Characterizing Hysteresis Nonlinearity Behavior of SMA Actuators by Krasnosel’skii-Pokrovskii Model
نویسندگان
چکیده
Krasnosel’skii-Pokrovskii (KP) model is one of the great operator-based phenomenological models which is used in modeling hysteretic nonlinear behavior in smart actuators. The time continuity and the parametric continuity of this operator are important and valuable factors for physical considerations as well as designing well-posed identification methodologies. In most of the researches conducted about the modeling of smart actuators by KP model, especially SMA actuators, only the ability of the KP model in characterizing the hysteretic behavior of the actuators is demonstrated with respect to some specified experimental data and the accuracy of the developed model with respect to other data is not validated. Therefore, it is not clear whether the developed model is capable of predicting hysteresis minor loops of those actuators or not and how accurate it is in this prediction task. In this paper the accuracy of the KP model in predicting SMA hysteresis minor loops as well as first order ascending curves attached to the major hysteresis loop are experimentally validated, while the parameters of the KP model has been identified only with some first order descending reversal curves attached to the major loop. The results show that, in the worst case, the maximum of prediction error is less than 18.2% of the maximum output and this demonstrates the powerful capability of the KP model in characterizing the hysteresis nonlinearity of SMA actuators.
منابع مشابه
Hysteresis Modeling, Identification and Fuzzy PID Control of SMA Wire Actuators Using Generalized Prandtl-Ishlinskii Model with Experimental Validation
In this paper, hysteretic behavior modeling, system identification and control of a mechanism that is actuated by shape memory alloy (SMA) wires are presented. The mechanism consists of two airfoil plates and the rotation angle between these plates can be changed by SMA wire actuators. This mechanism is used to identify the unknown parameters of a hysteresis model. Prandtl–Ishlinskii method is ...
متن کاملModeling SMA actuated systems based on Bouc-Wen hysteresis model and feed-forward neural network
Despite the fact that shape-memory alloy (SMA) has several mechanical advantages as it continues being used as an actuator in engineering applications, using it still remains as a challenge since it shows both non-linear and hysteretic behavior. To improve the efficiency of SMA application, it is required to do research not only on modeling it, but also on control hysteresis behavior of these m...
متن کاملA generalized Prandtl–Ishlinskii model for characterizing the hysteresis and saturation nonlinearities of smart actuators
Smart actuators, such as shape memory alloy (SMA) and magnetostrictive actuators, exhibit saturation nonlinearity and hysteresis that may be symmetric or asymmetric. The Prandtl–Ishlinskii model employing classical play operators has been used to describe the hysteresis properties of smart actuators that are symmetric in nature. In this study, the application of a generalized play operator capa...
متن کاملFeedforward-Feedback Hybrid Control for Magnetic Shape Memory Alloy Actuators Based on the Krasnosel'skii-Pokrovskii Model
As a new type of smart material, magnetic shape memory alloy has the advantages of a fast response frequency and outstanding strain capability in the field of microdrive and microposition actuators. The hysteresis nonlinearity in magnetic shape memory alloy actuators, however, limits system performance and further application. Here we propose a feedforward-feedback hybrid control method to impr...
متن کاملHysteresis Nonlinearity Identification Using New Preisach Model-Based Artificial Neural Network Approach
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 mod...
متن کامل