Control of hopping height in legged robots using a neural-mechanical approach

We compare two previous approaches for hopping height control to the new scheme proposed in this paper. This new approach is an example of work in the developing area of neural-mechanical systems and has some very simplified versions of building blocks observed in nature, including a central pattern generator. Explicit formulas for hopping height and conditions for stability were obtained for all three approaches based on approximate Poincare return maps (not included). We also present a novel robot leg design and experimental data which supports our analysis. Our adaptive periodic forcing approach is shown to be comparable or out-perform the other two methods in terms of bandwidth requirement, hopping height, and stability properties