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.

Keywords:
adaptive control, nonlinear control systems, learning systems, uncertain systems, nonlinear dynamical systems, iterative methods, convergence, control system synthesis, Adaptive Iterative Learning Control, Unknown Control Gain, Repetitive Tracking
Topics:
Iterative learning control, Nonlinear systems, Errors, Trajectories (Physics)
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