Adaptive learning control method of piezoelectric ceramics driver

Abstract

The invention relates to an adaptive learning control method of a piezoelectric ceramics driver. The adaptive learning control method of the piezoelectric ceramics driver comprises the following steps of (1), building a dynamic hysteretic model of the piezoelectric ceramics driver and designing a control method with the artificial neural network and a PID combined, (2), adopting a reinforcement learning algorithm to achieve adaptive setting of PID parameters on line, (3), adopting a three-layer radial basis function network to approach a strategic function of an actuator in the reinforcement learning algorithm and a value function of an evaluator in the reinforcement learning algorithm; (4), inputting a system error, an error first-order difference and an error second-order difference through a first layer of the radial basis function network, (5), achieving mapping of the system state to the three PID parameters through the actuator in the reinforcement learning algorithm, and (6), judging the output of the actuator and generating an error signal through the evaluator in the reinforcement learning algorithm, and updating system parameters through the signal. The adaptive learning control method of the piezoelectric ceramics driver solves the hysteresis nonlinear problem of the piezoelectric ceramics driver, improves the repeated locating accuracy of a piezoelectric ceramics drive platform, and eliminates influence on a system from hysteresis nonlinearity of piezoelectric ceramics.

Description

technical field [0001] The invention is an adaptive learning control method of a piezoelectric ceramic driver, which belongs to the innovative technology of the adaptive learning control method of a piezoelectric ceramic driver. Background technique [0002] Piezoelectric ceramic actuators have the advantages of high positioning accuracy, large driving force, and fast response speed. They are ideal driving components in ultra-precision positioning and micro-displacement technology. However, due to its inherent hysteresis, nonlinearity, creep and other characteristics, the repeatability and accuracy of the micro-displacement mechanism are reduced, and the transient response speed is slowed down, which has caused certain difficulties in the application of piezoelectric ceramics. From the perspective of control design method, the focus and difficulty in improving the control accuracy of piezoelectric ceramic actuators is to overcome the influence of hysteresis. At present, hyst...

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Problems solved by technology

However, due to its inherent hysteresis, nonlinearity, creep and other characteristics, the repeatability and accuracy of the micro-displacement mechanism are reduced, and the transient response speed is slowed down, which has caused certain difficulties in the application of piezoelectric ceramics.

From the perspective of control design method, the focus and difficulty in improving the control accuracy of piezoelectric ceramic actuators is to overcome the influence of hysteresis. At present, hysteresis modeling and parameter identification are the main research methods, such as the classic Preisach model and the Prandtl-Ishinskii model. However, when the nonlinear structural characteristics of the system are difficult to describe, it is difficult for traditional mathematical modeling to truly reflect the dynamic characteristics of piezoelectric ceramics

In PID control based on neural network, supervised learning is generally used for parameter optimization, and the teacher signal in supervised learning is difficult to obtain

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