A Nonlinear Model for Online Identifying a High-Speed Bidirectional DC Motor
Keywords:Coulomb friction, DC motor, dead zone, nonlinear online identification, neural networks, recursive weighted least squares, Wiener model
The modeling system is a process to define the real physical system mathematically, and the input/output data are responsible for configuring the relation between them as a mathematical model. Most of
the actual systems have nonlinear performance, and this nonlinear behavior is the inherent feature for those
systems; Mechatronic systems are not an exception. Transforming the electrical energy to mechanical one or
vice versa has not been done entirely. There are usually losses as heat, or due to reverse mechanical, electrical,
or magnetic energy, takes irregular shapes, and they are concerned as the significant resource of that nonlinear
behavior. The article introduces a nonlinear online Identification of a high-speed bidirectional DC motor with
dead zone and Coulomb friction effect, which represent a primary nonlinear source, as well as viscosity forces.
The Wiener block-oriented nonlinear system with neural networks are implemented to identify the nonlin-
ear dynamic, mechatronic system. Online identification is adopted using the recursive weighted least squares
(RWLS) method, which depends on the current and (to some extent) previous data. The identification fitness
is found for various configurations with different polynomial orders, and the best model fitness is obtained
about 98% according to normalized root mean square criterion for a third order polynomial.
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