Fast nonlinear model predictive control: Formulation and industrial process applications

Abstract With the widespread availability of model predictive control (MPC), nonlinear MPC provides a natural extension to include nonlinear models for trajectory tracking and dynamic optimization. NMPC can include first principle models developed for off-line dynamic studies as well as nonlinear data-driven models, but requires the application of efficient large-scale optimization strategies to avoid computational delays and to ensure stability, robustness and superior performance. This study presents the application of the recently developed advanced step NMPC (asNMPC) strategy. This approach solves the detailed optimization problem in background and applies a sensitivity-based update on-line. Two large-scale process case studies are considered: detailed distillation control and multi-stage operation for steam generation in a power plant. In both cases, efficient and robust controller performance is achieved with nonlinear dynamic optimization.

[1]  Victor M. Zavala,et al.  Optimization-based strategies for the operation of low-density polyethylene tubular reactors: nonlinear model predictive control , 2009, Comput. Chem. Eng..

[2]  L. Biegler,et al.  A fast moving horizon estimation algorithm based on nonlinear programming sensitivity , 2008 .

[3]  Zoltan K. Nagy,et al.  Real-Time Implementation of Nonlinear Model Predictive Control of Batch Processes in an Industrial Framework , 2007 .

[4]  James B. Rawlings,et al.  Economic Dynamic Real-Time Optimization and Nonlinear Model-Predictive Control on Infinite Horizons , 2009 .

[5]  Toshiyuki Ohtsuka,et al.  A continuation/GMRES method for fast computation of nonlinear receding horizon control , 2004, Autom..

[6]  L. Biegler,et al.  A Moving Horizon Estimator for processes with multi-rate measurements: A Nonlinear Programming sensitivity approach , 2012 .

[7]  Victor M. Zavala,et al.  The advanced-step NMPC controller: Optimality, stability and robustness , 2009, Autom..

[8]  Rüdiger Franke,et al.  Integration of Advanced Model Based Control with Industrial IT , 2007 .

[9]  S. Timoshenko,et al.  Theory of elasticity , 1975 .

[10]  L. Biegler,et al.  Fast Offset-Free Nonlinear Model Predictive Control Based on Moving Horizon Estimation , 2010 .

[11]  Riccardo Scattolini,et al.  Robustness and robust design of MPC for nonlinear systems , 2007 .

[12]  R. Donald Bartusiak,et al.  NLMPC: A Platform for Optimal Control of Feed- or Product-Flexible Manufacturing , 2007 .

[13]  L. Biegler,et al.  Large‐scale DAE optimization using a simultaneous NLP formulation , 1998 .

[14]  Lorenz T. Biegler,et al.  Optimal sensitivity based on IPOPT , 2012, Math. Program. Comput..

[15]  E. Camacho,et al.  Nonlinear Model Predictive Control: An Introductory Review , 2007 .

[16]  S. Joe Qin,et al.  An Overview of Nonlinear Model Predictive Control Applications , 2000 .

[17]  Sebastian Engell.  Feedback control for optimal process operation , 2007 .

[18]  Moritz Diehl,et al.  A Lyapunov Function for Economic Optimizing Model Predictive Control , 2011, IEEE Transactions on Automatic Control.

[19]  M. Branicky.  Stability of switched and hybrid systems , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.

[20]  Victor M. Zavala,et al.  On-line economic optimization of energy systems using weather forecast information. , 2009 .

[21]  L. Biegler,et al.  Robust stability of economically oriented infinite horizon NMPC that include cyclic processes , 2012 .

[22]  Martin Grötschel,et al.  Online optimization of large scale systems , 2001 .

[23]  M. Diehl,et al.  Real-time optimization and nonlinear model predictive control of processes governed by differential-algebraic equations , 2000 .

[24]  Wolfgang Marquardt,et al.  Integration of Economical Optimization and Control for Intentionally Transient Process Operation , 2007 .

[25]  William Prager,et al.  Theory of Thermal Stresses , 1960 .

[26]  Tor Arne Johansen,et al.  Lyapunov-based optimizing control of nonlinear blending processes , 2005, IEEE Transactions on Control Systems Technology.

[27]  Frank Allgöwer,et al.  Computational Delay in Nonlinear Model Predictive Control , 2004 .

[28]  Rodrigo Lopez-Negrete De La Fuente,et al.  Optimal Start-Up and Product Transition Policies of a Reactive Distillation Column , 2007 .

[29]  Darci Odloak,et al.  Integrating real-time optimization into the model predictive controller of the FCC system , 2002 .

[30]  L. Biegler,et al.  A FAST COMPUTATIONAL FRAMEWORK FOR LARGE-SCALE MOVING HORIZON ESTIMATION , 2007 .

[31]  Francesco Casella,et al.  Modelling of thermo-hydraulic power generation processes using Modelica , 2006 .

[32]  Fernando Javier D'Amato.  Industrial application of a model predictive control solution for power plant startups , 2006, 2006 IEEE Conference on Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006 IEEE International Symposium on Intelligent Control.

[33]  Moritz Diehl,et al.  Real-Time Optimization for Large Scale Nonlinear Processes , 2001 .

[34]  Victor M. Zavala,et al.  Fast implementations and rigorous models: Can both be accommodated in NMPC? , 2008 .

[35]  Lorenz T. Biegler,et al.  Lyapunov stability of economically oriented NMPC for cyclic processes , 2011 .

[36]  Lorenz T. Biegler,et al.  Robust stability of nonlinear model predictive control based on extended Kalman filter , 2012 .

[37]  Anil V. Rao,et al.  Practical Methods for Optimal Control Using Nonlinear Programming , 1987 .

[38]  Lorenz T. Biegler,et al.  On-line implementation of nonlinear MPC: an experimental case study , 2000 .

[39]  Lorenz T. Biegler,et al.  On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming , 2006, Math. Program..

[40]  Victor M. Zavala,et al.  Advanced step nonlinear model predictive control for air separation units , 2009 .

[41]  Anthony V. Fiacco,et al.  Introduction to Sensitivity and Stability Analysis in Nonlinear Programming , 2012 .