Intelligent Control for Deepwater Drilling Riser Disconnection and Recoil Considering System Uncertainty

Abstract

Recoil control for deepwater drilling riser system after emergency disconnection is a necessary technique in deepwater oil exploration. However, some dynamic parameters of the riser system are uncertain and difficult to measure, which poses severe challenges to the riser recoil control. Therefore, an intelligent riser recoil adaptive control method considering system uncertainties is established. Based on the nominal state-space expression of recoil control and closed-loop system stability, the modified control input considering model uncertainties is derived. The radial basis function (RBF) neural network is adopted to approximate model uncertainties, and the weight adaptive law satisfying Lyapunov stability is selected to realize dynamic compensation of uncertainties in control inputs. The results show that the proposed method is applicable to the actual recoil control valve with adjustment speed limit. The uncertainties of tensioner stiffness, damping, mud discharge friction, and riser buoyancy loads have a certain effect on recoil dynamic response and control performance. The RBF adaptive control method can effectively reduce the initial recoil oscillation height and reduce the risk of recoil bottoming. The findings can effectively solve the problem of recoil control without accurate system parameters in the engineering background.

Keywords

• drilling riser
• emergency disconnection
• recoil control
• parameter uncertainty
• radial basis function (rbf) neural network