Three-domain fuzzy wavelet width learning filtering system and method

Abstract

The invention discloses a three-domain fuzzy wavelet width learning filtering system and method, and the method comprises the steps: obtaining data collected by a sensor group in a master-slave mechanical arm through windows serial port software, and extracting a time sequence into a training sample and a test sample through a window function; establishing a three-domain fuzzy wavelet width learning model, and training the three-domain fuzzy wavelet width learning model through a training sample; transmitting the test sample to a trained three-domain fuzzy wavelet width learning model for prediction, and predicting a tremor signal; and eliminating the tremor signal according to the basic filtering mathematical model. According to the invention, an existing width learning system (BLS) is improved. A fuzzy domain is added in the design. Potential features can be extracted in a time domain and a frequency domain, fuzzy features of tremor interference in teleoperation signals can be extracted in a fuzzy domain, and finally, the tremor signals are eliminated through a basic filtering mathematical model, so that the purpose of eliminating the tremor interference caused by human arms on the basis of ensuring the existing performance is achieved.

Description

technical field [0001] The invention relates to the technical field of machine learning and filtering algorithms, in particular to a three-domain fuzzy wavelet width learning filtering system and method. Background technique [0002] With the development and maturity of technology, teleoperation system can replace human beings to complete complex tasks in harsh environments. Because of its strong robustness, high precision, good reliability and other advantages, the teleoperation system plays an increasingly important role. However, the master side in the master-slave teleoperation system has interference from the operator's arm tremor. This will affect the precision of the operation. [0003] In recent years, many filtering algorithms have been proposed and applied to predict and compensate for the disturbance caused by the operator's hand tremor. The relevant representative research works include: C.N.Riviere et al. proposed a weight-frequency Fourier linear combiner (w...

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Problems solved by technology

[0004] However, these existing filtering algorithms only design filtering algorithms and models in the time domain and frequency domain, and cannot eliminate the tremor interference caused by the human arm.

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