Nonlinear control of dynamic systems using single multiplicative neuron models

Jonathan G. Turner, Biswanath Samanta

Research output: Contribution to book or proceedingConference articlepeer-review

3 Scopus citations

Abstract

The paper presents an approach to nonlinear control of dynamic systems using artificial neural networks (ANN). A novel form of ANN, namely, single multiplicative neuron (SMN) model is proposed in place of more traditional multilayer perceptron (MLP). SMN derives its inspiration from the single neuron computation model in neuroscience. SMN model is trained off-line, to estimate the network weights and biases, using a population based stochastic optimization technique, namely, particle swarm optimization (PSO). Both off-line training and on-line learning of SMN have been considered. The development of the control algorithm is illustrated through the hardware-in-the-loop (HIL) implementation of DC motor speed control in LabVIEW environment. The controller based on SMN performs better than MLP. The simple structure and faster computation of SMN have the potential to make it a preferred candidate for implementation of real-life complex control systems.

Original languageEnglish
Title of host publicationDynamics, Control and Uncertainty
PublisherAmerican Society of Mechanical Engineers (ASME)
Pages173-181
Number of pages9
EditionPARTS A AND B
ISBN (Print)9780791845202
DOIs
StatePublished - 2012
EventASME 2012 International Mechanical Engineering Congress and Exposition, IMECE 2012 - Houston, TX, United States
Duration: Nov 9 2012Nov 15 2012

Publication series

NameASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE)
NumberPARTS A AND B
Volume4

Conference

ConferenceASME 2012 International Mechanical Engineering Congress and Exposition, IMECE 2012
Country/TerritoryUnited States
CityHouston, TX
Period11/9/1211/15/12

Scopus Subject Areas

  • Mechanical Engineering

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