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Copyright IFAC Artificial Intelligence in Real-Time Control, Delft, The Netherlands, 1992
 

Summary: Copyright © IFAC Artificial Intelligence in Real-Time
Control, Delft, The Netherlands, 1992
ADAPTIVE NEURAL NETWORK CONTROL OF FES·INDUCED
CYCLICAL LOWER LEG MOVEMENTS
S.H. Stroeve*, H.M. Franken*, P.H. Veltlnk* and W.T.C.van Luenen"
*BiomedicalEngineeringDivision,Department of Electrical Engineering, University ofTwente,TheNetherlands
**Control,Systemsand ComputerEngineering Group, Department ofElectricalEngineering,
University of'Twente, TheNetherlands
Abstract. As a first step to the control of paraplegic gait by functional electrical
stimulation (FES), the control of the swinging lower leg is being studied. This paper deals
with a neural control system, that has been developed for this case. The control system has
been tested for a model of the swinging lower leg using computer simulations. The neural
controller was trained by supervised learning (SL) and by backpropagation through time
(BTT). The performance of the controller with random initial weights was poor after
training with BTT and fair after SL. BTT training of the neural controller with weights,
which had been initialized by SL, resulted in good control. Training with BIT thus
improved the performance of the controller that initially had been trained by SL. An
adaptive neural control system based on BTT has been proposed and partially tested. The
controller adapted relatively fast to the change of an important model parameter.
Keywords. Adaptive control; backpropagation through time; biocybemetics; functional

  

Source: Al Hanbali, Ahmad - Department of Applied Mathematics, Universiteit Twente

 

Collections: Engineering