James B. Rawlings

Paul A. Elfers Professor and W. Harmon Ray Professor

Room: 2006
Engineering Hall
1415 Engineering Drive
Madison, WI 53706

Ph: (608) 263-5859
Fax: (608) 265-8794
rawlings@engr.wisc.edu


Profile Summary

Chemical process monitoring and control: Chemical processes are inherently nonlinear and must operate at their design constraints to achieve optimal economic performance. We have developed methods of moving horizon estimation and model predictive control to monitor and control the operation of chemical processes. This research has provided new theoretical results as well as practical, implementable methods for industrial application.

The Texas-Wisconsin-California Control Consortium (TWCCC), composed of leading chemical, microelectronic and pharmaceutical companies was created to promote these industrial collaborations. Graduate students involved in this research have many opportunities to present research results at consortium meetings and interact with industrial collaborators.

Reaction engineering at the molecular level: When reacting systems are considered at small length scales (small catalyst particles, inside living cells, etc.), the concentrations are small enough that the stochastic fluctuations cannot be neglected, and the classical standard methods of chemical reaction engineering are not applicable. The focus of our research is to develop new systems tools to support chemical reaction engineering at this molecular level.

Computational modeling: Our group has developed Octave, a freely available, high-level computer language for numerical simulation and analysis of chemical engineering models. We use Octave in order to define models quickly, compute and analyze solutions, estimate model parameters from data, and solve controller design problems.

 

Education

  • NATO Postdoctoral Fellow, Institute for System Dynamics and Process Control University of Stuttgart, Stuttgart, Germany 1985-1986
  • University of Wisconsin-Madison, Ph.D., Chemical Engineering, 1985
  • The University of Texas at Austin, B.S., Chemical Engineering, 1979

Research Interests

  • Chemical process control, nonlinear model predictive control.
  • State estimation and monitoring.
  • Chemical reaction engineering.
  • Virus modeling and stochastic chemical kinetics

Awards, Honors and Societies

  • Nordic Process Control Award (2013)
  • Chancellor\'s Distinguished Teaching Award, UW Madison (2013)
  • Elected Fellow, IEEE (2012)
  • WARF Named Professorship, Graduate School, UW Madison, W. Harmon Ray Professor of Chemical and Biological Engineering (2012)
  • \"Doctor technices honoris causa,\" Technical University of Denmark (2011)
  • Inaugural High Impact Paper Award, International Federation of Automatic Control (2011)
  • John R. Ragazzini (Education) Award, American Automatic Control Council (2011)
  • Harvey Spangler Award for Technology Enhanced Instruction, College of Engineering, UW Madison (2010)
  • Bayer Lecturer, Carnegie Mellon University (2010)
  • Elected Fellow, AIChE (2009)
  • Excellence in Process Development Research Award, Process Development Division, AIChE (2008)
  • Byron Bird Award for Excellence in a Research Publication, College of Engineering, UW Madison (2005)
  • Computing in Chemical Engineering Award, CAST Division, AIChE (1999)
  • Van Ness Lecturer, Rensselaer Polytechnic Institute (1999)
  • Paul A. Elfers Chair in Chemical and Biological Engineering, UW Madison (1995-2015)
  • Presidential Young Investigator, National Science Foundation (1989)

Publications

  • Srivastava, R., D. F. Anderson, and J. B. Rawlings. Comparison of finite difference based methods to obtain sensitivities of stochastic chemical kinetic models. J. Chem. Phys., 138:074110:1-10, 2013.
  • Rawlings, J. B. and L. Ji. Optimization-based state estimation: Current status and some new results. J. Proc. Cont., 22:1439-1444, 2012.
  • Pannocchia, G., S. J. Wright, and J. B. Rawlings. On the use of suboptimal solvers for efficient cooperative distributed linear MPC. In Negenborn, R. and P. Maestre, editors, Distributed MPC Made Easy. Springer, 2012.
  • Rawlings, J. B., D. Angeli, and C. Bates. Fundamentals of economic model predictive control. In IEEE Conference on Decision and Control (CDC), pages 3851-3861, Maui, HI, December 2012.
  • Pannocchia, G., D. Q. Mayne, and J. B. Rawlings. On the convergence of numerical solutions to the continuous-time constrained LQR problem. In The 21st International Symposium on Mathematical Programming (ISMP 2012), Berlin, Germany, August 2012.
  • Zagrobelny, M. A., L. Ji, and J. B. Rawlings. Quis custodiet ipsos custodes? In IFAC Conference on Nonlinear Model Predictive Control 2012, Noordwijkerhout, the Netherlands, August 2012.
  • Angeli, D., R. Amrit, and J. B. Rawlings. On average performance and stability of economic model predictive control. IEEE Trans. Auto. Cont., 57(7):1615-1626, 2012.
  • Subramanian, K., J. B. Rawlings, and C. T. Maravelias. Integration of control theory and scheduling methods for supply chain management. Proceedings of Foundations of Computer-AidedProcess Operations (FOCAPO) 2012 and Chemical Process Control (CPC) VIII, Savannah, GA, 2012.
  • Subramanian, K., C. T. Maravelias, and J. B. Rawlings. A state-space model for chemical production scheduling. Comput. Chem. Eng., 47:97-110, December 2012.
  • Amrit, R., J. B. Rawlings, and D. Angeli. Economic optimization using model predictive control with a terminal cost. Annual Rev. Control, 35:178-186, 2011.
  • Angeli, D., R. Amrit, and J. B. Rawlings. Enforcing convergence in nonlinear economic MPC. In IEEE Conference on Decision and Control (CDC), Orlando, FL, 2011.
  • Pannocchia, G., J. B. Rawlings, and S. J. Wright. Inherently robust suboptimal nonlinear MPC: theory and application. In Proceedings of 2011 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC), pages 3398-3403, Orlando, FL (USA), 2011.
  • Jùrgensen, J. B., J. K. Huusom, and J. B. Rawlings. Finite horizon MPC for systems in innovation form. In IEEE Conference on Decision and Control (CDC), pages 1896±1903, Orlando, FL, 2011.
  • Pannocchia, G., J. B. Rawlings, and S. J. Wright. Partial enumeration MPC: Robust stability results and application to an unstable CSTR. J. Proc. Cont., 21:1459-1466, 2011.
  • Rawlings, J. B. and D. Q. Mayne. Postface to: Model predictive control: Theory and design, 2011.  http://jbrwww.che.wisc.edu/home/jbraw/mpc/postface.pdf.
  • Pannocchia, G., J. B. Rawlings, and S. J. Wright. Conditions under which suboptimal nonlinear MPC is inherently robust. Sys. Cont. Let., 60:747-755, 2011.
  • Srivastava, R., E. L. Haseltine, E. A. Mastny, and J. B. Rawlings. The stochastic quasi-steady-state assumption: Reducing the model but not the noise. J. Chem. Phys., 34:154109:1-10, 2011.
  • Pannocchia, G., J. B. Rawlings, and S. J. Wright. Conditions under which suboptimal nonlinear MPC is inherently robust. In 18th IFAC World Congress, Milan, Italy, Sep. 2011.
  • Ferramosca, A., J. B. Rawlings, D. Limon, and E. F. Camacho. Cooperative distributed MPC for tracking. In 18th IFAC World Congress, Milan, Italy, Sep. 2011.
  • Stewart, B. T., S. J. Wright, and J. B. Rawlings. Cooperative distributed model predictive control for nonlinear systems. J. Proc. Cont., 21:698-704, 2011.
  • Lima, F. V. and J. B. Rawlings. Nonlinear stochastic modeling to improve state estimation in process monitoring and control. AIChE J., 57:996-1007, 2011.
  • Diehl, M., R. Amrit, and J. B. Rawlings. A Lyapunov function for economic optimizing model predictive control. IEEE Trans. Auto. Cont., 56(3):703-707, 2011.
  • Ferramosca, A., J. B. Rawlings, D. Limon, and E. F. Camacho. Economic MPC for a changing economic criterion. In IEEE Conference on Decision and Control (CDC), pages 6131-6136, Atlanta, GA, 2010.
  • Stewart, B. T., A. N. Venkat, J. B. Rawlings, S. J. Wright, and G. Pannocchia. Cooperative distributed model predictive control. Sys. Cont. Let., 59:460-469, 2010.
  • Pannocchia, G., J. B. Rawlings, and S. J. Wright. Partial enumeration MPC: Robust stability results and application to an unstable CSTR. In DYCOPS, Leuven, Belgium, June 2010.
  • Pannocchia, G., J. B. Rawlings, D. Q. Mayne, and W. Marquardt. On computing the solutions to the continuous time constrained linear quadratic regulator. IEEE Trans. Auto. Cont., 55(9):2192-2198,2010.
  • Angeli, D. and J. B. Rawlings. Receding horizon cost optimization and control for nonlinear plants.In 8th IFAC Symposium on Nonlinear Control Systems (NOLCOS), Bologna, Italy, September 2010.
  • Stewart, B. T., J. B. Rawlings, and S. J. Wright. Hierarchical cooperative distributed model pre-dictive control. In Proceedings of the American Control Conference, Baltimore, Maryland, June 2010.
  • Angeli, D., R. Amrit, and J. B. Rawlings. Receding horizon cost optimization for overly constrained nonlinear plants. In Proceedings of the Conference on Decision and Control, Shanghai, China, December 2009.
  • Hensel, S., J. B. Rawlings, and J. Yin. Stochastic kinetic modeling of vesicular stomatitis virus intracellular growth. Bull. Math. Biol., 71:1671-692, 2009. DOI 10.1007/s11538-009-9419-5.
  • Larsen, P. A. and J. B. Rawlings. The potential of current high-resolution imaging-based particle size distribution measurements for crystallization monitoring. AIChE J., 55(4):896-905, April 2009.
  • Larsen, P. A. and J. B. Rawlings. Assessing the reliability of particle number density measurements obtained by image analysis. Part. Part. Syst. Charact., 25(5-6):420-433, February 2009.
  • Rajamani, M. R., J. B. Rawlings, and S. J. Qin. Achieving state estimation equivalence for misas-signed disturbances in offset-free model predictive control. AIChE J., 55(2):396-407, February 2009.
  • Rajamani, M. R. and J. B. Rawlings. Estimation of the disturbance structure from data using semidefinite programming and optimal weighting. Automatica, 45:142-148, 2009.
  • Pannocchia, G., S. J. Wright, B. T. Stewart, and J. B. Rawlings. Efficient cooperative distributed MPC using partial enumeration. In ADCHEM 2009, International Symposium on Advanced Control of Chemical Processes, Istanbul, Turkey, July 12-15, 2009.
  • Pannocchia, G., J. B. Rawlings, D. Q. Mayne, and W. Marquardt. Computation of the infinite horizon continuous time constrained linear quadratic regulator. In ADCHEM 2009, International Symposium on Advanced Control of Chemical Processes, Istanbul, Turkey, July 12-15, 2009.
  • Würth, L., J. B. Rawlings, and W. Marquardt. Economic dynamic real-time optimization and non-linear model-predictive control on infnite horizons. In ADCHEM 2009, International Symposium on Advanced Control of Chemical Processes, Istanbul, Turkey, July 12-15, 2009.
  • Rawlings, J. B. and R. Amrit. Optimizing process economic performance using model predictive control. In Magni, L., D. M. Raimondo, and F. Allgöwer, editors, Nonlinear Model Predictive Control, volume 384 of Lecture Notes in Control and Information Sciences, pages 119-138, Berlin, 2009. Springer.
  • Venkat, A. N., I. A. Hiskens, J. B. Rawlings, and S. J. Wright. Distributed MPC strategies with application to power system automatic generation control. IEEE Ctl. Sys. Tech., 16(6):1192-1206, November 2008.
  • Haseltine, E. L., J. Yin, and J. B. Rawlings. Implications of decoupling the intracellular and ex-tracellular levels in multi-level simulations of virus infections. Biotech. Bioeng., 101(4):811-820, 2008.
  • Rawlings, J. B., D. Bonné, J. B. Jørgensen, A. N. Venkat, and S. B. Jørgensen. Unreachable setpoints in model predictive control. IEEE Trans. Auto. Cont., 53(9):2209-2215, October 2008.
  • Rawlings, J. B. and B. T. Stewart. Coordinating multiple optimization-based controllers: New opportunities and challenges. J. Proc. Cont., 18:839-845, 2008.
  • Haseltine, E. L., V. Lam, J. Yin, and J. B. Rawlings. Image-guided modeling of virus growth and spread. Bull. Math. Biol., 70:1730-1748, 2008.
  • Mastny, E. A., E. L. Haseltine, and J. B. Rawlings. Two classes of quasi-steady-state model reductions for stochastic kinetics. J. Chem. Phys., 127(9):094106, September 2007.
  • Venkat, A. N., J. B. Rawlings, and S. J. Wright. Distributed model predictive control of large-scale systems. In Assessment and Future Directions of Nonlinear Model Predictive Control, pages 591-605. Springer, 2007.
  • Middlebrooks, S. A. and J. B. Rawlings. Model predictive control of Si-Ge thin film chemical vapor deposition. IEEE Trans. Semicond. Manuf., 20(2):114-125, May 2007.
  • Pannocchia, G., J. B. Rawlings, and S. J. Wright. Fast, large-scale model predictive control by partial enumeration. Automatica, 43:852-860, 2007.
  • Larsen, P. A., J. B. Rawlings, and N. J. Ferrier. Model-based object recognition to measure crystal size and shape distributions from in situ video images. Chem. Eng. Sci., 62:1430-1441, 2007.
  • Wu, Z., M. R. Rajamani, J. B. Rawlings, and J. Stoustrup. Application of autocovariance least-squares method for model predictive control of hybrid ventilation in livestock stable. In Proceedings of the American Control Conference, New York, USA, July 11-13 2007.
  • Rajamani, M. R. and J. B. Rawlings. Improved state estimation using a combination of moving horizon estimator and particle filters. In Proceedings of the American Control Conference, New York, USA, July 11-13 2007.
  • Wu, Z., M. R. Rajamani, J. B. Rawlings, and J. Stoustrup. Model predictive control of thermal comfort and indoor air quality in livestock stable. In Proceedings of the European Control Conference, Kos, Greece, 2-5 July 2007.
  • Doshi, K., A. N. Venkat, R. D. Gudi, and J. B. Rawlings. An iterative, direct closed-loop identification method for model refinement: Application to interaction estimation. In DYCOPS, Cancun, Mexico, June 2007.

for a full list of publications please see extended website

Links

Octave: Octave modelling language

TWCCC

Books

Chemical Reactor Analysis and Design Fundamentals 2nd Edition

James B. Rawlings, Department of Chemical and Biological Engineering, University of Wisconsin - Madison, Wisconsin and John G. Ekerdt, Department of Chemical Engineering, The University of Texas - Austin, Texas

Copyright (C) 2012 Nob Hill Publishing, LLC 

Model Predictive Control: Theory and Design

James B. Rawlings, Department of Chemical and Biological Engineering, University of Wisconsin - Madison and David Q. Mayne, Department of Electrical and Electronic Engineering, Imperial College - London, England

Copyright (C) 2009 Nob Hill Publishing, LLC

Modeling and Analysis Principles for Chemical and Biological Engineers

Michael D. Graham, Department of Chemical and Biological Engineering, University of Wisconsin - Madison, Wisconsin and James B. Rawlings Department of Chemical and Biological Engineering, University of Wisconsin - Madison, Wisconsin

Copyright (C) 2013 Nob Hill Publishing, LLC

Comprehensive Course List

 

  • CBE 255 - Introduction to Chemical Process Modeling
  • CBE 430 - Chemical Kinetics and Reactor Design
  • CBE 470 - Process Dynamics and Control
  • CBE 599 - Special Problems
  • CBE 660 - Intermediate Problems in Chemical Engineering
  • CBE 731 - Computational Modelling of Reactive Systems
  • CBE 790 - Master\'s Research or Thesis
  • CBE 489 - Honors in Research
  • CBE 890 - Pre-Dissertator\'s Research
  • CBE 990 - Thesis-Research
  • CBE 699 - Advanced Independent Studies
  • CBE 599 - Special Problems
  • CBE 890 - Pre-Dissertator\'s Research
  • CBE 990 - Thesis-Research
  • CBE 790 - Master\'s Research or Thesis
  • CBE 699 - Advanced Independent Studies

Courses

Fall 2014-2015

  • CBE 890 - Pre-Dissertator\'s Research
  • CBE 790 - Master\'s Research or Thesis
  • CBE 699 - Advanced Independent Studies
  • CBE 599 - Special Problems
  • CBE 470 - Process Dynamics and Control
  • CBE 489 - Honors in Research
  • Profile Summary

    Chemical process monitoring and control: Chemical processes are inherently nonlinear and must operate at their design constraints to achieve optimal economic performance. We have developed methods of moving horizon estimation and model predictive control to monitor and control the operation of chemical processes. This research has provided new theoretical results as well as practical, implementable methods for industrial application.

    The Texas-Wisconsin-California Control Consortium (TWCCC), composed of leading chemical, microelectronic and pharmaceutical companies was created to promote these industrial collaborations. Graduate students involved in this research have many opportunities to present research results at consortium meetings and interact with industrial collaborators.

    Reaction engineering at the molecular level: When reacting systems are considered at small length scales (small catalyst particles, inside living cells, etc.), the concentrations are small enough that the stochastic fluctuations cannot be neglected, and the classical standard methods of chemical reaction engineering are not applicable. The focus of our research is to develop new systems tools to support chemical reaction engineering at this molecular level.

    Computational modeling: Our group has developed Octave, a freely available, high-level computer language for numerical simulation and analysis of chemical engineering models. We use Octave in order to define models quickly, compute and analyze solutions, estimate model parameters from data, and solve controller design problems.

     


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