Wu Yanbin, He Xiaomeng, Shi Linlong, Dong Shengli
College of Information Science and Technology, Dalian Maritime University, Dalian 116026, China.
College of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, China.
Sensors (Basel). 2025 Jan 23;25(3):679. doi: 10.3390/s25030679.
This paper proposes an adaptive dynamic positioning (DP) control method based on a multi-observer fusion architecture with minimal sensor requirements. A sliding mode observer is designed based on a high- and low-frequency superposition model to filter high-frequency state variables, while a finite-time convergence disturbance observer estimates unknown time-varying low-frequency disturbances online. For efficient handling of model uncertainties, a single-parameter learning neural network is implemented that requires only one parameter to be estimated online. The control system employs auxiliary dynamic systems to handle input saturation constraints and considers thruster system dynamics. Theoretical analysis demonstrates the stability of the observer-fusion control strategy, while simulation results based on the SimuNPS platform validate its effectiveness in state estimation and disturbance rejection compared to traditional sensor-dependent methods.
本文提出了一种基于多观测器融合架构的自适应动态定位（DP）控制方法，该方法对传感器的要求极低。基于高频和低频叠加模型设计了滑模观测器，以滤除高频状态变量，同时采用有限时间收敛干扰观测器在线估计未知时变低频干扰。为了有效处理模型不确定性，实现了一种单参数学习神经网络，该网络仅需在线估计一个参数。控制系统采用辅助动态系统来处理输入饱和约束，并考虑了推进器系统动力学。理论分析证明了观测器融合控制策略的稳定性，而基于SimuNPS平台的仿真结果验证了与传统依赖传感器的方法相比，该策略在状态估计和干扰抑制方面的有效性。
