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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...
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