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Improved PI model identification method based on particle swarm-ant colony parallel crossover algorithm

A crossover algorithm and model identification technology, applied in the field of system identification, can solve problems such as low model accuracy and insufficient capability of piezoelectric micro-positioning platforms

Active Publication Date: 2020-11-10
JILIN UNIV
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Problems solved by technology

Among them, the PI model is a phenomenological model. Since the hysteresis curve described by the traditional PI model is symmetrical about the center of the operator and the function expression is a raised ring on both sides, the piezoelectricity measured by the experiment The hysteresis loop of the micro-positioning platform is non-centrosymmetric, which leads to the insufficient ability of the traditional PI model to represent the piezoelectric micro-positioning platform, resulting in low model accuracy

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  • Improved PI model identification method based on particle swarm-ant colony parallel crossover algorithm

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Embodiment Construction

[0070] Both the particle swarm and ant colony algorithm are intelligent optimization algorithms, which can effectively identify the system model parameters. The present invention combines the two algorithms and proposes a method based on the particle swarm-ant colony parallel crossover algorithm, which is convenient to develop and play the two The respective advantages of the algorithms can prevent the results from falling into local optimum and obtain a hysteresis model with higher precision.

[0071] Step 1: Obtain an improved PI model based on the traditional PI model and the dead zone operator: first propose an improved unilateral play operator whose mapping relationship is in the form of a curve based on the traditional unilateral play operator, and then connect the dead zone operator in series to obtain an asymmetric The unilateral play operator is improved, and then weighted and superimposed to obtain an improved PI model.

[0072] Step 2: Design the parameters of the p...

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Abstract

The invention discloses an improved PI model identification method based on a particle swarm ant colony parallel crossover algorithm, and belongs to the technical field of system identification. According to the improved PI model identification method based on the particle swarm ant colony parallel crossover algorithm, improvement of a traditional PI model is achieved through an improved Play operator and a dead zone operator which have the change slope, and the established model has the capacity of describing asymmetric hysteresis characteristics. The method comprises the steps that an improved PI model is obtained, a particle swarm ant colony parallel cross algorithm is designed to identify parameters of the improved PI model, a piezoelectric micro-positioning platform is built to collect data needed by the identification model, final model parameters are identified according to the particle swarm ant colony parallel cross algorithm in the second step, and a modeling result is given.The method has great research significance for promoting research on a method for eliminating hysteresis nonlinearity of the piezoelectric micro-positioning platform and popularizing the piezoelectric micro-positioning platform.

Description

technical field [0001] The invention belongs to the technical field of system identification. Background technique [0002] In modern high-precision instruments and ultra-precision processing technology, the piezoelectric micro-positioning platform is the core driving component, which has the advantages of small size, light weight, high resolution, and is not affected by changes in environmental temperature and humidity. However, its input and output mapping performance is inherently The asymmetric hysteretic nonlinearity seriously restricts the wider application of piezoelectric micropositioning platforms. Therefore, the establishment of an accurate model of the piezoelectric micro-positioning platform is conducive to speeding up the research on methods to reduce the specific influence of its hysteresis. At present, the main models used to describe the hysteresis characteristics are mainly divided into physical mechanism models based on the physical mechanism of materials a...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/00G06F17/16G05B11/42
CPCG06N3/006G06F17/16G05B11/42
Inventor 周淼磊孙丽媛王一帆徐瑞杨立平高巍韩志武
Owner JILIN UNIV
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