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Estimation Method of Sideslip Angle of Heavy-duty Mobile Robot Based on Wavelet Denoising and CNN-LSTM

Published: 23 May 2024 Publication History

Abstract

The key to the motion stability control of heavy-duty mobile robots for transshipment of large products is the accurate acquisition of the sideslip angle, and model-driven based methods are difficult to estimate the state quantities due to the high cost of direct measurements, dynamical nonlinearity and uncertainty. To this end, a new data-driven state estimation method is proposed, which combines the feature extraction capability of convolutional neural network (CNN) and the data memory property of long-short-term memory network (LSTM) to generate a time-lag nonlinear state estimation model through the noise reduction of conventional sensor measurements by the wavelet denoising method. Simulation software is used to simulate different working conditions to collect motion state quantities to form a dataset for training and simulation experiments on the estimation model, and the simulation estimation results of the estimation model are analysed to obtain the optimal estimation model. The simulation experiments show that the estimation accuracy of the sideslip angle estimation model based on wavelet threshold denoising and CNN-LSTM network is better than that of the single LSTM network estimation model.

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  1. Estimation Method of Sideslip Angle of Heavy-duty Mobile Robot Based on Wavelet Denoising and CNN-LSTM

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    ICAICE '23: Proceedings of the 4th International Conference on Artificial Intelligence and Computer Engineering
    November 2023
    1263 pages
    ISBN:9798400708831
    DOI:10.1145/3652628
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 23 May 2024

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