Neural network control for leg rhythmic movements via functional electrical stimulation

A neural network control system is developed to control the leg rhythmic movements with functional electrical stimulation (FES). The neural network contains two parts. One part is the neural oscillator. It derives from the central pattern generator of biological nervous system and serves as a feedforward controller generating the rhythmic pattern for the leg movements. Another part of the neural network is a radial basis function (RBF) artificial neural network, which maps the signals from the neural oscillator to regulate the FES stimulator, then the stimulator generates appropriate electrical pulse trains to stimulate the muscles. A computer dynamic model of a leg is developed in this paper. The model comprises of skeletal dynamics, revised hill-type muscle model with excitation-contraction dynamics. We have done some simulation studies with this control system on the one-segment leg musculoskeletal model. It shows satisfactory tracking performance and fast tracking response. This idea indicates feasible clinical application for the paralyzed patients in the future work.