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In this paper, an evolutionary-based sparse regression algorithm is proposedand applied onto experimental data collected from a Duffing oscillator setupand numerical simulation data. Our purpose is to identify the Coulomb frictionterms as part of the ordinary differential equation of the system. Correctidentification of this nonlinear system using sparse identification is hugelydependent on selecting the correct form of nonlinearity included in thefunction library. Consequently, in this work, the evolutionary-based sparseidentification is replacing the need for user knowledge when constructing thelibrary in sparse identification. Constructing the library based on thedata-driven evolutionary approach is an effective way to extend the space ofnonlinear functions, allowing for the sparse regression to be applied on anextensive space of functions. ,e results show that the method provides aneffective algorithm for the purpose of unveiling the physical nature of theDuffing oscillator. In addition, the robustness of the identification algorithmis investigated for various levels of noise in simulation. ,e proposed methodhas possible applications to other nonlinear dynamic systems in mechatronics,robotics, and electronics.

Evolutionary-Based Sparse Regression for the Experimental Identification of Duffing Oscillator