A numerical verification of model predictive control using partitioned model for leg/wheel mobile robots

When leg/wheel mobile robots are controlled by model predictive control (MPC), the optimization is too complex for an embedded CPU. Then, we proposed a method which reduces computational complexity by sequential optimization for the models partitioned into each leg. However, robustness of the proposed method was not evaluated enough in the disturbing environment. In this paper, we conduct simulations on the inclined plane to evaluate the robustness. Through the simulations, the robustness of the proposed method is improved by changing the control cycle and the predictive length.