Abstract
In this paper, by employing Radial Basis Function (RBF) Neural Networks (NN) to approximate uncertain functions, the robust adaptive neural networks design for a class of SISO systems was brought in based on dynamic surface control (DSC) and minimal-learning-parameter (MLP) algorithm. With less learning parameters and reduced computation load, the proposed algorithm can avoid the possible controller singularity problem and the trouble caused by "explosion of complexity" in traditional backstepping methods is removed, so it is convenient to be implemented in applications. In addition, it is proved that all the signals of the closed-loop system are uniformly ultimately bounded(UUB), and simulation results on ocean-going training ship ’YULONG’ are shown to validate the effectiveness and the performance of the proposed algorithm.
This work was supported in part by the National Natural Science Foundation of China (No.51179019), the Natural Science Foundation of Liaoning Province (No. 20102012) and the Program for Liaoning Excellent Talents in University (LNET).
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Li, W., Ning, J., Yu, R. (2013). DSC Approach to Robust Adaptive NN Tracking Control for a Class of SISO Systems. In: Guo, C., Hou, ZG., Zeng, Z. (eds) Advances in Neural Networks – ISNN 2013. ISNN 2013. Lecture Notes in Computer Science, vol 7952. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39068-5_29
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DOI: https://doi.org/10.1007/978-3-642-39068-5_29
Publisher Name: Springer, Berlin, Heidelberg
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