Abstract: In the complex and variable deep-sea environment, the compensation control of ship motion ensures the safety and efficiency of equipment installation and transportation in offshore wind farms. However, the ship motion posture compensation control system is severely affected by uncertainties, which significantly impact the accuracy of compensation control. In this paper, we propose a ship three-degree-of-freedom (3-DoF) motion posture stabilization control method based on the DTW-LSTM-MATD3 algorithm. We use the multi-agent twin delayed deep deterministic policy gradient (MATD3) to control a platform with six electric cylinders to achieve stable control. However, owing to random noise affecting the ship’s motion posture, we use a dynamic time warping (DTW) algorithm to distinguish between high-frequency noise and low-frequency tracking signals. Further, we embed a long short-term memory (LSTM) network into the MATD3 network to better align the Critic network’s training with the true Q-value. We use a combined reward function to enhance the agent’s exploration capability in complex dynamic environments. Finally, verification was conducted under sixth-level, abrupt sea conditions with high-frequency noise, as well as under real abrupt sea conditions, and a generalization test was also carried out. Simulation results show that the proposed DTW-LSTM-MATD3 method has great compensation control ability.

Key words: Compensation control; Multi-agent twin delayed deep deterministic policy gradient (MATD3) algorithm; Dynamic time warping (DTW) algorithm; Long short-term memory (LSTM) network

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