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.

翻译：随着建筑行业的发展以及全球熟练劳动力的短缺，起重机控制的自动化对于安全高效的操作变得日益重要。自动起重机控制中的一个核心挑战是减少运动过程中的载荷摆动，这主要通过合适的回转轨迹来解决。在此背景下，经典基于模型的控制方法依赖于精确的动力学模型和专家调参，往往难以满足安全性和精度要求；而许多基于学习的方法则需要大量数据集和显著的计算资源。本文提出了一种行为数据驱动的框架，用于生成回转起重机的开环回转轨迹，在抑制载荷摆动的同时减少操作时间和能耗。该方法基于Willems基本引理及其推广，绕过了显式的系统建模，直接对测量得到的输入输出数据进行操作。本文提出了一个实用工作流程，以减少对专家知识的需求。尽管起重机动力学具有欠驱动特性，该方法仍能识别系统行为的非参数表示，并利用有限的数据和凸优化生成平滑的最优轨迹。所提出的轨迹生成方法在实验室起重机平台上进行了验证，并与一种成熟的基于模型的方法进行了对比，结果显示载荷摆动减少高达35%，跟踪误差减少高达43%，运行时间减少高达50%。