Transferring heavy payloads in maritime settings relies on efficient crane operation, limited by hazardous double-pendulum payload sway. This sway motion is further exacerbated in offshore environments by external perturbations from wind and ocean waves. Manual suppression of these oscillations on an underactuated crane system by human operators is challenging. Existing control methods struggle in such settings, often relying on simplified analytical models, while deep reinforcement learning (RL) approaches tend to generalise poorly to unseen conditions. Deploying a predictive controller onto compute-constrained, highly non-linear physical systems without relying on extensive offline training or complex analytical models remains a significant challenge. Here we show a complete real-time control pipeline centered on the MuJoCo MPC framework that leverages a cross-entropy method planner to evaluate candidate action sequences directly within a physics simulator. By using simulated rollouts, this sampling-based approach successfully reconciles the conflicting objectives of dynamic target tracking and sway damping without relying on complex analytical models. We demonstrate that the controller can run effectively on a resource-constrained embedded hardware, while outperforming traditional PID and RL baselines in counteracting external base perturbations. Furthermore, our system demonstrates robustness even when subjected to unmodeled physical discrepancies like the introduction of a second payload.

翻译：在海上环境中进行重型货物转运依赖于高效的起重机操作，但需应对危险的双摆货物摇摆问题。这种摇摆运动在近海环境中会因风浪等外部扰动而进一步加剧。操作人员通过人工方式抑制欠驱动起重机系统的此类振荡具有挑战性。现有控制方法在此类场景中效果有限——传统方法往往依赖简化解析模型，而深度强化学习（RL）方法则难以泛化至未见过工况。在不依赖大量离线训练或复杂解析模型的前提下，将预测控制器部署于计算受限、高度非线性的物理系统仍是一项重大挑战。本文展示了一套完整的实时控制流程，它以MuJoCo MPC框架为核心，利用交叉熵方法规划器直接在物理模拟器中评估候选动作序列。通过模拟展开，这种基于采样的方法成功协调了动态目标跟踪与摇摆阻尼这对相互冲突的目标，且无需依赖复杂解析模型。我们证明该控制器可在资源受限的嵌入式硬件上高效运行，同时在抑制外部基础扰动方面优于传统PID及强化学习基线方法。此外，即使面对引入第二个负载等未建模的物理差异，本系统仍展现出鲁棒性。