Dynamic Modeling of Bucket-Soil Interactions Using Koopman-DFL Lifting Linearization for Model Predictive Contouring Control of Autonomous Excavators

A lifting-linearization method based on the Koopman operator and Dual Faceted Linearization is applied to control of a robotic excavator. In excavation, bucket interacts with surrounding soil in highly nonlinear complex manner. Here, we propose represent bucket-soil dynamics set linear state equations higher-dimensional space. The space independent variables augmented by adding associated elements involved dynamics. These include resistive forces moment acting from soil, effective inertia that varies as captured into bucket. Variables these are treated additional variables, their time evolution represented another differential equations. lifted dynamic model then Model Predictive Contouring Control, where cost functional minimized convex optimization problem thanks tuned data-driven using simulator. Simulation experiments homogeneous verify effectiveness proposed lifting linearization compared its counterpart.