Experimental Validation of Nonlinear MPC on an Overhead Crane using Automatic Code Generation

Recent advances in improving the efficiency of nonlinear model predictive control (MPC) algorithms have made them suited for challenging mechatronic applications that require high sampling rates. We demonstrate this fact by applying a highly efficient nonlinear MPC algorithm to a laboratory-scale overhead crane setup, featuring a fast moving cart and a winch mechanism. The aim is to perform optimized point-to-point motions with varying line length while respecting actuator limits. In order to solve the resulting optimization problems in less than one millisecond, an automatically generated Gauss-Newton real-time iteration algorithm is employed. We show experimental results illustrating the control performance of the closed-loop system as well as the efficiency of the nonlinear MPC algorithm.