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首页> 外文期刊>Modern Physics Letters, B. Condensed Matter Physics, Statistical Physics, Applied Physics >Parameter identification of piezoelectric hysteresis model based on improved artificial bee colony algorithm
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Parameter identification of piezoelectric hysteresis model based on improved artificial bee colony algorithm

机译:基于改进的人工蜂菌落算法的压电滞段模型参数识别

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摘要

The widely used Bouc-Wen hysteresis model can be utilized to accurately simulate the voltage-displacement curves of piezoelectric actuators. In order to identify the unknown parameters of the Bouc-Wen model, an improved artificial bee colony (IABC) algorithm is proposed in this paper. A guiding strategy for searching the current optimal position of the food source is proposed in the method, which can help balance the local search ability and global exploitation capability. And the formula for the scout bees to search for the food source is modified to increase the convergence speed. Some experiments were conducted to verify the effectiveness of the IABC algorithm. The results show that the identified hysteresis model agreed well with the actual actuator response. Moreover, the identification results were compared with the standard particle swarm optimization (PSO) method, and it can be seen that the search performance in convergence rate of the IABC algorithm is better than that of the standard PSO method.
机译:广泛使用的BOUC-WEN滞后模型可用于精确模拟压电致动器的电压 - 位移曲线。为了识别BOUC-WEN模型的未知参数,本文提出了一种改进的人工蜂菌落(IABC)算法。在该方法中提出了一种搜索当前食品源最佳位置的指导策略,可以帮助平衡本地搜索能力和全局利用能力。和侦察蜜蜂用于搜索食物源的公式被修改以增加收敛速度。进行了一些实验以验证IABC算法的有效性。结果表明,鉴定的滞后模型与实际执行器响应相同。此外,将识别结果与标准粒子群优化(PSO)方法进行比较,可以看出,IABC算法的收敛速率的搜索性能优于标准PSO方法。

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