New Neural Network Inverse Control of Two-Motor Drive System

Mei Kang; Wen Xiang Zhao; Jing Hua Ji; Guo Hai Liu

doi:10.4028/www.scientific.net/AMM.416-417.447

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 416-417New Neural Network Inverse Control of Two-Motor...

New Neural Network Inverse Control of Two-Motor Drive System

Abstract:

Two-motor drive system is a multi-variable, nonlinear and strongly coupled system. A new synchronous control strategy for two-motor system is proposed based on radial basis function (RBF) neural network inverse with particle swarm optimization. To enhance the system performance, the particle swarm optimization is adopted to optimize the RBF nerve center, an optimized RBF neural network inverse and a two-motor system is connected in series to form composite pseudo-linear system. This two-motor synchronous system can be decoupled into two independent linear subsystems for speed and tension. Then, the decoupled control is implemented by designing a linear closed-loop adjustor. The experimental results verify that the two-motor synchronous system can be decoupled well for speed and tension based on the proposed neural network inverse system. Also, the proposed system can deal with external disturbances with strong robustness.

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Periodical:

Applied Mechanics and Materials (Volumes 416-417)

Pages:

447-453

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.416-417.447

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Online since:

September 2013

Authors:

Mei Kang, Wen Xiang Zhao, Jing Hua Ji, Guo Hai Liu

Keywords:

Decoupling Control, Inverse System, Neural Network (NN), Particle Swarm Optimization (PSO)

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