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Neural-network based model predictive control for piezoelectric-actuated stick-slip micro-positioning devices
Long Cheng; Chao Zhou; Zeng-Guang Hou; Min Tan
2016
Conference NameIEEE International Conference on Advanced Intelligent Mechatronics (AIM)
Conference DateJUL 12-15, 2016
Conference PlaceBanff
CountryCanada
AbstractThe piezoelectric-actuated stick-slip micro positioning devices (PASSMDs) have been drawing considerable attention for micro-positioning applications due to their theoretically unlimited motion and good positioning precision. However, the inherent hysteresis of the piezoelectric actuators (PEAs) seriously deteriorates the positioning accuracy of the PASSMDs. In addition, due to the stick-slip actuated principle of PASSMDs, the control of PASSMDs is a challenging job in the literature. This paper proposes a neural network based model predictive control for PASSMDs, which includes the one step control phase and the sub-step control phase. By the proposed method, the hysteresis can be effectively dealt with, which can achieve a relatively accurate positioning performance of PASSMDs. Moreover, to verify the proposed method, a prototype of PASSMDs is developed and experiments are conducted. The experiment results show that the proposed method is an promising way to solve the positioning control of PASSMDs.
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23134
Collection复杂系统管理与控制国家重点实验室_先进机器人
Recommended Citation
GB/T 7714
Long Cheng,Chao Zhou,Zeng-Guang Hou,et al. Neural-network based model predictive control for piezoelectric-actuated stick-slip micro-positioning devices[C],2016.
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