Piezoelectric actuators have been received much attention for the advantages of high precision, no wear and rapid response, etc. However, the intrinsic hysteresis behavior of the piezoelectric materials seriously degraded the output performance of piezoelectric actuators. In this paper, to decrease such nonlinear effects and further improve the output performances of piezoelectric actuators, a modified nonlinear autoregressive moving average with exogenous inputs model, which could describe the rate-dependent hysteresis features of piezoelectric actuators was investigated. In the experiment, the different topologies of the proposed back propagation neural network algorithm were compared and the optimal topology was selected considering both the tracking precision and the structure complexity. The experimental results validated that the modified nonlinear autoregressive moving average with exogenous inputs model featured the hysteresis characteristics description ability with high precision, and the predicted motion matched well with the real trajectory. Then, the initial parameters of the back propagation neural network algorithm were further optimized by particle swarm optimization algorithm. The experimental results also verified that the proposed model based on particle swarm optimization–back propagation neural network algorithm was more accurate than that identified through the conventional back propagation neural network algorithm, and has a better predicting performance.

中文翻译：

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基于粒子群优化神经网络的压电执行器迟滞建模

压电执行器以其精度高、无磨损、响应速度快等优点备受关注。然而，压电材料固有的滞后行为严重降低了压电执行器的输出性能。在本文中，为了减少这种非线性效应并进一步提高压电执行器的输出性能，研究了一种具有外生输入模型的修正非线性自回归移动平均模型，该模型可以描述压电执行器的速率相关滞后特性。实验中，比较了所提出的反向传播神经网络算法的不同拓扑结构，并在考虑跟踪精度和结构复杂性的同时选择了最优拓扑结构。实验结果验证了改进的带外源输入的非线性自回归移动平均模型具有高精度的滞后特性描述能力，预测运动与真实轨迹匹配良好。然后，通过粒子群优化算法进一步优化反向传播神经网络算法的初始参数。实验结果也验证了所提出的基于粒子群优化-反向传播神经网络算法的模型比通过传统反向传播神经网络算法识别的模型更准确，具有更好的预测性能。并且预测的运动与真实轨迹匹配良好。然后，通过粒子群优化算法进一步优化反向传播神经网络算法的初始参数。实验结果也验证了所提出的基于粒子群优化-反向传播神经网络算法的模型比通过传统反向传播神经网络算法识别的模型更准确，具有更好的预测性能。并且预测的运动与真实轨迹匹配良好。然后，通过粒子群优化算法进一步优化反向传播神经网络算法的初始参数。实验结果也验证了所提出的基于粒子群优化-反向传播神经网络算法的模型比通过传统反向传播神经网络算法识别的模型更准确，具有更好的预测性能。