Abstract

Feedforward control is essential for achieving high accuracy for nonlinear mechatronic systems. The aim of this paper is to develop a kernel-based iterative learning control (ILC) approach that enables the specification of parameters through suitable kernels. The developed kernel-based iterative learning control (KILC) framework employs basis functions to facilitate task flexibility and nonlinear and non-causal feedforward as function of the reference signal. Experimental results on a printer motion system subject to nonlinear friction demonstrate that the developed framework is capable of achieving improved performance for systems with non-minimum phase and higher-order dynamics compared to preexisting feedforward methods.

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