Trajectory tracking control for rotary steerable systems using interval type-2 fuzzy logic and reinforcement learning. Issue 2 (January 2018)
- Record Type:
- Journal Article
- Title:
- Trajectory tracking control for rotary steerable systems using interval type-2 fuzzy logic and reinforcement learning. Issue 2 (January 2018)
- Main Title:
- Trajectory tracking control for rotary steerable systems using interval type-2 fuzzy logic and reinforcement learning
- Authors:
- Zhang, Chi
Zou, Wei
Cheng, Ningbo
Gao, Junshan - Abstract:
- Abstract: Rotary steerable system (RSS) is a directional drilling technique which has been applied in oil and gas exploration under complex environment for the requirements of fossil energy and geological prospecting. The nonlinearities and uncertainties which are caused by dynamical device, mechanical structure, extreme downhole environment and requirements of complex trajectory design in the actual drilling work increase the difficulties of accurate trajectory tracking. This paper proposes a model-based dual-loop feedback cooperative control method based on interval type-2 fuzzy logic control (IT2FLC) and actor-critic reinforcement learning (RL) algorithms with one-order digital low-pass filters (LPF) for three-dimensional trajectory tracking of RSS. In the proposed RSS trajectory tracking control architecture, an IT2FLC is utilized to deal with system nonlinearities and uncertainties, and an online iterative actor-critic RL controller structured by radial basis function neural networks (RBFNN) and adaptive dynamic programming (ADP) is exploited to eliminate the stick–slip oscillations relying on its approximate properties both in action function (actor) and value function (critic). The two control effects are fused to constitute cooperative controller to realize accurate trajectory tracking of RSS. The effectiveness of our controller is validated by simulations on designed function tests for angle building hole rate and complete downhole trajectory tracking, and byAbstract: Rotary steerable system (RSS) is a directional drilling technique which has been applied in oil and gas exploration under complex environment for the requirements of fossil energy and geological prospecting. The nonlinearities and uncertainties which are caused by dynamical device, mechanical structure, extreme downhole environment and requirements of complex trajectory design in the actual drilling work increase the difficulties of accurate trajectory tracking. This paper proposes a model-based dual-loop feedback cooperative control method based on interval type-2 fuzzy logic control (IT2FLC) and actor-critic reinforcement learning (RL) algorithms with one-order digital low-pass filters (LPF) for three-dimensional trajectory tracking of RSS. In the proposed RSS trajectory tracking control architecture, an IT2FLC is utilized to deal with system nonlinearities and uncertainties, and an online iterative actor-critic RL controller structured by radial basis function neural networks (RBFNN) and adaptive dynamic programming (ADP) is exploited to eliminate the stick–slip oscillations relying on its approximate properties both in action function (actor) and value function (critic). The two control effects are fused to constitute cooperative controller to realize accurate trajectory tracking of RSS. The effectiveness of our controller is validated by simulations on designed function tests for angle building hole rate and complete downhole trajectory tracking, and by comparisons with other control methods. … (more)
- Is Part Of:
- Journal of the Franklin Institute. Volume 355:Issue 2(2018)
- Journal:
- Journal of the Franklin Institute
- Issue:
- Volume 355:Issue 2(2018)
- Issue Display:
- Volume 355, Issue 2 (2018)
- Year:
- 2018
- Volume:
- 355
- Issue:
- 2
- Issue Sort Value:
- 2018-0355-0002-0000
- Page Start:
- 803
- Page End:
- 826
- Publication Date:
- 2018-01
- Subjects:
- Science -- Periodicals
Technology -- Periodicals
Patents -- United States -- Periodicals
505 - Journal URLs:
- http://www.elsevier.com/journals ↗
http://www.sciencedirect.com/science/journal/00160032 ↗ - DOI:
- 10.1016/j.jfranklin.2017.12.001 ↗
- Languages:
- English
- ISSNs:
- 0016-0032
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4755.000000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 5742.xml