scholarly journals Neural Network Parameter Identification Based Constitutive Modeling of Superelastic Shape Memory Alloys

PAMM ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Andreas Kaup ◽  
Niklas Lenzen ◽  
Okyay Altay
Keyword(s):  
Neural Network ◽  
Parameter Identification ◽  
Shape Memory Alloys ◽  
Shape Memory ◽  
Constitutive Modeling ◽  
Network Parameter

A Neural Network Model of Restrained Recovery for Shape Memory Alloys

2013 ◽  
Vol 650 ◽  
pp. 172-177
Author(s):  
Shuang Wu ◽  
Shou Gen Zhao ◽  
Da Fang Wu ◽  
Xue Mei Yu
Keyword(s):  
Neural Network ◽  
Shape Memory Alloys ◽  
Shape Memory ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Constitutive Models ◽  
Nonlinear Function ◽  
Bp Neural Network Model ◽  
Bp Model

The methods of constitutive modeling of restrained recovery for Shape memory alloys (SMAs) were described in this paper and experiments were carried out to provide the essential data for the methods. The present mathematical constitutive models are inconvenient for engineering applications. Then a back propagation (BP) neural network model was developed for restrained recovery of SMAs. This BP neural network model can learn the hysteresis of SMAs in the process of heating and cooling based on its properties of nonlinear function mapping and adaptation, and it can predict the complete restrained recovery stress of SMAs with different initial strains. The predicted results obtained from the proposed BP model agree well with the experimental data. Moreover, the proposed BP model is more simple, convenient and low cost compared with the present mathematical constitutive models.

scholarly journals Research on Model Predictive Control of IPMSM Based on Adaline Neural Network Parameter Identification

Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4803 ◽  
Author(s):  
Lihui Wang ◽  
Guojun Tan ◽  
Jie Meng
Keyword(s):  
Neural Network ◽  
Model Predictive Control ◽  
Parameter Identification ◽  
Permanent Magnet ◽  
Predictive Control ◽  
Control Method ◽  
Permanent Magnet Synchronous Motor ◽  
Network Parameter ◽  
Maximum Torque Per Ampere ◽  
Adaline Neural Network

This paper reports the optimal control problem on the interior permanent magnet synchronous motor (IPMSM) systems. The control performance of the traditional model predictive control (MPC) controller is ruined due to the parameter uncertainty and mismatching. In order to solve the problem that the MPC algorithm has a large dependence on system parameters, a method which integrates MPC control method and parameter identification for IPMSM is proposed. In this method, the d-q axis inductances and rotor permanent magnet flux of IPMSM motor are identified by the Adaline neural network algorithm, and then, the identification results are applied to the predictive controller and maximum torque per ampere (MTPA) module. The experimental results show that the optimized MPC control proposed in this paper has a good steady state and robust performance.

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