Backlash operator and neural network-based adaptive filter

An adaptive filter and neural network technology, applied in the field of nonlinear hysteresis creep system modeling, can solve the problems of insufficient hysteresis nonlinear accuracy and complex implementation.

Inactive Publication Date: 2011-03-16
BEIJING INSTITUTE OF TECHNOLOGYGY
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Problems solved by technology

[0005] Aiming at the problem that the existing Backlash operator superposition modeling method is complex and the time delay line transverse adaptive filter has insufficient precision when modeling the hysteresis nonlinearity, the present invention proposes a method based on the structure of the transverse adaptive filter. Adaptive Filter of Backlash Operator and Neural Network and Its Modeling Method for Hysteresis-Creep System

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Embodiment Construction

[0049] In order to better illustrate the present invention, it will be further described below in conjunction with the accompanying drawings and embodiments.

[0050] The structural block diagram of adaptive filter based on Backlash operator and neural network of the present invention is as figure 1 As shown, it includes a creep module, multiple Backlash operator modules of the same width, a neural network module and an error calculation module. The neural network module includes two hidden layers and an output layer, and each layer consists of several adaptive neuron modules (including adaptive weighting module, adder module and activation function module). According to the formula (3), calculate the output of each serial Backlash operator link. The following process illustrates the effectiveness of formula (3).

[0051] for width r 1 and r 2 The transfer characteristics of the two Backlash operators in discrete form are:

[0052] y 1 ...

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Abstract

The invention relates to a Backlash operator and neural network-based adaptive filter and a method for modeling a creepage and hysteresis system and relates to the technical field of nonlinear hysteresis and creepage system modeling. The Backlash operator and neural network-based adaptive filter comprises a creepage module, a plurality of Backlash operator modules with the same width, a neural network module and an error calculation module. The design method for the adaptive filter is simple and practicable, delay items are avoided, the output of the filter is only related to the current input, and the hysteresis linearity can be reflected simultaneously. Meanwhile, the output of a Backlash operator serial structure is connected with the neural network, the weight can be adjusted in real time, massive sample data is not needed to be prepared in advance, and the modeling accuracy can be greatly improved. The method is suitable for modeling a piezoelectric ceramic actuator, a magnetostrictive actuator, an excited motor and other systems with hysteresis and creepage nonlinear characteristics.

Description

technical field [0001] The invention relates to an adaptive filter based on a Backlash operator and a neural network and a modeling method thereof for a creep hysteresis system, belonging to the technical field of nonlinear hysteresis creep system modeling. Background technique [0002] In recent years, sensors and actuators made of smart materials have been widely used in precision machining and precise positioning systems. However, the nonlinear characteristics of smart materials, such as hysteresis and creep, reduce the repeatability of these systems, slow down the transient response speed, reduce the control accuracy and even make the closed-loop system unstable, and increase the difficulty of controller design. These problems make it difficult for both classical control theory and modern control theory to effectively control them, so it is necessary to propose specific modeling and control methods to solve problems in this field. To compensate creep hysteresis nonlinea...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H03H17/02
Inventor 刘向东耿洁陈振赖志林
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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