Optimal Decompression of Divers [Applications of Control]

Decompression modeling of divers is a research field that is over 100 years old and since the beginning has been studied mainly as a clinical and biomedical problem. The topic is largely unexplored by the technological sciences using methods and theories for modeling and control. This article outlines a structure where the process of bubble formation is modeled as a nonlinear dynamic model and then used to design a state estimator and model-based predictor. Procedures are then calculated using explicit MPC. We further discussed dive-computer implementations using approximate explicit solutions. Finally, we showed practical differences for divers using computers that implement this approach. When future advances in sensor technology are made, the present structure can be further developed to include more feedback control of the estimator and optimal control formulation.

[1]  Tor Arne Johansen,et al.  Barrier function nonlinear optimization for optimal Decompression of divers , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[2]  R. D. Venter,et al.  Bubble evolution in a stirred volume of liquid closed to mass transport , 1983 .

[3]  P B Bennett,et al.  A deep stop during decompression from 82 fsw (25 m) significantly reduces bubbles and fast tissue gas tensions. , 2004, Undersea & hyperbaric medicine : journal of the Undersea and Hyperbaric Medical Society, Inc.

[4]  W. Gerth,et al.  Mathematical Model of Diffusion-Limited Evolution of Multiple Gas Bubbles in Tissue , 2003, Annals of Biomedical Engineering.

[5]  Tor Arne Johansen,et al.  Approximate explicit receding horizon control of constrained nonlinear systems , 2004, Autom..

[6]  David Q. Mayne,et al.  Constrained model predictive control: Stability and optimality , 2000, Autom..

[7]  Nihat M. Gürmen,et al.  Simulation of dynamic bubble spectra in tissues , 2001, IEEE Transactions on Biomedical Engineering.

[8]  Tor Arne Johansen,et al.  Optimal Decompression through Multi-Parametric Nonlinear Programming , 2010 .

[9]  Alf O. Brubakk,et al.  Use of heart rate monitoring for an individualized and time-variant decompression model , 2010, European Journal of Applied Physiology.

[10]  T. Langø,et al.  Diffusion coefficients and solubility coefficients for gases in biological fluids and tissues: a review. , 1996, Undersea & hyperbaric medicine : journal of the Undersea and Hyperbaric Medical Society, Inc.

[11]  Alf O. Brubakk,et al.  Parameter estimation of the copernicus decompression model with venous gas emboli in human divers , 2010, Medical & Biological Engineering & Computing.

[12]  W. Butler.  Caisson disease during the construction of the Eads and Brooklyn Bridges: A review. , 2004, Undersea & Hyperbaric Medicine.

[13]  Haakon Tjelmeland,et al.  Validation of decompression procedures based on detection of venous gas bubbles: A Bayesian approach. , 2007, Aviation, space, and environmental medicine.

[14]  E. Heath.  Essentials of Exercise Physiology, 2nd Edition , 2000 .

[15]  J S Haldane,et al.  The Prevention of Compressed-air Illness , 1908, Journal of Hygiene.

[16]  B R Wienke.  Reduced gradient bubble model. , 1990, International journal of bio-medical computing.

[17]  P. M. Duff,et al.  Decompression Sickness From Commercial Offshore Air-Diving Operations On the UK Continental Shelf During 1982 To 1988 , 1989 .

[18]  Darrell M. Wilson,et al.  A Closed-Loop Artificial Pancreas Using Model Predictive Control and a Sliding Meal Size Estimator , 2009, Journal of diabetes science and technology.

[19]  M. Burkard,et al.  The oxygen window and decompression bubbles: estimates and significance. , 1993, Aviation, space, and environmental medicine.

[20]  Yount De,et al.  On the use of a bubble formation model to calculate diving tables. , 1986 .

[21]  A. Grancharova,et al.  Computational Aspects of Approximate Explicit Nonlinear Model Predictive Control , 2007 .

[22]  Alf O. Brubakk,et al.  [abstract] DIFFERENCE IN BUBBLE FORMATION USING DEEP STOPS IS DEPENDENT ON LENGTH OF BOTTOM TIME; EXPERIMENTAL FINDINGS AND THEORETICAL SUPPORT. , 2007 .

[23]  D. Yount,et al.  On the use of a bubble formation model to calculate diving tables. , 1986, Aviation, space, and environmental medicine.

[24]  J M Waligora,et al.  Information about venous gas emboli improves prediction of hypobaric decompression sickness. , 1998, Aviation, space, and environmental medicine.

[25]  P. I. Barton,et al.  Dynamic optimization of hybrid systems , 1998 .

[26]  Cj Lambertsen,et al.  Development and evaluation of a decompression stress index based on tissue bubble dynamics, Part 3 , 1991 .

[27]  Tor Arne Johansen,et al.  Hardware Synthesis of Explicit Model Predictive Controllers , 2007, IEEE Transactions on Control Systems Technology.

[28]  B. Bequette,et al.  A tutorial on biomedical process control , 2007 .

[29]  Alf O. Brubakk,et al.  A Dynamic Two-Phase Model for Vascular Bubble Formation During Decompression of Divers , 2009, IEEE Transactions on Biomedical Engineering.

[30]  Paul I. Barton,et al.  Dynamic optimization of hybrib systems , 1998 .