Abstract
Preisach model is a well‐known hysteresis identification method in which the hysteresis is modeled by linear combination of hysteresis operators. Although Preisach model describes the main features of system with hysteresis behavior, due to its rigorous numerical nature, it is not convenient to use in real‐time control applications. Here a novel neural network approach based on the Preisach model is addressed, provides accurate hysteresis nonlinearity modeling in comparison with the classical Preisach model and can be used for many applications such as hysteresis nonlinearity control and identification in SMA and Piezo actuators and performance evaluation in some physical systems such as magnetic materials. To evaluate the proposed approach, an experimental apparatus consisting one‐dimensional flexible aluminum beam actuated with an SMA wire is used. It is shown that the proposed ANN‐based Preisach model can identify hysteresis nonlinearity more accurately than the classical one. It also has powerful ability to precisely predict the higher‐order hysteresis minor loops behavior even though only the first‐order reversal data are in use. It is also shown that to get the same precise results in the classical Preisach model, many more data should be used, and this directly increases the experimental cost.
Highlights
Today hysteresis modeling is one of the most interesting and challenging field of study in many engineering applications such as shape memory alloy SMA, piezoelectric, piezocermaic, magnetostrictive, and electromechanical actuators
For evaluation of the proposed ANN-based Preisach hysteresis model, numerical classical Preisach model, and Shirley approach, a one-dimensional flexible aluminum beam whose deflection is controlled by an SMA wire as an actuator is used
To identify hysteresis system by using Shirley approach, density function should be approximated too. We described how this method can realize Preisach model by density function approximation based on finding best fit solution of a linear equation system
Summary
Today hysteresis modeling is one of the most interesting and challenging field of study in many engineering applications such as shape memory alloy SMA , piezoelectric, piezocermaic, magnetostrictive, and electromechanical actuators. Since unmodeled hysteresis causes inaccuracy in trajectory tracking and decreases the performance of control systems, Journal of Applied Mathematics an accurate modeling of hysteresis behavior for performance evaluation and identification as well as controller design is essentially needed. To overcome this drawback, it is necessary to develop hysteresis models that their parameters can and precisely be identified and are suitable for real-time control and compensation system design 1. The first group of models is derived from the underlying physics of hysteresis and combined with empirical factors to describe the observed characteristics 3–5. Another major drawback of these physical models is that they are specific to a particular type of system, and this implies separate controller design techniques for each system 7
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