A Survey of Industrial Model Predictive Control Technology
Control Engineering Practice 11 (2003) 733–764
A survey of industrial model predictive control technology
S. Joe Qina,*, Thomas A. Badgwellb,1 a Department of Chemical Engineering, The University of Texas at Austin, 1 Texas Lonhorns, C0400, Austin, TX 78712, USA b Aspen Technology, Inc., 1293 Eldridge Parkway, Houston, TX 77077, USA Received 8 November 2001; accepted 31 August 2002
Abstract This paper provides an overview of commercially available model predictive control (MPC) technology, both linear and nonlinear, based primarily on data provided by MPC vendors. A brief history of industrial MPC technology is presented first, followed by results of our vendor survey of MPC control and identification technology. A general MPC control algorithm is presented, and approaches taken by each vendor for the different aspects of the calculation are described. Identification technology is reviewed to determine similarities and differences between the various approaches. MPC applications performed by each vendor are summarized by application area. The final section presents a vision of the next generation of MPC technology, with an emphasis on potential business and research opportunities. r 2002 Elsevier Science Ltd. All rights reserved.
1. Introduction Model predictive control (MPC) refers to a class of computer control algorithms that utilize an explicit process model to predict the future response of a plant. At each control interval an MPC algorithm attempts to optimize future plant behavior by computing a sequence of future manipulated variable adjustments. The first input in the optimal sequence is then sent into the plant, and the entire calculation is repeated at subsequent control intervals. Originally developed to meet the specialized control needs of power plants and petroleum refineries, MPC technology can now be found in a wide variety of application areas including chemicals, food processing, automotive, and aerospace applications. Several recent
A survey of industrial model predictive control technology
S. Joe Qina,*, Thomas A. Badgwellb,1 a Department of Chemical Engineering, The University of Texas at Austin, 1 Texas Lonhorns, C0400, Austin, TX 78712, USA b Aspen Technology, Inc., 1293 Eldridge Parkway, Houston, TX 77077, USA Received 8 November 2001; accepted 31 August 2002
Abstract This paper provides an overview of commercially available model predictive control (MPC) technology, both linear and nonlinear, based primarily on data provided by MPC vendors. A brief history of industrial MPC technology is presented first, followed by results of our vendor survey of MPC control and identification technology. A general MPC control algorithm is presented, and approaches taken by each vendor for the different aspects of the calculation are described. Identification technology is reviewed to determine similarities and differences between the various approaches. MPC applications performed by each vendor are summarized by application area. The final section presents a vision of the next generation of MPC technology, with an emphasis on potential business and research opportunities. r 2002 Elsevier Science Ltd. All rights reserved.
1. Introduction Model predictive control (MPC) refers to a class of computer control algorithms that utilize an explicit process model to predict the future response of a plant. At each control interval an MPC algorithm attempts to optimize future plant behavior by computing a sequence of future manipulated variable adjustments. The first input in the optimal sequence is then sent into the plant, and the entire calculation is repeated at subsequent control intervals. Originally developed to meet the specialized control needs of power plants and petroleum refineries, MPC technology can now be found in a wide variety of application areas including chemicals, food processing, automotive, and aerospace applications. Several recent