Title: Dual network control system for bottom hole throttling pressure control based on RBF with big data computing

Authors: Yanghou Chen

Addresses: School of Machinery and Automation, Weifang University, Weifang 261061, Shandong, China

Abstract: In the context of smart city development, the managed pressure drilling (MPD) drilling process faces many uncertainties, but the characteristics of the process are complex and require accurate wellbore pressure control. However, this process runs the risk of introducing un-modelled dynamics into the system. To this problem, this paper employs neural network control techniques to construct a dual-network system for throttle pressure control, the design encompasses both the controller and identifier components. The radial basis function (RBF) network and proportional features are connected in parallel in the controller structure, and the RBF network learning algorithm is used to train the identifier structure. The simulation results show that the actual wellbore pressure can quickly track the reference pressure value when the pressure setpoint changes. In addition, the controller based on neural network realises effective control, which enables the system to track the input target quickly and achieve stable convergence.

Keywords: controller; identifier; MDP; neural network; radial basis function; RBF.

DOI: 10.1504/IJDMB.2024.139459

International Journal of Data Mining and Bioinformatics, 2024 Vol.28 No.3/4, pp.365 - 380

Received: 07 Aug 2023
Accepted: 26 Oct 2023
Published online: 02 Jul 2024 *

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