Abstract
Shape memory alloy- (SMA-) based actuators are widely applied in the compliant actuating systems. However, the measured data of the SMA-based compliant actuating system reveal the input-output hysteresis behavior, and the actuating precision of the compliant actuating system could be degraded by such hysteresis nonlinearities. To characterize such nonlinearities in the SMA-based compliant actuator precisely, a Jiles-Atherton model is adopted in this paper, and a modified particle swarm optimization (MPSO) algorithm is proposed to identify the parameters in the Jiles-Atherton model, which is a combination of several differential nonlinear equations. Compared with the basic PSO identification algorithm, the designed MPSO algorithm can reduce the local optimum problem so that the Jiles-Atherton model with the identified parameters can show good agreements with the measured experimental data. The good capture ability of the proposed identification algorithm is also examined through the comparisons with Jiles-Atherton model using the basic PSO identification algorithm.
| Original language | English |
|---|---|
| Article number | 7465461 |
| Journal | Complexity |
| Volume | 2019 |
| DOIs | |
| Publication status | Published - 2019 Jan 1 |
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Jiles-atherton based hysteresis identification of shape memory alloy-actuating compliant mechanism via modified particle swarm optimization algorithm. / Chen, Le; Feng, Ying; Li, Rui; Chen, Xinkai; Jiang, Hui.
In: Complexity, Vol. 2019, 7465461, 01.01.2019.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Jiles-atherton based hysteresis identification of shape memory alloy-actuating compliant mechanism via modified particle swarm optimization algorithm
AU - Chen, Le
AU - Feng, Ying
AU - Li, Rui
AU - Chen, Xinkai
AU - Jiang, Hui
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Shape memory alloy- (SMA-) based actuators are widely applied in the compliant actuating systems. However, the measured data of the SMA-based compliant actuating system reveal the input-output hysteresis behavior, and the actuating precision of the compliant actuating system could be degraded by such hysteresis nonlinearities. To characterize such nonlinearities in the SMA-based compliant actuator precisely, a Jiles-Atherton model is adopted in this paper, and a modified particle swarm optimization (MPSO) algorithm is proposed to identify the parameters in the Jiles-Atherton model, which is a combination of several differential nonlinear equations. Compared with the basic PSO identification algorithm, the designed MPSO algorithm can reduce the local optimum problem so that the Jiles-Atherton model with the identified parameters can show good agreements with the measured experimental data. The good capture ability of the proposed identification algorithm is also examined through the comparisons with Jiles-Atherton model using the basic PSO identification algorithm.
AB - Shape memory alloy- (SMA-) based actuators are widely applied in the compliant actuating systems. However, the measured data of the SMA-based compliant actuating system reveal the input-output hysteresis behavior, and the actuating precision of the compliant actuating system could be degraded by such hysteresis nonlinearities. To characterize such nonlinearities in the SMA-based compliant actuator precisely, a Jiles-Atherton model is adopted in this paper, and a modified particle swarm optimization (MPSO) algorithm is proposed to identify the parameters in the Jiles-Atherton model, which is a combination of several differential nonlinear equations. Compared with the basic PSO identification algorithm, the designed MPSO algorithm can reduce the local optimum problem so that the Jiles-Atherton model with the identified parameters can show good agreements with the measured experimental data. The good capture ability of the proposed identification algorithm is also examined through the comparisons with Jiles-Atherton model using the basic PSO identification algorithm.
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U2 - 10.1155/2019/7465461
DO - 10.1155/2019/7465461
M3 - Article
VL - 2019
JO - Complexity
JF - Complexity
SN - 1076-2787
M1 - 7465461
ER -