Deep Water Drilling: Full Pressure Profile Control in Open Hole Section Utilizing Model Predictive Control

2009 ◽  
Author(s):  
Oeyvind Breyholtz ◽  
Gerhard Haukenes Nygaard
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Deep Water ◽  
Pressure Profile ◽  
Profile Control ◽  
Open Hole ◽  
Full Pressure

scholarly journals Nonlinear Model Predictive Control of a Robotic Soft Esophagus

2021 ◽  
Author(s):  
Dipankar Bhattacharya ◽  
Ryman Hashem ◽  
Leo Cheng ◽  
Peter Xu
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Dynamic Models ◽  
Pressure Sensors ◽  
Pressure Profile ◽  
Open Loop ◽  
Air Pressure ◽  
Embedded Sensing ◽  
And Control

<p>Strictures caused by esophageal cancer can narrow down the esophageal lumen, leading to dysphagia. Palliation of dysphagia has driven the development of a Robotic Soft Esophagus (RoSE) to provide a novel in vitro platform for esophageal stent testing and food viscosity studies. In RoSE, peristaltic wave generation and control were done in an open-loop manner since the conduit lacked visibility and embedded sensing capability. Hence, in this work, RoSE version 2.0 (RoSEv2.0) was designed with embedded Time Of Flight (TOF) and pressure sensors to measure conduit displacement and air pressure, respectively, for modeling and control. Model Predictive Control (MPC) of RoSEv2.0 was implemented to govern the peristalsis and air pressure profile autonomously. The implemented MPC used Sparse Identification Nonlinear Dynamics with Control (SINDYC) models to estimate the future states of ROSEv2.0. The dynamic models were discovered from the TOF and pressure sensors captured data. Peristalsis waves of speed 20 mm/s, wavelength 75 mm, and amplitudes 5, 7.5, and 10 mm were successfully generated by the MPC. Additionally, RoSEv2.0 with the MPC was employed to perform stent migration testing with various food boluses consistencies.</p>

scholarly journals Low EffortLiNuclear Fusion Plasma Control Using Model Predictive Control Laws

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Izaskun Garrido ◽  
Aitor J. Garrido ◽  
Jesús A. Romero ◽  
Edorta Carrascal ◽  
Goretti Sevillano-Berasategui ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Fusion Energy ◽  
Current Drive ◽  
Current Profile ◽  
Fusion Plasma ◽  
Successful Operation ◽  
Control Laws ◽  
Internal Inductance ◽  
Profile Control

One of the main problems of fusion energy is to achieve longer pulse duration by avoiding the premature reaction decay due to plasma instabilities. The control of the plasma inductance arises as an essential tool for the successful operation of tokamak fusion reactors in order to overcome stability issues as well as the new challenges specific to advanced scenarios operation. In this sense, given that advanced tokamaks will suffer from limited power available from noninductive current drive actuators, the transformer primary coil could assist in reducing the power requirements of the noninductive current drive sources needed for current profile control. Therefore, tokamak operation may benefit from advanced control laws beyond the traditionally used PID schemes by reducing instabilities while guaranteeing the tokamak integrity. In this paper, a novel model predictive control (MPC) scheme has been developed and successfully employed to optimize both current and internal inductance of the plasma, which influences the L-H transition timing, the density peaking, and pedestal pressure. Results show that the internal inductance and current profiles can be adequately controlled while maintaining the minimal control action required in tokamak operation.

Temperature Profile Control of a Rotary Dryer with Multivariable Model Predictive Control

1993 ◽  
Vol 26 (2) ◽  
pp. 159-162
Author(s):  
J. Nishizawa ◽  
M. Numata ◽  
M. Ohshima ◽  
T. Fujiwara ◽  
I. Hashimoto
Keyword(s):  
Model Predictive Control ◽  
Temperature Profile ◽  
Predictive Control ◽  
Multivariable Model ◽  
Rotary Dryer ◽  
Profile Control

Two-Level Nonlinear Model Predictive Control for Lean NOx Trap Regenerations

2010 ◽  
Vol 132 (4) ◽  
Author(s):  
Ming-Feng Hsieh ◽  
Junmin Wang ◽  
Marcello Canova
Keyword(s):  
Diesel Engine ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Nox Reduction ◽  
Nonlinear Model Predictive Control ◽  
Lean Nox Trap ◽  
Nox Trap ◽  
Profile Control ◽  
Lean Nox

This paper describes a two-level nonlinear model predictive control (NMPC) scheme for diesel engine lean NOx trap (LNT) regeneration control. Based on the physical insights into the LNT operational characteristics, a two-level NMPC architecture with the higher-level for the regeneration timing control and the lower-level for the regeneration air to fuel ratio profile control is proposed. A physically based and experimentally validated nonlinear LNT dynamic model is employed to construct the NMPC control algorithms. The control objective is to minimize the fuel penalty induced by LNT regenerations while keeping the tailpipe NOx emissions below the regulations. Based on the physical insights into the LNT system dynamics, different choices of cost function were examined in terms of the impacts on fuel penalty and tailpipe NOx slip amount. The designed control system was evaluated on an experimentally validated vehicle simulator, cX-Emissions, with a 1.9 l diesel engine model through the FTP75 driving cycle. Compared with a conventional LNT control strategy, 31.9% of regeneration fuel penalty reduction was observed during a single regeneration. For the entire cold-start FTP75 test cycle, a 28.1% of tailpipe NOx reduction and 40.9% of fuel penalty reduction were achieved.

scholarly journals Nonlinear Model Predictive Control of a Robotic Soft Esophagus

2021 ◽  
Author(s):  
Dipankar Bhattacharya ◽  
Ryman Hashem ◽  
Leo Cheng ◽  
Peter Xu
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Dynamic Models ◽  
Pressure Sensors ◽  
Pressure Profile ◽  
Open Loop ◽  
Air Pressure ◽  
Embedded Sensing ◽  
And Control

<p>Strictures caused by esophageal cancer can narrow down the esophageal lumen, leading to dysphagia. Palliation of dysphagia has driven the development of a Robotic Soft Esophagus (RoSE) to provide a novel in vitro platform for esophageal stent testing and food viscosity studies. In RoSE, peristaltic wave generation and control were done in an open-loop manner since the conduit lacked visibility and embedded sensing capability. Hence, in this work, RoSE version 2.0 (RoSEv2.0) was designed with embedded Time Of Flight (TOF) and pressure sensors to measure conduit displacement and air pressure, respectively, for modeling and control. Model Predictive Control (MPC) of RoSEv2.0 was implemented to govern the peristalsis and air pressure profile autonomously. The implemented MPC used Sparse Identification Nonlinear Dynamics with Control (SINDYC) models to estimate the future states of ROSEv2.0. The dynamic models were discovered from the TOF and pressure sensors captured data. Peristalsis waves of speed 20 mm/s, wavelength 75 mm, and amplitudes 5, 7.5, and 10 mm were successfully generated by the MPC. Additionally, RoSEv2.0 with the MPC was employed to perform stent migration testing with various food boluses consistencies.</p>

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