  Mechatronic systems are commonly used in the industry, where fast and
accurate motion performance is always required to guarantee manufacturing
precision and efficiency. Nevertheless, the system model and parameters are
difficult to be obtained accurately. Moreover, the high-order modes, strong
coupling in the multi-axis systems, or unmodeled frictions will bring uncertain
dynamics to the system. To overcome the above-mentioned issues and enhance the
motion performance, this paper introduces a novel intelligent and totally
model-free control method for mechatronic systems with unknown dynamics. In
detail, a 2-degree-of-freedom (DOF) architecture is designed, which organically
merges a generalized super-twisting algorithm with a unique iterative learning
law. The controller solely utilizes the input-output data collected in
iterations such that it works without any knowledge of the system parameters.
The rigorous proof of convergence ability is given and a case study on
flexture-joint dual-drive H-gantry stage is shown to validate the effectiveness
of the proposed method.
