Intelligent control algorithm for dynamic positioning control system - Guan, Hongqiang (Frontiers in Mechanical Engineering, 2024)
In response to the problem that the current ship dynamic positioning capability cannot meet people's needs, this study designs a Kalman filter based on unscented optimization for the dynamic positioning control system. Then, targeted intelligent control algorithms, namely adaptive weight mutation particle swarm optimization algorithm and thrust optimization allocation algorithm, are designed for the dynamic positioning top-level controller and thrust optimization allocation controller that occupy an important position in the system. The results showed that the average error of the three degrees of freedom position processed by the proposed filter was 1.53m. Compared with other mainstream controllers, the average root mean square errors of the controllers based on adaptive weight mutation particle swarm optimization algorithm in environments A and B were 2.295 m and 1.8 m, respectively. The controller based on thrust optimization allocation algorithm in environment C could obtain the optimal solution when the fully rotating thruster reached the 7th second and the channel thruster reached the 4th second. In environment D, the thrust penalty area was crossed in the 46th second. In the dynamic positioning capability curve of the system, the experimental ship could achieve balanced environmental loads with significant differences at all angles. In summary, the proposed intelligent control algorithm can effectively improve the positioning ability of the dynamic positioning control system, which meets the current needs of people for ship navigation.