🤖 AI Summary
This study addresses the safety and precision challenges in automated slewing control of knuckle-boom cranes caused by payload oscillations. The authors propose an open-loop slewing trajectory generation method driven purely by behavioral input–output data, circumventing the need for explicit system modeling. Leveraging Willems’ behavioral theory and its extended formulations, the approach enables a non-parametric characterization of the underactuated system’s dynamics and generates smooth, optimal trajectories via convex optimization. Compared to conventional model-based strategies, the proposed method substantially reduces reliance on expert knowledge and large datasets. Experimental results demonstrate a 35% reduction in payload swing, a 43% decrease in tracking error, and a 50% shortening of execution time, highlighting its efficacy and practicality in real-world crane automation.
📝 Abstract
With the growth of the construction industry and the global shortage of skilled labor, the automation of crane control has become increasingly important for safe and efficient operations. A central challenge in automatic crane control is the reduction of load oscillations during motion, which is primarily addressed through appropriate slewing trajectories. In this context, classical model-based control methods rely on accurate dynamical models and expert tuning, and often struggle to meet safety and precision requirements, while many learning-based approaches require large data sets and significant computational resources. This paper proposes a behavioral data-driven framework for generating open-loop slewing trajectories for rotary cranes that suppress load sway while reducing operation time and energy consumption. The approach builds on Willems' fundamental lemma and its generalizations, to bypass explicit system modeling and operate directly on measured input-output data. A practical workflow is presented in this paper to reduce the need for expert knowledge. Despite the underactuated nature of the crane dynamics, the method identifies a nonparametric representation of the system behavior and generates smooth, optimal trajectories using limited data and convex optimization. The proposed trajectory generation method is validated on a laboratory crane setup and compared against an established model-based approach, achieving up to 35% reduction in load sway, 43% reduction in tracking error, and 50% reduction in travel time.