ABSTRACT
A neuro-control system was designed to control the knee joint to move in accordance with the desired trajectory of movement through stimulation of quadriceps muscle. This control system consisted of a neural controller and a fixed parameter proportional-integral-derivative (PID) feedback controller, which was designated as a neuro-PID controller. A multilayer feedforward time-delay neural network was used and trained as an inverse model of the functional electrical stimulation (FES)-induced quadriceps-lower leg system for direct feedforward control. The training signals for neural network learning were obtained from experimentation using a low-pass filtered random sequence to reveal the plant characteristics. The Nguyen-Widrow method was used to initialize the neural connection weights. The conjugate gradient descent algorithm was then used to modify these connection weights so as to minimize the errors between the desired outputs and the network outputs. The knee joint angle was controlled with only small deviations along the desired trajectory with the aid of the neural controller. In addition, the PID feedback controller was utilized to compensate for the residual tracking errors caused by disturbances and modeling errors. This control strategy was evaluated on one able-bodied and one paraplegic subject. The neuro-PID controller showed promise as a position controller of knee joint angle with quadriceps stimulation.
