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Piezoelectric actuator is widely used in precision positioning mechanism for the advantages of ultra high resolution, high response frequency and rapid dynamic performance. But the displacement error is conducted for the inherent hysteretic nonlinear characteristics, and the tracking precision is limited. A modified modeling method combining the neural network with the genetic algorithm (GA) is designed...
Piezoelectric ceramics actuator is widely used in ultra high precision and tracking mechanism for the advantages of simple construction, high response frequency, rapid dynamic performance and excellent heavy carrying capacity. But the hysteretic nonlinear characteristic reduced the tracking precision. A modified modeling method based on dynamic recurrent neural network(DRNN) is designed in this paper...
Piezoelectric ceramics actuators have ultra high resolution, but the positioning precision is depressed by the inherent characteristic of hysteresis and nonLinearity. Accurate tracking is difficult to be achieved. An adaptive control sCheme combining neural network estimator with traditional PID controller is proposed to improve the performance of piezo-actuator in Scanning TunneLing Microscope. The...
Piezoelectric actuators are popularly applied in precision positioning mechanism in Scanning Tunneling Microscope due to its advantage of nanometer resolution. Accurate tracking is difficult to be achieved owing to the intrinsic nonlinear hysteresis of piezoelectric ceramics. An adaptive control scheme combining neural network with traditional PID controller is proposed to improve the performance...
A new control scheme based on neural network and traditional PID controller is proposed to improve the performance of precision stage in SPMs. The mathematical model is set up by analyzing the principle of the piezoelectric actuator. Mechanical parameters are identified online by a neural estimator. The traditional PID controller is replaced by a neural network PID controller. Weights in the network...
This paper presents an approach to modeling the dynamic hysteresis nonlinearity in the major loop of a piezoelectric stack actuator. The dynamic hysteresis model structure is analyzed and an artificial neural network is designed to identify the static hysteresis model in the major loop of the piezoelectric actuator. Experiment shows that the accuracy of the dynamic hysteresis model is better than...
A novel methodology is proposed in this paper for real-time modeling of a nanometer scale positioning stage driven by the piezoelectric ceramics. The precision of the stage is limited by the intrinsic nonlinear and hysteretic behaviors of the actuator. By integrating a second-order linear dynamics and a diagonal recurrent neural network, a nonlinear dynamic model is developed and experimentally validated...
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