https://doi.org/10.1115/1.4027919
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