Integrated Analysis of Permeability Reduction Caused by Polymer Retention for Better Understanding Polymer Transport

Polymer retention is one of the most important factors to govern polymer propagation through porous media, determining whether successful or not. The focus of previous studies has been limited to polymer concentration loss caused by the retention; not only change in polymer concentration, but also reduction in reservoir permeability is the main issue for theoretical transport study. Due to the lack of accuracy of Langmuir isotherm describing the polymer retention mechanisms, this study proposes a new type of matching interpretation method to correlate the permeability reduction factors from experiments to permeability. In order to solve the problem of poorly matching results between estimation and observation, use of nonadsorptive constant conditionally selected in matching process was made. Based on the threshold permeability reduction factors, approximate critical permeability can be calculated to which nonadsorptive constant would be applied. Results showed significant improvements in the estimation of permeability reduction for both low and high permeability cores. In addition, effects of permeability reduction on polymer transport in field scale were analyzed using the proposed matching model. Thus, not only does this interpretation method help to evaluate prediction for accurate flow behavior, but also unwanted risk can be evaluated

Position Control of Brushless DC Motor using Adaptive Sliding mode Control with Time-delay Control

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Improved Robustness of Reinforcement Learning Based on Uncertainty and Disturbance Estimator

This paper proposes a method to improve the robustness of RLs based on model-free uncertainty and disturbance estimator (RL-based UDE). In the real environment, instead of using optimal trajectory and control techniques to perform complex tasks, it learns through RL and supplements robustness by using uncertainty and disturbance estimator (UDE). From UDE, the robotics system can be improved the stability by appropriately canceling the uncertainty and disturbance without efforts to obtain model information; hence the UDE can compensate for the performance degradation of RL when system is non-stationary. In addition, the performance can be improved by reducing the sensor noise from low-pass filter of UDE. It is shown through an experiment that the proposed RL-based UDE provides robustness.1

A Reinforcement Learning-based Adaptive Time-Delay Control and Its Application to Robot Manipulators

This study proposes an innovative reinforcement learning-based time-delay control (RL-TDC) scheme to provide more intelligent, timely, and aggressive control efforts than the existing simple-structured adaptive time-delay controls (ATDCs) that are well-known for achieving good tracking performances in practical applications. The proposed control scheme adopts a state-of-the-art RL algorithm called soft actor critic (SAC) with which the inertia gain matrix of the timedelay control is adjusted toward maximizing the expected return obtained from tracking errors over all the future time periods. By learning the dynamics of the robot manipulator with a data-driven approach, and capturing its intractable and complicated phenomena, the proposed RL-TDC is trained to effectively suppress the inherent time delay estimation (TDE) errors arising from time delay control, thereby ensuring the best tracking performance within the given control capacity limits. As expected, it is demonstrated through simulation with a robot manipulator that the proposed RL-TDC avoids conservative small control actions when large ones are required, for maximizing the tracking performance. It is observed that the stability condition is fully exploited to provide more effective control actions.1

Efficient Path Generation and Tracking Control for Autonomous Vehicles

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Adaptive Model-Free Control With Nonsingular Terminal Sliding-Mode for Application to Robot Manipulators

An adaptive model-free control with nonsingular terminal sliding-mode (AMC-NTSM) is proposed for high precision motion control of robot manipulators. The proposed AMC-NTSM employs one-sample delayed measurements to cancel nonlinearities and uncertainties of manipulators and to subsequently obtain sufficiently simple models for easy control design. In order to maintain high gain controls even when the joint angles are close to the reference target values and accordingly achieve high precision and fast response control, a nonlinear sliding variable is also adopted instead of a linear one, asymptotically stabilizing controls by guaranteeing even a finite-time convergence. In addition, sliding variables are reflected on control inputs to support fast convergence while achieving uniform ultimate boundedness of tracking errors. The control gains of the proposed AMC-NTSM are adaptively adjusted over time according to the magnitude of the sliding variable. Such adaptive control gains become high for fast convergence or low for settling down to steady motion with better convergence precision, when necessary. The switching gains of the proposed AMC-NTSM are also adaptive to acceleration such that inherent time delay estimation (TDE) errors can be suppressed effectively regardless of their magnitudes. The simulation and experiment show that the proposed AMC-NTSM has good tracking performance.11Ysciescopu

Compositional Simulation on the Flow of Polymeric Solution Alternating CO through Heavy Oil Reservoir

Water-alternating-gas (WAG) method provides superior mobility control of CO 2 and improves sweep efficiency. However, WAG process has some problems in highly viscous oil reservoir such as gravity overriding and poor mobility ratio. To examine the applicability of carbon dioxide to recover viscous oil from highly heterogeneous reservoirs, this study suggests polymer-alternating-gas (PAG) process. The process involves a combination of polymer flooding and CO 2 injection. In this numerical model, high viscosity of oil and high heterogeneity of reservoir are the main challenges. To confirm the effectiveness of PAG process in the model, four processes (waterflooding, continuous CO 2 injection, WAG process, and PAG process) are implemented and recovery factor, WOR, and GOR are compared. Simulation results show that PAG method would increase oil recovery over 45% compared with WAG process. The WAG ratio of 2 is found to be the optimum value for maximum oil recovery. The additional oil recovery of 3% through the 2 WAG ratio is achieved over the base case of 1: 1 PAG ratio and 180 days cycle period