Aquino-Lugo, Angel A.

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Gray box modeling of electric drives using radial basis functions: an experimental case
(2006) Aquino-Lugo, Angel A.; Vélez-Reyes, Miguel; College of Engineering; O’Neill-Carrillo, Efraín; Irrizary-Rivera, Agustín; Department of Electrical and Computer Engineering; Maldonado-Fortunet, Francisco
Modeling of electric drives coupled to complex mechanical loads may be a challenging task. Since the electric drives are commonly used in the industry, for many applications is desirable to have the capability of self-tuning the controller parameters to drive different mechanical load. A possible solution consists of correctly identify the drive and the mechanical load. For that reason, gray-box modeling using neural networks is presented as a possible solution for the identification of the mechanical loads and the drive system. In the proposed gray-box modeling, the drive system is divided into the known part governed by the physical laws, which in our case was the electrical subsystem, and an unknown part, which in our case was the mechanical subsystem. This type of approach is known as a two stage identification process. At each stage, the parameters are estimated using the method of linear and recursive linear least squares. To validate the effectiveness of this approach, simulations and experiments were perform and their results are presented in this work.