Journal
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
Volume 63, Issue 3, Pages 1717-1727Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIE.2015.2504553
Keywords
Adaptive neural network (NN) control; marine surface vessels; neural learning; predefined performances
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Funding
- National Natural Science Foundation of China [61527811, 61473121, 61374119, 61225014]
- China Postdoctoral Science Foundation [2013T60803, 2012M511807]
- Guangdong Natural Science Foundation [2014A030312005]
- Science and Technology New Star of Zhujiang
- Fundamental Research Funds for the Central Universities
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This paper studies neural learning control with predefined tracking error bound for a marine surface vessel whose accurate dynamics could not be obtained a priori. With the introduction of an error transformation function, the constrained tracking control of the original vessel is transformed into the stabilization of an unconstrained system. A filtered tracking error is introduced based on the error transformation, and radial basis function (RBF) neural networks (NNs) are employed to approximate unknown vessel dynamics. Subsequently, stable adaptive NN control is proposed to ensure ultimate boundedness of all the signals in the closed-loop system and to guarantee prescribed tracking performances. Under persistent excitation (PE) condition, the proposed adaptive NN control is shown to be capable of acquiring knowledge on the vessel dynamics and of storing the learned knowledge in memory. The stored knowledge is reused to develop neural learning control such that the improved control performance with faster tracking convergence rate and less computational burden could be achieved, while prescribed transient and steady-state tracking control performances are guaranteed. Simulation studies are performed to demonstrate the effectiveness of the proposed design techniques.
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