System identification refers to the general process of extracting information about a system from measured input-output data. A typical outcome is an input-output model which may be static or dynamic, deterministic or stochastic, linear or nonlinear. One can use such an input-output model for simulation, controller design, or analysis. System identification can extract the physical properties of a system such as its mass, stiffness, and damping distribution. System identification methods can also be applied to obtain information other than a model of a system. For example, it can be used to identify an observer or Kalman filter gain, existing feedback controller gain, disturbance environment, or to detect actuator and sensor failure. The same theory can even be used to synthesize feedback or feedforward controller gains directly from input-output data without having to obtain an intermediate model of the system first. System identification has widespread applications in virtually all areas of engineering including chemical, electrical, mechanical, biomedical, aerospace engineering, and economics.
Faculty contact: Minh Q. Phan