A Dynamic Mode Decomposition Approach to Parameter Identification
For control and robotics practitioners, this method enables parameter identification without explicit model knowledge, but it is incremental as it extends existing DMD techniques to parameter estimation.
The paper presents a data-driven algorithm for simultaneous system identification and parameter estimation in control-affine nonlinear systems, demonstrating accurate recovery of system trajectories and unknown parameters (damping, stiffness, nonlinearity) on a Duffing oscillator example.
This paper presents a data-driven algorithm for simultaneous system identification and parameter estimation in control-affine nonlinear systems. Parameter estimation is achieved by training a data-driven predictive model using state-action measurements and various known values at the parameters of interest. The predictive model is then used in conjunction with state-action data corresponding to unknown values of the parameters to estimate the said unknown value. Numerical experiments on the controlled Duffing oscillator with unknown damping, stiffness, and nonlinearity coefficients demonstrate accurate recovery of both the system trajectories and the unknown parameter values from data collected under open-loop excitation.