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<p>Liquid sloshing in moving open containers poses significant risks in various industrial and engineering applications, often leading to spillage, contamination, and reduced operational safety. Effective control of sloshing is therefore critical for ensuring product integrity and preventing losses during transportation. This paper presents three novel Deep Reinforcement Learning (DRL)-based feedback control frameworks for automatic motion planning of an open cylindrical liquid container moving along a straight-line trajectory. The sloshing dynamics are modeled as a nonlinear underactuated system—specifically, a simple pendulum mounted on a moving cart—to capture the essential fluid-structure interaction while enabling control design in a simulation environment. Each proposed framework employs a DRL agent trained using the Deep Deterministic Policy Gradient (DDPG) algorithm to generate optimal control actions that minimize sloshing and reduce overall travel time. The agents are trained in a closed-loop feedback setting using the pendulum-cart model to ensure robustness and adaptability to dynamic disturbances induced by the sloshing liquid. The performance of the proposed DRL-based frameworks is rigorously evaluated and benchmarked against several conventional control strategies, including Super Twisting Control (STC), Linear Quadratic Regulator (LQR) and adaptive Sliding Mode Control (ASMC), under disturbance condition. Furthermore, to validate the practical applicability of the learned policies, the DRL-generated trajectories are tested in open-loop simulations using FLOW-3D computational fluid dynamics (CFD) software. This dual-layered validation approach demonstrates the effectiveness and robustness of the proposed methods in achieving efficient, spill-free transport in liquid handling systems.</p>

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Spill-free liquid container handling using deep reinforcement learning agents in feedback control

  • 期刊论文

  • 发表日期：

    2026年1月28日

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  • Ashish Kumar Shakya,
  • Mike Fogel,
  • Gopinatha Pillai,
  • Laurent Burlion,
  • Sohom Chakrabarty

摘要

Liquid sloshing in moving open containers poses significant risks in various industrial and engineering applications, often leading to spillage, contamination, and reduced operational safety. Effective control of sloshing is therefore critical for ensuring product integrity and preventing losses during transportation. This paper presents three novel Deep Reinforcement Learning (DRL)-based feedback control frameworks for automatic motion planning of an open cylindrical liquid container moving along a straight-line trajectory. The sloshing dynamics are modeled as a nonlinear underactuated system—specifically, a simple pendulum mounted on a moving cart—to capture the essential fluid-structure interaction while enabling control design in a simulation environment. Each proposed framework employs a DRL agent trained using the Deep Deterministic Policy Gradient (DDPG) algorithm to generate optimal control actions that minimize sloshing and reduce overall travel time. The agents are trained in a closed-loop feedback setting using the pendulum-cart model to ensure robustness and adaptability to dynamic disturbances induced by the sloshing liquid. The performance of the proposed DRL-based frameworks is rigorously evaluated and benchmarked against several conventional control strategies, including Super Twisting Control (STC), Linear Quadratic Regulator (LQR) and adaptive Sliding Mode Control (ASMC), under disturbance condition. Furthermore, to validate the practical applicability of the learned policies, the DRL-generated trajectories are tested in open-loop simulations using FLOW-3D computational fluid dynamics (CFD) software. This dual-layered validation approach demonstrates the effectiveness and robustness of the proposed methods in achieving efficient, spill-free transport in liquid handling systems.