Two-Motor Synchronous Decoupling Control Based on Improved Incremental Regularized Extreme Learning Machine

Abstract:

Neural network generalized inverse (NNGI) can realize two-motor synchronous decoupling control, but traditional neural network (NN) exists many shortcomings, Regular extreme learning machine (RELM) has fast learning and good generalization ability, which is an ideal approach to approximate inverse system. But it is difficult to accurately give the reasonable number of hidden neurons. Improved incremental RELM(IIRELM) is prospected on the basis of analyzing RELM learning algorithm, which can automatically determine optimal network structure through gradually adding new hidden-layer neurons, and prediction model based on IIRELM is applied in two-motor closed-loop control based on NNGI, the decoupling control between velocity and tension is realized. The experimental results proved that the system has excellent performance.

• Periodical: Advanced Materials Research (Volumes 765-767)
• Pages: 1854-1857
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.765-767.1854
• Online since: September 2013
• Authors: Feng Wang, Jin Lin Ding, Hong Sun
• Keywords: Cholesky Factorization without Square Root, Decoupling Control, Generalized Inverse, IIRELM, Vector Mode

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