Neurofuzzy adaptive controlling of selective stimulation for FES: a case study

A controller was designed for the selective stimulation of the sciatic nerve with a multiple contact cuff electrode to generate a desired torque in the ankle joint of cat. The design integrates three approaches, artificial neural network (ANN) modeling, fuzzy logical adaptation, and geometrical mapping. The geometrical mapping refers to the vector transformation from the joint coordinates to the virtual muscle coordinates which have been conceptually developed to represent the major recruitment features of contact-based functional units in the physical plant. This method reduces the complexity of generating a data set for training the neural network in the feedforward path and implementing the on-line learning algorithm embedded in the feedback loop. The controller was evaluated by computer simulation with the experimental data obtained from the torque generation in five acute cats. The results show that the ANN-based feedforward is capable of predicting 65% of a given desired isometric torque, and the fuzzy logical machine is able to provide suitable gains for feedback modulation to reduce the error from 35 to 8.5% and produce a robust control.