Linear Model Identification

Estimate a linear dynamic model from measurement data using MATLAB System Identification Toolbox. This skill selects the right model type, determines model order, estimates parameters, and validates results — following the methodology a System Identification Toolbox expert would use.

When to Use

• Identify a transfer function, state-space, or process model from I/O data
• Determine model order from measurement data
• Fit a parametric model for simulation, prediction, or control design
• Convert frequency response data (FRD) to a parametric model
• Compare model structures (ARX vs state-space vs transfer function)
• Determine frequency response from time-domain data
• Determine a plant model for PID tuning or control design
• Obtain a data-driven linear model when linearization of a Simulink model is not possible or practical
• Tune parameters of a physics-based model (grey-box) using data
• Compare multiple models to determine which best fits the data
• Simulate or predict system response using the identified model
• Perform subspace identification for high-order systems or MIMO systems, or use Eigenvalue Realization Algorithm (ERA)
• Extract modal parameters (natural frequencies, damping ratios, mode shapes) from frequency response
• Compare model structures (ARX vs state-space vs transfer function)
• Study the possibility of feedback in data by analyzing the correlation between input and output signals
• Study persistence of excitation in the input signals to ensure that the data is informative enough for model identification