Neural Network Decoupling technique and its application to a powered wheelchair system

Publisher:
IEEE
Publication Type:
Conference Proceeding
Citation:
Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2015, pp. 4586 - 4589
Issue Date:
2015-08-25
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This paper proposes a neural network decoupling technique for an uncertain multivariable system. Based on a linear diagonalization technique, a reference model is designed using nominal parameters to provide training signals for a neural network decoupler. A neural network model is designed to learn the dynamics of the uncertain multivariable system in order to avoid required calculations of the plant Jacobian. To avoid overfitting problem, both neural networks are trained by the Lavenberg-Marquardt with Bayesian regulation algorithm that uses a real-time recurrent learning algorithm to obtain gradient information. Three experimental results in the powered wheelchair control application confirm that the proposed technique effectively minimises the coupling effects caused by input-output interactions even under the condition of system uncertainties.
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