Adaptive learning-based recoil control for deepwater drilling riser systems

This paper deals with the adaptive learning based recoil suppression control problem of deepwater drilling riser systems subject to parametric perturbations, platform heave motion, and friction resistance force of drilling fluid discharge. First, a stochastic configuration network based approximator is presented to predict the friction resistance force of drilling fluid discharge. Then, model reference adaptive learning recoil controllers are developed for the drilling riser system, and the existence condition and supporting algorithm are provided. Simulation results show that: (i) the designed stochastic configuration network approximator is efficient to predict the friction resistance of drilling fluid discharge on the riser; (ii) the presented model reference adaptive learning recoil controllers are effective to restrain the recoil response of the riser; (iii) compared with the existing recoil controllers, which are all based on the exact dynamic recoil control models, the model reference adaptive learning recoil controllers proposed in this paper are no longer constrained by the accuracy of recoil model; (iv) the model reference adaptive learning recoil controllers are more robust than the existing ones to the unmodeled dynamics, systematic uncertainty, and parametric perturbations, and have better transient performance of system thereby guaranteeing the safety of the drilling riser systems effectively.