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Smith Prediction and Compensation Method Based on Sixth Order b-spline Wavelet Neural Network

A spline wavelet and neural network technology, applied in the direction of instruments, adaptive control, control/regulation systems, etc., can solve problems such as control quality deterioration and system instability, and achieve the effect of improving accuracy and ideal suppression effect.

Active Publication Date: 2020-12-18
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

However, this method is very sensitive to the error of the process model, and the compensation effect depends on the accuracy of the compensator model. If the error is too large, the control quality will deteriorate, and the system may even become unstable.

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  • Smith Prediction and Compensation Method Based on Sixth Order b-spline Wavelet Neural Network
  • Smith Prediction and Compensation Method Based on Sixth Order b-spline Wavelet Neural Network
  • Smith Prediction and Compensation Method Based on Sixth Order b-spline Wavelet Neural Network

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Embodiment Construction

[0056] The Smith estimation compensation method based on the sixth-order B-spline wavelet neural network, its steps are as follows:

[0057] (1) Let the actual controlled object be:

[0058]

[0059] Where x represents the system state quantity, u represents the input quantity,

[0060] (2) Discretize formula (1) to get:

[0061]

[0062] where T n is the sampling time, T n+1 -T n is the sampling interval of the system state quantity x, n=0, 1, 2, 3...,

[0063] (3) Determine the value of u according to the actual situation, and determine a constant value Δx according to the model accuracy requirements,

[0064] (4) When x increases by Δx, that is, x(T n+1 )-x(T n )=Δx, record ΔT n=T n+1 -T n value,

[0065] (5) ΔT recorded by n value, calculate y n =Δx / ΔT n , get the learning sample y n , record the total number of learning samples,

[0066] (6) Arrange the obtained learning samples into a vector Y:

[0067]

[0068] (7) Sixth-order B-spline wavelet ...

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Abstract

The invention is a Smith estimation compensation method based on the sixth-order B-spline wavelet neural network. Solve the problem of low accuracy of the Smith predictor model and unsatisfactory suppression of interference. By establishing a differential equation for the measured object and performing discretization processing, the sampling interval of the system state quantity and the learning samples of the sixth-order B-spline wavelet neural network are obtained. After determining the neural network structure, input layer weights and hidden layer functions, After the number of nodes, iterative training is carried out to obtain the weight vector of the output layer and the neural network expression, so as to obtain the mathematical model of the Smith predictive compensator. The invention can model the nonlinear controlled object, and can effectively improve the precision of the process model, and at the same time, the characteristic of the limited frequency band of the wavelet neural network makes it ideal for suppressing interference.

Description

Technical field: [0001] The present invention is related to the Smith predictive compensation method. Background technique: [0002] Smith predictive control is a control strategy designed for pure lagging systems. In control theory, hysteresis means that the change of the controlled variable lags behind the change of the disturbance in time, which is a very common phenomenon. Pure hysteresis refers to the delay in the transmission of materials, energy or signals due to the limited transmission speed. Generally pure lag refers to the lag caused by the transmission speed limit. Smith predictive control is a kind of pure lag compensation control, which weakens and eliminates pure lag by introducing a compensator connected in parallel with the controlled object. Compensated by the Smith Predictor, the pure hysteresis is moved out of the closed-loop control loop so that it does not adversely affect the system. It can be seen from the displacement theorem of Laplace transform...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G05B13/04
CPCG05B13/042
Inventor 张治国施博文
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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