Mixed Use of Analytical Derivatives and Algorithmic Differentiation for NMPC of Robot Manipulators

In the context of nonlinear model predictive control (NMPC) for robot manipulators, we address problem enabling mixed and transparent use algorithmic differentiation (AD) efficient analytical derivatives rigid-body dynamics (RBD) to decrease solution time subjacent optimal (OCP). Efficient functions RBD their are made available numerical optimization framework CasADi by overloading operators in implementations library Pinocchio adding a derivative-overloading feature CasADi. A comparison between AD is based on influence OCP, showing benefits using manipulators.