Lift analysis and anti-rolling control system design of Magnus rotating roll stabilizer at full speed range

Abstract

To analyze the impact of the operating mode of the Magnus rotating roll stabilizer on anti-rolling performance, a full speed range lift calculation method is proposed. By combining the micro-element approach with the mechanism of the Magnus effect force, the lift law of the rotating stabilizer in different modes could be obtained theoretically. Subsequently, correction factors are fitted for various operating conditions to correct the full speed range lift model through hydrodynamic simulation analysis of the stabilizer. In addition, RBF neural networks are introduced to approximate uncertain ship models under different modes to achieve anti-rolling control of the Magnus rotating stabilizer in various operating modes. When the stabilizer switches operating mode, this network provides a corresponding adaptive ship model for the sliding mode controller to optimize the anti-rolling effect of the sliding mode controller. The simulation shows that the corrected lift model has good predictive performance. Compared to traditional fin stabilizers and rotating stabilizers, the sliding mode control algorithm based on the RBF neural network can maintain excellent anti-rolling performance at full speed range, with a maximum anti-roll efficiency of over 93%.