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压电陶瓷致动器输入速率相关迟滞的混合补偿方案

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提出了一种关于压电陶瓷致动器的混合补偿方案。在混合补偿方案中,对压电陶瓷致动器的输入速率相关迟滞特性进行了补偿。前馈控制器是一种新型的输入速率相关的神经网络迟滞逆模型,而反馈控制器为比例-积分-微分(proportion integration differentiation,PID)控制器。在所提出的逆模型中,输入速率相关辅助逆算子(rate-dependent auxiliary inverse operator,RAIO)和迟滞的输出构造了逆模型的扩展输入空间(expanded input space,EIS),将多值映射的迟滞逆映射转换为单值映射,并在理论上分析了所提RAIO的消去、输入速率相关特性和连续性。基于EIS方法,建立了迟滞的神经网络逆模型,即动态反向传播神经网络(dynamic back propagation neural network,DBPNN)模型。此外,还设计了针对压电陶瓷致动器的混合补偿方案来补偿迟滞。最后,将所提方法、常规PID控制器和含改进的输入速率相关的Prandtl-Ishlinskii逆模型的混合控制器应用于实验。实验结果表明,所提方法在系统性能上具有明显的优势。
A Hybrid Compensation Scheme for the Input Rate-Dependent Hysteresis of the Piezoelectric Ceramic Actuators
A hybrid compensation scheme for piezoelectric ceramic actuators(PEAs)is proposed.In the hybrid compensation scheme,the input rate-dependent hysteresis characteristics of the PEAs are compensated.The feedforward controller is a novel input rate-dependent neural network hysteresis inverse model,while the feedback controller is a proportion integration differentiation(PID)controller.In the proposed inverse model,an input rate-dependent auxiliary inverse operator(RAIO)and output of the hysteresis construct the expanded input space(EIS)of the inverse model which transforms the hysteresis inverse with multi-valued mapping into single-valued mapping,and the wiping-out,rate-dependent and continuous properties of the RAIO are analyzed in theories.Based on the EIS method,a hysteresis neural network inverse model,namely the dynamic back propagation neural network(DBPNN)model,is established.Moreover,a hybrid compensation scheme for the PEAs is designed to compensate for the hysteresis.Finally,the proposed method,the conventional PID controller and the hybrid controller with the modified input rate-dependent Prandtl-Ishlinskii(MRPI)model are all applied in the experimental platform.Experimental results show that the proposed method has obvious superiorities in the performance of the system.

hybrid controlinput rate-dependent hysteresisinverse modelneural networkpiezoelectric ceramic actuator

董瑞丽、谭永红、侯佳佳、郑榜生

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东华大学信息科学与技术学院,上海 201620

上海师范大学机械与电子工程学院,上海 200234

混合控制 输入速率相关的迟滞 逆模型 神经网络 压电陶瓷致动器

国家自然科学基金国家自然科学基金国家自然科学基金

621712856197112062327807

2024

东华大学学报(英文版)
东华大学

东华大学学报(英文版)

影响因子:0.091
ISSN:1672-5220
年,卷(期):2024.41(4)