  In this paper, an evolutionary-based sparse regression algorithm is proposed
and applied onto experimental data collected from a Duffing oscillator setup
and numerical simulation data. Our purpose is to identify the Coulomb friction
terms as part of the ordinary differential equation of the system. Correct
identification of this nonlinear system using sparse identification is hugely
dependent on selecting the correct form of nonlinearity included in the
function library. Consequently, in this work, the evolutionary-based sparse
identification is replacing the need for user knowledge when constructing the
library in sparse identification. Constructing the library based on the
data-driven evolutionary approach is an effective way to extend the space of
nonlinear functions, allowing for the sparse regression to be applied on an
extensive space of functions. ,e results show that the method provides an
effective algorithm for the purpose of unveiling the physical nature of the
Duffing oscillator. In addition, the robustness of the identification algorithm
is investigated for various levels of noise in simulation. ,e proposed method
has possible applications to other nonlinear dynamic systems in mechatronics,
robotics, and electronics.
