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Sampled-data iterative learning control for nonlinear systems with arbitrary relative degree
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DOI：10.1016/S0005-1098(00)00141-2.png)
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摘要
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In this paper, a sampled-data iterative learning control method is proposed for nonlinear systems without restriction on system relative degree. The learning algorithm does not require numerical differentiations of any order from the tracking error. A sufficient condition is derived to guarantee the convergence of the system output at each sampling instant to the desired trajectory. Numerical simulation is conducted to demonstrate the theoretical result. (C) 2000 Elsevier Science Ltd. All rights reserved.
Keyword:
learning control
convergence
relative degree
sampled-data
nonlinear systems
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IF:
5.9
论文数:
1.2W
被引数:
5.2W
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