journal article·2024-06-16Open access

Design of an Intelligent Cascade Control Scheme Using a Hybrid Adaptive Neuro-Fuzzy PID Controller for the Suppression of Drill String Torsional Vibration

Abdelbaset Laib, Mohamed Gharib

Applied Sciences· Volume 14· Issue 12

Abstract

Eliminating the excessive stick–slip torsional vibrations of drill strings by utilizing an effective controller can significantly increase penetration rates and reduce drilling operation costs. The present study aims to develop a fast and intelligent cascade control structure for a multi-degree-of-freedom drill string system under torsional vibrations. The proposed control configuration consists of two control loops in a cascade arrangement. The first controller estimates the top drive velocity reference from the actual drill bit velocity and its reference. The role of the second controller is to regulate the top drive velocity to its reference by generating the necessary torque for the drilling operation process to progress. Each control loop is designed based on a hybrid adaptive neuro-fuzzy PID and feedforward term. The latter assures fast regulation when there are sudden changes in operation. To evaluate the performance of the suggested cascade feedforward neuro-fuzzy PID (CFF-NFPID) control structure, extensive simulations were conducted using Matlab/Simulink. The simulation results clearly showed that the proposed CFF-NFPID controller provided high-performance control with variations in the weight on the bit and the desired drill bit rotary speed in comparison with that of cascade feedforward fuzzy PID, sliding-mode, cascade feedforward PID, cascade PID, and conventional PID controllers. Furthermore, the control robustness is very suitable despite the change in the system parameters.
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Research context

OpenAlex topics: Drilling and Well Engineering · Tunneling and Rock Mechanics · Oil and Gas Production Techniques

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Mechanical Engineering, Texas A&M University at Qatar, Doha 23874, Qatar

Department of Automatic, Faculty of Electrical Engineering, University of Science & Technology Houari Boumediene, Bab Ezzouar 16111, Algeria

Department of Engineering Technology & Industrial Distribution, Texas A&M University, College Station, TX 77843, USA

Funding

Qatar National Research Fund

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