Feedforward closed loop composite control method and system of piezoelectric ceramic driver

A piezoelectric ceramic and composite control technology, which is applied in general control systems, control/regulation systems, adaptive control, etc., can solve problems such as asymmetrical hysteresis curves of piezoelectric ceramics

Inactive Publication Date: 2018-01-19
SUZHOU UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The classic PI model can describe the hysteretic nonlinear process with fewer parameters, and there will be no error accumulation, but it can only describe the symmetric hysteretic process, but the hysteresis curve of the piezoelectric ceramic is asymmetric

Method used

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  • Feedforward closed loop composite control method and system of piezoelectric ceramic driver
  • Feedforward closed loop composite control method and system of piezoelectric ceramic driver
  • Feedforward closed loop composite control method and system of piezoelectric ceramic driver

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0104] The feedforward and closed-loop composite control method of the piezoelectric ceramic driver in this embodiment includes: inputting the expected displacement r(t) of the piezoelectric ceramic driver into the feedforward model, and the feedforward model is calculated according to the expected displacement value r(t) Get the initial driving voltage u of the piezoceramic driver ε (t); With the piezoelectric ceramic displacement or the piezoelectric ceramic driver displacement as the feedback signal of the PID controller, the initial drive voltage u is controlled by the PID controller ε (t) Eliminate the error to obtain the driving voltage of the piezoelectric ceramic driver, and use the BP neural network to adjust the proportional parameter k of the PID controller p , integral adjustment parameter k i , the differential adjustment parameter k d optimize.

[0105] In the present embodiment, described feed-forward model is improved PI hysteresis inverse model, and describ...

Embodiment 2

[0169] The feed-forward and closed-loop composite control method of the piezoelectric ceramic driver of the present embodiment, on the basis of embodiment 1, the described utilization of BP neural network to the proportional adjustment parameter k of the PID controller p , integral adjustment parameter k i , the differential adjustment parameter k d Ways to optimize include:

[0170] S1 determines the structure of the BP neural network, that is, determines the number of nodes in the input layer M and the number of nodes in the hidden layer Q, and gives the initial values ​​of the weight coefficients of the input layer and the hidden layer and Select learning efficiency and inertia coefficient, at this time K=1;

[0171] S2 sampling to obtain the reference input rin(k) and the system output yout(k), and calculate the error at this moment e(k)=rin(k)-yout(k);

[0172] S2 calculates the input and output of the neurons in each layer of the BP neural network, and the output o...

Embodiment 3

[0207] The feedforward and closed-loop composite control system of the piezoelectric ceramic driver of this embodiment includes: a feedforward model unit, a PID controller, a PID parameter optimization unit, a sensor measurement unit, and a piezoelectric ceramic driver, wherein,

[0208] The feedforward model unit is used to obtain the initial drive voltage u of the piezoelectric ceramic driver according to the expected displacement r(t) input operation of the piezoelectric ceramic driver ε (t);

[0209] The sensor measuring unit is used to output the displacement of the piezoelectric ceramic driver, obtain the displacement of the piezoelectric ceramic in the piezoelectric ceramic driver, as the feedback signal of the closed-loop control of the PID controller;

[0210] The PID controller is used to control the initial driving voltage u according to the displacement of the piezoelectric ceramic driver and the displacement of the piezoelectric ceramic in the piezoelectric cerami...

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Abstract

The invention relates to a feedforward closed loop composite control method and system of a piezoelectric ceramic driver, which are designed to improve control precision. The arbitrary non-linear expression capability of a neural network is utilized, a PID controller for self-learning of parameters kp, ki and kd is established through learning of the performance of a piezoelectric ceramic closed loop control system, and the setting PID controller is utilized to control a piezoelectric ceramic driver through a feedback displacement closed loop control method. Parameters of the PID controller are adjusted on the basis of operating states of the system to achieve optimization of a certain performance index, so output states of output layer Neurons correspond to the three adjustable parameterskp, ki and kd of the PID controller, and through self-learning of the neural network and weighting coefficient adjustments, the output of the neural network corresponds to the parameters of the PID controller under a certain optimal control rule.

Description

technical field [0001] The invention relates to a feedforward and closed-loop compound control method and system of a piezoelectric ceramic driver. Background technique [0002] There are errors in the feedforward compensation of the hysteresis model of piezoelectric ceramics, and the anti-interference performance of the open-loop control is poor, and the closed-loop control has a feedback link that can effectively anti-interference. The PID controller obtains the control deviation e(t) according to the given value r(t) and the actual output value f(t), that is, e(t)=r(t)-f(t), and then the ratio of the deviation value , integral and differential form the control variable through linear combination, and control the controlled object. PID controller is a kind of linear controller, and its control law is: [0003] [0004] where k p - Scale factor; T I - integration time constant; T D - differential time constant; e(t) - deviation signal. [0005] In simple terms, the ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B13/04
Inventor 钟博文
Owner SUZHOU UNIV
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