controlidentification

Control identification is the process of building mathematical models of dynamic systems from input-output data to support control design and analysis. It sits at the intersection of system identification and control engineering, producing models that predict behavior and enable the synthesis of controllers such as model predictive control (MPC) or linear-quadratic regulators (LQR).

Typical models used in control identification include linear time-invariant representations such as state-space and transfer-function forms, as well as linear and nonlinear auto-regressive models (ARX/ARMAX/Box-Jenkins) and nonlinear approaches (NLARX, Wiener–Hammerstein, neural networks). The choice of model structure depends on the system, data quality, and the intended control application.

The identification process generally involves experiment design and data collection, selection of a model structure, parameter estimation, and rigorous validation. Identification can be performed in open-loop or closed-loop settings, with special methods to address bias and identifiability in closed-loop data. Common estimation techniques include least squares, maximum likelihood, and prediction error methods, often complemented by subspace identification for large-scale systems. Validation uses residual analysis, goodness-of-fit metrics, cross-validation, and scenario testing to ensure the model captures essential dynamics.

Key challenges include identifiability, measurement noise, time delays, nonlinearities, and actuator limits. Robustness to model error and uncertainty is often addressed through robust control design or adaptive methods, and sometimes via dual or persistent excitation strategies to balance estimation and control.

Applications span process control, aerospace, robotics, energy systems, and automotive engineering. See also system identification, model predictive control, LQR, and nonlinear identification methods.