Modeling is one of the most important and difficult steps in control systems design, because a reliable model of the plant is necessary to apply sophisticated control theory. System identification, which builds a model from experimental data, is known as a powerful modeling tool. However it involves a trial and error procedure. This causes a serious problem when one applies system identification theory to industrial plants.
In this talk, important points for system identification users are discussed by showing examples from automobile industry. The first example is modeling of an acoustic field for feedback active noise control systems design. The second example is modeling for an automated steering control system from data measured by a CCD camera and a yaw rate sensor.
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