Speed Control of a Pneumatic Monopod using a Neural Network
Kale Harbick and Gaurav S. Sukhatme
We discuss a speed controller for a hopping robot with a pneumatically powered leg. The controller uses a neural network to model the neutral point as a function of running speed and hopping height. The network is trained off-line using training data taken from a simulated hopper that is manually controlled by a human. Simulation experiments of hopping in the sagittal plane show improved performance over a Raibert PD controller, which uses a linear approximation for the neutral point.