  The research on sliding mode control strategy is generally based on the
robust approach. The larger parameter space consideration will inevitably
sacrifice part of the performance. Recently, the data-driven sliding mode
control method attracts much attention and shows excellent benefits in the fact
that data is introduced to compensate the controller. Nevertheless, most of the
research on data-driven sliding mode control relied on identification
techniques, which limits its online applications due to the special
requirements of the data. In this paper, an input-mapping technique is inserted
into the design framework of sliding mode control to compensate for the
influence generated by the unknown dynamic of the system. The major novelty of
the proposed input-mapping sliding mode control strategy lies in that the
sliding mode surface and the sliding mode controller are co-designed through
online learning from historical input-output data to minimize an objective
function. Then, the convergence rate of the system is improved significantly
based on the method designed in this work. Finally, some simulations are
provided to show the effectiveness and superiority of the proposed methods.
