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A servo system position control method based on extended implicit generalized prediction of ipso

A servo system and generalized prediction technology, applied in the field of servo system control, can solve problems such as ignoring the nonlinearity of the system

Active Publication Date: 2017-11-17
HARBIN ENG UNIV
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Problems solved by technology

The algorithm still uses the traditional generalized predictive control, uses the least squares method for identification, and ignores the nonlinearity of the system in the model construction.

Method used

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  • A servo system position control method based on extended implicit generalized prediction of ipso
  • A servo system position control method based on extended implicit generalized prediction of ipso
  • A servo system position control method based on extended implicit generalized prediction of ipso

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Embodiment Construction

[0057] The present invention will be further described below in conjunction with accompanying drawing:

[0058] Steps of the invention

[0059] (1) collect the data of the position of the servo system at each moment;

[0060] (2) Identify the implicit generalized prediction model of the servo system at the current moment by using the auxiliary model least squares method according to the collected data;

[0061] (3) According to the collected data and the predicted output value of the identified implicit generalized prediction model, the particle swarm algorithm is used to identify the nonlinear part of the system model

[0062](4) Control the position of the servo system according to the current position of the servo system; make it move according to the predetermined track;

[0063] Enter the next moment and return to step (1);

[0064] According to the collected data, using the auxiliary model least squares method to identify the implicit generalized prediction model of t...

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Abstract

The invention, which belongs to the servo system control field, relates to an IPSO-extended-implicit-generalized-prediction-based servo system position control method. The method comprises: position data of a servo system at all time are collected; according to the collected data, an implicit generalized prediction model of the servo system at current time is identified by using a least square method of an aided model; non-linear part identification of the system model is carried out by using a particle swarm algorithm according to the collected data as well as a prediction output value of the identified implicit generalized prediction model; on the basis of the current servo system position, position control of the servo system is carried out; and thus the servo system moves according to a predetermined track; and the step (1) is carried out again at next time. According to the invention, the control method belongs to the field of the automatic control technology; and requirements during the actual industrial process can be met.

Description

technical field [0001] The invention belongs to the field of servo system control, and relates to a servo system position control method based on extended implicit generalized prediction of IPSO. Background technique [0002] Servo system has a wide range of uses in many aspects, such as CNC machine tools, robots. The precise control of the servo system is not only related to the quality of the product, but also to ensure the production effect. Therefore, the research on the control of the servo system has very strong practical significance. [0003] Since Richalct et al. proposed the concept of large-scale predictive control, people have been conducting research on predictive control. On the basis of Richalct, Clarke et al. proposed a generalized predictive control self-corrector. This algorithm is based on the carima model. The optimized performance index for a long period of time, combined with the identification and self-correction mechanism, has the characteristics of...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G05B13/04
CPCG05B13/042G05B2219/34013
Inventor 姚建均余瀚肖蕊陈硕王涛牛庆涛孙程
Owner HARBIN ENG UNIV
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