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：在海上环境中转移重型载荷依赖于高效的起重机操作，但受到危险的双摆载荷摆动的限制。这种摆动运动在海上环境中因风和海浪的外部扰动而进一步加剧。由操作员手动抑制欠驱动起重机系统的这些振荡具有挑战性。现有的控制方法在此类环境中表现不佳，通常依赖于简化的解析模型，而深度强化学习方法往往对未见条件的泛化能力较差。在不依赖大量离线训练或复杂解析模型的情况下，将预测控制器部署到计算受限、高度非线性的物理系统上仍然是一个重大挑战。本文展示了一个完整的实时控制流程，其核心是MuJoCo MPC框架，该框架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。通过使用模拟推演，这种基于采样的方法成功地协调了动态目标跟踪与摆动阻尼这两个相互冲突的目标，而无需依赖复杂的解析模型。我们证明该控制器可以在资源受限的嵌入式硬件上有效运行，并且在抵消外部基础扰动方面优于传统的PID和RL基线方法。此外，即使受到未建模的物理差异（如引入第二个载荷）的影响，我们的系统也表现出鲁棒性。