THE dawn of a new millennium augurs both hopes and challenges. Since the start of the 21st century, most particularly since the 16th National Congress of the Communist Party of China (CPC) 10 years ago, China has cove...THE dawn of a new millennium augurs both hopes and challenges. Since the start of the 21st century, most particularly since the 16th National Congress of the Communist Party of China (CPC) 10 years ago, China has covered an uneven path of development. Devastating natural disasters, along with grim challenges of the international financial crisis, prompted and led to the CPC’s formulation of effective international and domestic policies. Having surmounted difficulties and advanced under the CPC leadership, the country has maintained a steady momentum of rapid development towards its objective of becoming an all-round moderately prosperous society.展开更多
Piezo-actuated stage is a core component in micro-nano manufacturing field.However,the inherent nonlinearity,such as rate-dependent hysteresis,in the piezo-actuated stage severely impacts its tracking accuracy.This st...Piezo-actuated stage is a core component in micro-nano manufacturing field.However,the inherent nonlinearity,such as rate-dependent hysteresis,in the piezo-actuated stage severely impacts its tracking accuracy.This study proposes a direct adaptive control(DAC)method to realize high precision tracking.The proposed controller is designed by a time delay recursive neural network.Compared with those existing DAC methods designed under the general Lipschitz condition,the proposed control method can be easily generalized to the actual systems,which have hysteresis behavior.Then,a hopfield neural network(HNN)estimator is proposed to adjust the parameters of the proposed controller online.Meanwhile,a modular model consisting of linear submodel,hysteresis submodel,and lumped uncertainties is established based on the HNN estimator to describe the piezoactuated stage in this study.Thus,the performance of the HNN estimator can be exhibited visually through the modeling results.The proposed control method eradicates the adverse effects on the control performance arising from the inaccuracy in establishing the offline model and improves the capability to suppress the influence of hysteresis on the tracking accuracy of piezo-actuated stage in comparison with the conventional DAC methods.The stability of the control system is studied.Finally,a series of comparison experiments with a dual neural networks-based data driven adaptive controller are carried out to demonstrate the superiority of the proposed controller.展开更多
文摘THE dawn of a new millennium augurs both hopes and challenges. Since the start of the 21st century, most particularly since the 16th National Congress of the Communist Party of China (CPC) 10 years ago, China has covered an uneven path of development. Devastating natural disasters, along with grim challenges of the international financial crisis, prompted and led to the CPC’s formulation of effective international and domestic policies. Having surmounted difficulties and advanced under the CPC leadership, the country has maintained a steady momentum of rapid development towards its objective of becoming an all-round moderately prosperous society.
基金supported by the National Natural Science Foundation of China(Grant Nos.51675228 and 51875237)the Key Project of Science and Technology Development Plan of Jilin Province,China(Grant No.20190303020SF)。
文摘Piezo-actuated stage is a core component in micro-nano manufacturing field.However,the inherent nonlinearity,such as rate-dependent hysteresis,in the piezo-actuated stage severely impacts its tracking accuracy.This study proposes a direct adaptive control(DAC)method to realize high precision tracking.The proposed controller is designed by a time delay recursive neural network.Compared with those existing DAC methods designed under the general Lipschitz condition,the proposed control method can be easily generalized to the actual systems,which have hysteresis behavior.Then,a hopfield neural network(HNN)estimator is proposed to adjust the parameters of the proposed controller online.Meanwhile,a modular model consisting of linear submodel,hysteresis submodel,and lumped uncertainties is established based on the HNN estimator to describe the piezoactuated stage in this study.Thus,the performance of the HNN estimator can be exhibited visually through the modeling results.The proposed control method eradicates the adverse effects on the control performance arising from the inaccuracy in establishing the offline model and improves the capability to suppress the influence of hysteresis on the tracking accuracy of piezo-actuated stage in comparison with the conventional DAC methods.The stability of the control system is studied.Finally,a series of comparison experiments with a dual neural networks-based data driven adaptive controller are carried out to demonstrate the superiority of the proposed controller.
