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Onboard MuJoCo-based Model Predictive Control for Shipboard Crane with Double-Pendulum Sway Suppression

Oscar Pang, Lisa Coiffard, Paul Templier, Luke Beddow, Kamil Dreczkowski, Antoine Cully

Abstract

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.

Onboard MuJoCo-based Model Predictive Control for Shipboard Crane with Double-Pendulum Sway Suppression

Abstract

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.
Paper Structure (22 sections, 3 equations, 5 figures, 5 tables, 1 algorithm)

This paper contains 22 sections, 3 equations, 5 figures, 5 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Background
  3. Problem Formulation
  4. Sampling-Based Model Predictive Control
  5. Related Work
  6. Classical and Adaptive Anti-Sway Crane Control
  7. Learning-Based Approaches
  8. Sampling-Based MPC on Robotic Platforms
  9. Method
  10. Modeling and System Identification
  11. Cross-Entropy Method for Model Predictive Control
  12. Cross-Entropy Method for Real-Time Control
  13. Cost Function Design
  14. State Estimation and Disturbance Prediction
  15. Experimental Setup
  16. ...and 7 more sections

Figures (5)

  • Figure 1: Experimental setup of a boom-type crane mounted on a motion platform.
  • Figure 2: MJPC controller architecture.
  • Figure 3: Payload position error from target (top row) and angular tilt (bottom row) over various motion platform settings. We plot the median (solid line) and IQR (shaded area) over 10 replications.
  • Figure 4: Mean nominal return over 50 replications for various CEM planning iterations $i$ in simulation. We also plot the planning time per cycle for each iteration parameter.
  • Figure 5: Mean payload position error from target, on the real crane, over 60s interval for various CEM planning iterations.