Control of a 2-D bounding passive quadruped model with Poincaré map approximation and model predictive control

摘要

In this paper, we simplify the dynamics of a bounding quadrupedal robot by using a planar and conservative model which has a rigid body and two massless virtual spring legs to separately simulate the effects of the front and hind legs. The quantitative formulation of the model is derived by using the Lagrangian method. We proceed to search for stable operation points in state space, and the local system behaviors of which are then approximated by a trained neural network model. Next, a model predictive controller is utilized to stabilize the model. In simulation results, the controller succeeded in balancing the model in a dynamic bounding gait without any form of energy input. This research may serve as a guideline for real quadrupedal robots with actuators to create energy conservative gaits.