Radial basis function neural network-based PID model for functional electrical stimulation system control

Functional electrical stimulation (FES) has been widely used in the area of neural engineering. It utilizes electrical current to activate nerves innervating extremities affected by paralysis. An effective combination of a traditional PID controller and a neural network, being capable of nonlinear expression and adaptive learning property, supply a more reliable approach to construct FES controller that help the paraplegia complete the action they want. A FES system tuned by radial basis function (RBF) neural network-based proportional-integral-derivative (PID) model was designed to control the knee joint according to the desired trajectory through stimulation of lower limbs muscles in this paper. Experiment result shows that the FES system with RBF neural network-based PID model get a better performance when tracking the preset trajectory of knee angle comparing with the system adjusted by Ziegler-Nichols tuning PID model.