Neural network-based fuzzy vibration controller for offshore platform with random time delay

Abstract

A neural network-based fuzzy controller is proposed to attenuate the irregular wave-induced vibration of a steel-jacket offshore platform. Firstly, the offshore platform is modeled as a system with time-varying control delay under random wave forces. Secondly, disturbance rejection measures are taken in designing an optimal controller containing delayed states. Thirdly, neural networks are adopted to observe and restore the controlled system, in order to learn the delayed control law using instant state. Finally, fuzzy models are constructed for reducing the complexity in data collecting and neural network training. Trained with sample data from fuzzy models, the neuro-fuzzy observation system is able to reconstruct the control system, and the generalized neural network-based controller works efficiently in different delayed cases. It achieves better vibration-attenuating performance under uncertain control delay and random waves, when compared to existed optimal control laws and fuzzy controllers without neural networks. The main contributions of this paper are: 1) obtaining a neural network-based observer in state approximation; 2) designing a neural network-based controller based on fuzzy rules to cope with random control delay.