Constrained real-time model-predictive reference trajectory planning for rotary cranes

Abstract

Two-degree-of-freedom control (2DOF), i. e. a combination of feedforward and feedback control, can give good tracking control performance on many large-scale robotic systems such as cranes. Every 2DOF control system needs a reference trajectory. The generation of this reference trajectory is crucial: Too slow reference trajectories decrease system performance, while too aggressive trajectories can easily violate state or input constraints. This degrades tracking performance and can even cause accidents. In case of operator-controlled rotary cranes, the trajectory generation needs to be done in real-time which limits the number of available algorithms. In this contribution, a real-time repetitive optimal control trajectory planner for rotary cranes is presented. It takes input and state constraints into account. A fallback-strategy is provided in case no optimal control solution is found. The results are validated with simulations as well as measurements on a Liebherr LHM 420 mobile harbour crane.