An adaptive iterative learning control approach is proposed for a class of single-input single-output uncertain nonlinear systems with completely unknown control gain. Unlike the ordinary iterative learning controls that require some preconditions on the learning gain to stabilize the dynamic systems, the adaptive iterative learning control achieves the convergence through a learning gain in a Nussbaum-type function for the unknown control gain estimation. This paper shows that all tracking errors along a desired trajectory in a finite time interval can converge into any given precision through repetitive tracking. Simulations are carried out to show the validity of the proposed control method.
Adaptive Iterative Learning Control for Nonlinear Systems With Unknown Control Gain1
Contributed by the Dynamic Systems, Measurement, and Control Division of THE AMERICAN SOCIETY OF MECHANICAL ENGINEERS for publication in the ASME JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received by the ASME Dynamic Systems and Control Division September 22, 2002; final revision, December 15, 2003. Review conducted by: R. Langari.
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Chen , H., and Jiang, P. (March 11, 2005). "Adaptive Iterative Learning Control for Nonlinear Systems With Unknown Control Gain." ASME. J. Dyn. Sys., Meas., Control. December 2004; 126(4): 916–920. https://doi.org/10.1115/1.1850538
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