Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 26, Issue 6, Pages 1312-1322Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2015.2401395
Keywords
Adaptive control; asymptotically stability; neural networks (NNs); nonlinear systems; self-organizing
Categories
Funding
- National Science Foundation of China [61034008, 61203099, 61225016]
- Beijing Municipal Natural Science Foundation [4122006]
- Beijing Science and Technology Project [Z141100001414005, Z141101004414058]
- Hong Kong Scholar Program [XJ2013018]
- Beijing Municipal Education Commission Foundation [KZ201410005002, km201410005001]
- Beijing Nova Program [Z131104000413007]
- China Post-Doctoral Science Foundation [2014M550017]
- Ph.D. Program Foundation through the Ministry of Education, China [20121103120020, 20131103110016]
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This paper is concerned with the problem of adaptive neural control for a class of uncertain or ill-defined nonaffine nonlinear systems. Using a self-organizing radial basis function neural network (RBFNN), a direct self-constructing neural controller (DSNC) is designed so that unknown nonlinearities can be approximated and the closed-loop system is stable. The key features of the proposed DSNC design scheme can be summarized as follows. First, different from the existing results in literature, a self-organizing RBFNN with adaptive threshold is constructed online for DSNC to improve the control performance. Second, the control law and adaptive law for the weights of RBFNN are established so that the closed-loop system is stable in the term of Lyapunov stability theory. Third, the tracking error is guaranteed to uniformly asymptotically converge to zero with the aid of an additional robustifying control term. An example is finally given to demonstrate the design procedure and the performance of the proposed method. Simulation results reveal the effectiveness of the proposed method.
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