Computationally effective optimization methods for complex process control and scheduling problems

[1]  David Q. Mayne,et al.  Robust model predictive control using tubes , 2004, Autom..

[2]  M. V. F. Pereira,et al.  A New Benders Decomposition Approach to Solve Power Transmission Network Design Problems , 2001, IEEE Power Engineering Review.

[3]  Thomas L. Magnanti,et al.  Accelerating Benders Decomposition: Algorithmic Enhancement and Model Selection Criteria , 1981, Oper. Res..

[4]  A. Rantzer A dual to Lyapunov's stability theorem , 2001 .

[5]  Andrew G. Barto,et al.  Reinforcement learning , 1998 .

[6]  Berç Rustem,et al.  An interior point algorithm for continuous minimax: implementation and computation , 2008, Optim. Methods Softw..

[7]  C. Floudas,et al.  Effective Continuous-Time Formulation for Short-Term Scheduling. 1. Multipurpose Batch Processes , 1998 .

[8]  Michel Gendreau,et al.  Accelerating Benders Decomposition by Local Branching , 2009, INFORMS J. Comput..

[9]  Michael Nikolaou,et al.  Chance‐constrained model predictive control , 1999 .

[10]  Golbon Zakeri,et al.  Inexact Cuts in Benders Decomposition , 1999, SIAM J. Optim..

[11]  Hans Bock,et al.  FINITE HORIZON OPTIMIZING CONTROL OF ADVANCED SMB CHROMATOGRAPHIC PROCESSES , 2005 .

[12]  Shimon Whiteson,et al.  Evolutionary Function Approximation for Reinforcement Learning , 2006, J. Mach. Learn. Res..

[13]  J. F. Benders Partitioning procedures for solving mixed-variables programming problems , 1962 .

[14]  Eduardo F. Camacho,et al.  Min–Max MPC based on an upper bound of the worst case cost with guaranteed stability. Application to a pilot plant , 2011 .

[15]  Weiping Li,et al.  Applied Nonlinear Control , 1991 .

[16]  Panagiotis D. Christofides,et al.  Stabilization of nonlinear systems with state and control constraints using Lyapunov-based predictive control , 2005, Proceedings of the 2005, American Control Conference, 2005..

[17]  M. Dellnitz,et al.  An adaptive method for the approximation of the generalized cell mapping , 1997 .

[18]  Thomas E. Marlin,et al.  On-line statistical results analysis in real-time operations optimization , 1998 .

[19]  Gérard Cornuéjols,et al.  A convex-analysis perspective on disjunctive cuts , 2006, Math. Program..

[20]  R. Cheng Decomposition and coordination of large-scale operations optimization , 2007 .

[21]  Basil Kouvaritakis,et al.  Stochastic MPC with inequality stability constraints , 2006, Autom..

[22]  Peter I. Frazier,et al.  Knowledge-Gradient Methods for Statistical Learning , 2009 .

[23]  J. B. Riggs,et al.  On-line optimization of the Tennessee Eastman challenge problem , 2000 .

[24]  Warren B. Powell,et al.  Handbook of Learning and Approximate Dynamic Programming , 2006, IEEE Transactions on Automatic Control.

[25]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[26]  Xiaoming Hu,et al.  Constructive stabilization for quadratic input nonlinear systems , 2008, Autom..

[27]  Ali Jadbabaie,et al.  Control of a thrust‐vectored flying wing: a receding horizon—LPV approach , 2002 .

[28]  Darci Odloak,et al.  Industrial implementation of a real-time optimization strategy for maximizing production of LPG in a FCC unit , 2000 .

[29]  Jong Min Lee,et al.  An approximate dynamic programming based approach to dual adaptive control , 2009 .

[30]  Zhenghong Yu,et al.  Worst-case formulations of model predictive control for systems with bounded parameters , 1997, Autom..

[31]  G. Nicolao,et al.  Stabilizing receding-horizon control of nonlinear time-varying systems , 1998, IEEE Trans. Autom. Control..

[32]  Jay H. Lee,et al.  Choice of approximator and design of penalty function for an approximate dynamic programming based control approach , 2006 .

[33]  Weehong Tan,et al.  Nonlinear Control Analysis and Synthesis using Sum-of-Squares Programming , 2006 .

[34]  Wolfgang Marquardt,et al.  Neighboring-extremal updates for nonlinear model-predictive control and dynamic real-time optimization , 2009 .

[35]  Eduardo F. Camacho,et al.  Min-Max MPC using a tractable QP Problem , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[36]  Thomas F. Edgar,et al.  Process Dynamics and Control , 1989 .

[37]  Jesse Hoey,et al.  An analytic solution to discrete Bayesian reinforcement learning , 2006, ICML.

[38]  Gongsheng Huang,et al.  Realization of robust nonlinear model predictive control by offline optimisation , 2008 .

[39]  Steven J. Bradtke,et al.  Linear Least-Squares algorithms for temporal difference learning , 2004, Machine Learning.

[40]  J. Fraser Forbes,et al.  Real-time optimization under parametric uncertainty: a probability constrained approach , 2002 .

[41]  L. Magni,et al.  Stability margins of nonlinear receding-horizon control via inverse optimality , 1997 .

[42]  John F. Forbes,et al.  Model-based real-time optimization of automotive gasoline blending operations , 2000 .

[43]  Yu Yang,et al.  Probabilistic modeling and dynamic optimization for performance improvement and risk management of plant-wide operation , 2010, Comput. Chem. Eng..

[44]  John W. Tukey,et al.  Exploratory Data Analysis. , 1979 .

[45]  Andrew R. Teel,et al.  Examples when nonlinear model predictive control is nonrobust , 2004, Autom..

[46]  Manfred Morari,et al.  Robust constrained model predictive control using linear matrix inequalities , 1994, Proceedings of 1994 American Control Conference - ACC '94.

[47]  John R. Birge,et al.  Introduction to Stochastic Programming , 1997 .

[48]  Mayuresh V. Kothare,et al.  Efficient robust constrained model predictive control with a time varying terminal constraint set , 2003, Syst. Control. Lett..

[49]  Z. Artstein Stabilization with relaxed controls , 1983 .

[50]  C. Watkins Learning from delayed rewards , 1989 .

[51]  Marianthi G. Ierapetritou,et al.  Accelerating Benders method using covering cut bundle generation , 2010, Int. Trans. Oper. Res..

[52]  Miroslav Krstic,et al.  Nonlinear and adaptive control de-sign , 1995 .

[53]  Fritz Wysotzki,et al.  Risk-Sensitive Reinforcement Learning Applied to Control under Constraints , 2005, J. Artif. Intell. Res..

[54]  M. Laughton,et al.  Large-scale mixed integer programming: Benders-type heuristics , 1984 .

[55]  George B. Dantzig,et al.  Decomposition Principle for Linear Programs , 1960 .

[56]  A. Garulli,et al.  Positive Polynomials in Control , 2005 .

[57]  JayHyung Lee,et al.  Nonlinear model predictive control of the Tennessee Eastman challenge process , 1995 .

[58]  P.V. Kokotovic,et al.  The joy of feedback: nonlinear and adaptive , 1992, IEEE Control Systems.

[59]  Frank Allgöwer,et al.  A quasi-infinite horizon nonlinear model predictive control scheme with guaranteed stability , 1997, 1997 European Control Conference (ECC).

[60]  Vladimir Havlena,et al.  A DISTRIBUTED AUTOMATION FRAMEWORK FOR PLANT-WIDE CONTROL, OPTIMISATION, SCHEDULING AND PLANNING , 2005 .

[61]  Richard S. Sutton,et al.  Reinforcement Learning , 1992, Handbook of Machine Learning.

[62]  D. Bertsekas,et al.  Adaptive aggregation methods for infinite horizon dynamic programming , 1989 .

[63]  R. Bellman Dynamic programming. , 1957, Science.

[64]  John E. Beasley,et al.  Improving benders decomposition using a genetic algorithm , 2009, Eur. J. Oper. Res..

[65]  Martin L. Puterman,et al.  Markov Decision Processes: Discrete Stochastic Dynamic Programming , 1994 .

[66]  John N. Tsitsiklis,et al.  Neuro-Dynamic Programming , 1996, Encyclopedia of Machine Learning.

[67]  Vladimir A. Yakubovich,et al.  Linear Matrix Inequalities in System and Control Theory (S. Boyd, L. E. Ghaoui, E. Feron, and V. Balakrishnan) , 1995, SIAM Rev..

[68]  Petar V. Kokotovic,et al.  Design of 'softer' robust nonlinear control laws , 1993, Autom..

[69]  Jay H. Lee,et al.  An introduction to a dynamic plant-wide optimization strategy for an integrated plant , 2004, Comput. Chem. Eng..

[70]  Csaba Szepesvári,et al.  Exploration-exploitation tradeoff using variance estimates in multi-armed bandits , 2009, Theor. Comput. Sci..

[71]  John R. Birge,et al.  Decomposition and Partitioning Methods for Multistage Stochastic Linear Programs , 1985, Oper. Res..

[72]  Robert P. W. Duin,et al.  Support Vector Data Description , 2004, Machine Learning.

[73]  L. Grüne Asymptotic Behavior of Dynamical and Control Systems under Perturbation and Discretization , 2002 .

[74]  Martha A. Gallivan,et al.  Optimization of a thin film deposition process using a dynamic model extracted from molecular simulations , 2008, Autom..

[75]  N. El‐Farra,et al.  Bounded robust control of constrained multivariable nonlinear processes , 2003 .

[76]  Jay H. Lee,et al.  Approximate dynamic programming based approach to process control and scheduling , 2006, Comput. Chem. Eng..

[77]  Pablo A. Parrilo,et al.  Nonlinear control synthesis by convex optimization , 2004, IEEE Transactions on Automatic Control.

[78]  Peter Kall,et al.  Stochastic Programming , 1995 .

[79]  Yuandan Lin,et al.  A universal formula for stabilization with bounded controls , 1991 .

[80]  Yishay Mansour,et al.  Policy Gradient Methods for Reinforcement Learning with Function Approximation , 1999, NIPS.

[81]  C. R. Cutler,et al.  Real time optimization with multivariable control is required to maximize profits , 1983 .

[82]  Dimitri P. Bertsekas,et al.  Dynamic Programming and Optimal Control, Two Volume Set , 1995 .

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

[84]  E. Gilbert,et al.  Optimal infinite-horizon feedback laws for a general class of constrained discrete-time systems: Stability and moving-horizon approximations , 1988 .

[85]  Eduardo F. Camacho,et al.  Input to state stability of min-max MPC controllers for nonlinear systems with bounded uncertainties , 2006, Autom..

[86]  K. C. Kiwiel,et al.  A direct method of linearization for continuous minimax problems , 1987 .

[87]  Dominique Bonvin,et al.  Dynamic optimization in the presence of uncertainty: From off-line nominal solution to measurement-based implementation , 2007 .

[88]  W. P. M. H. Heemels,et al.  Predictive control of hybrid systems: Input-to-state stability results for sub-optimal solutions , 2009, Autom..

[89]  Peiling Wu,et al.  A demand-shifting feasibility algorithm for Benders decomposition , 2003, Eur. J. Oper. Res..

[90]  Richard S. Sutton,et al.  Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming , 1990, ML.

[91]  Michail G. Lagoudakis,et al.  Least-Squares Policy Iteration , 2003, J. Mach. Learn. Res..

[92]  Warren B. Powell,et al.  Approximate Dynamic Programming - Solving the Curses of Dimensionality , 2007 .

[93]  Matthew Galati,et al.  Decomposition methods for integer linear programming , 2010 .

[94]  Panagiotis D. Christofides,et al.  Predictive control of switched nonlinear systems with scheduled mode transitions , 2005, IEEE Transactions on Automatic Control.

[95]  Steven I. Marcus,et al.  Simulation-based Algorithms for Markov Decision Processes/ Hyeong Soo Chang ... [et al.] , 2013 .

[96]  Alberto Bemporad,et al.  The explicit linear quadratic regulator for constrained systems , 2003, Autom..

[97]  Mahmoud Reza Pishvaie,et al.  A new approach to real time optimization of the Tennessee Eastman challenge problem , 2005 .

[98]  Tor Arne Johansen,et al.  Computation of Lyapunov functions for smooth nonlinear systems using convex optimization , 2000, Autom..

[99]  E. F. Vogel,et al.  A plant-wide industrial process control problem , 1993 .

[100]  Werner Dinkelbach On Nonlinear Fractional Programming , 1967 .

[101]  Victor M. Zavala,et al.  Large-scale nonlinear programming using IPOPT: An integrating framework for enterprise-wide dynamic optimization , 2009, Comput. Chem. Eng..

[102]  Ufuk Topcu,et al.  Local stability analysis using simulations and sum-of-squares programming , 2008, Autom..

[103]  M. Wendt,et al.  Robust model predictive control under chance constraints , 2000 .

[104]  R. Suárez,et al.  Suboptimal control of constrained nonlinear systems via receding horizon constrained control Lyapunov functions , 2003 .

[105]  Lorenz T. Biegler,et al.  Dynamic optimization of the Tennessee Eastman process using the OptControlCentre , 2003, Comput. Chem. Eng..

[106]  Francis J. Doyle,et al.  Real-time optimization of the pulp mill benchmark problem , 2008, Comput. Chem. Eng..

[107]  Niels Kjølstad Poulsen,et al.  Constrained predictive control and its application to a coupled-tanks apparatus , 2001 .

[108]  Andrew G. Barto,et al.  Learning to Act Using Real-Time Dynamic Programming , 1995, Artif. Intell..

[109]  Michael A. Saunders,et al.  USER’S GUIDE FOR SNOPT 5.3: A FORTRAN PACKAGE FOR LARGE-SCALE NONLINEAR PROGRAMMING , 2002 .

[110]  L. Biegler,et al.  Advances in simultaneous strategies for dynamic process optimization , 2002 .

[111]  R. Wets,et al.  L-SHAPED LINEAR PROGRAMS WITH APPLICATIONS TO OPTIMAL CONTROL AND STOCHASTIC PROGRAMMING. , 1969 .

[112]  Eduardo Sontag A universal construction of Artstein's theorem on nonlinear stabilization , 1989 .

[113]  J. Fraser Forbes,et al.  Dantzig-Wolfe decomposition and plant-wide MPC coordination , 2008, Comput. Chem. Eng..

[114]  Naif B. Almutairi,et al.  Sliding mode control of coupled tanks , 2006 .

[115]  Matteo Fischetti,et al.  A note on the selection of Benders’ cuts , 2010, Math. Program..

[116]  Kao-Shing Hwang,et al.  Reinforcement learning to adaptive control of nonlinear systems , 2003, IEEE Trans. Syst. Man Cybern. Part B.

[117]  I. Grossmann,et al.  Convergence properties of generalized benders decomposition , 1991 .

[118]  Hussam Nosair,et al.  Min–max control using parametric approximate dynamic programming , 2010 .

[119]  Marianthi G. Ierapetritou,et al.  Improving benders decomposition using maximum feasible subsystem (MFS) cut generation strategy , 2010, Comput. Chem. Eng..

[120]  Jie Yu,et al.  Unconstrained receding-horizon control of nonlinear systems , 2001, IEEE Trans. Autom. Control..

[121]  A. M. Geoffrion Generalized Benders decomposition , 1972 .