Laboratory demonstration for model predictive multivariable control with a coupled drive system (original) (raw)

Design of the Model Predictive Control Education and Application Interface

Journal of Software Engineering and Applications, 2012

In this study, an education and application interface was designed for model predictive control (MPC). For this design, MPC Toolbox and MATLAB GUI in the MATLAB software were used. Developed interface includes model predictive control methods, such as single-input single-output, multi-input multi-output, constrained or unconstrained systems. The interface, developed for education of model predictive control methods, was tested in class by the students attending to the Process Dynamic and Control course.

A multicontroller structure for teaching and designing predictive control strategies

The paper deals with the unification of the existing linear control algorithms in order to facilitate their transfer to the engineering students and to industry's engineers. The resulting control algorithm is the Global Predictive Control (GlobPC), which is now taught at the graduate and continuing education levels. GlobPC is based on an internal model framework where three independent control criteria are minimized: one for tracking, one for regulation and one for feedforward. This structure allows to obtain desired tracking, regulation and feedforward behaviors in an optimal way while keeping them perfectly separated. It also cleanly separates the deterministic and stochastic predictions of the process model output.

Model predictive control design for industrial applications

2006

In many industrial processes, the regulatory level based on PID controllers is able to maintain the process variables about the given set point values. However, economic reasons and operational constraints make it necessary to optimise plant operations to achieve as much operational efficiency as possible. This paper presents two solutions to solve the optimisation problem: either the optimal predictive controller replaces the regulatory level PID controllers, or the predictive controller is implemented at the supervisory level. A comparison with popular multi-variable PID tuning methods demonstrates the superior performances of predictive control. The example is developed using a graphical predictive control software that uses the state of the art identification, control design optimisation and simulation LabVIEW toolkits for design verification and deployment. The control solutions can be easily imported to a real time platform for industrial applications.

Experimental application of predictive controllers

Journal of Control Science …, 2012

Model predictive control (MPC) has been used successfully in industry. The basic characteristic of these algorithms is the formulation of an optimization problem in order to compute the sequence of control moves that minimize a performance function on the time horizon with the best information available at each instant, taking into account operation and plant model constraints. The classical algorithms Infinite Horizon Model Predictive Control (IHMPC) and Model Predictive Control with Reference System (RSMPC) were used for the experimental application in the multivariable control of the pilot plant (level and pH). The simulations and experimental results indicate the applicability and limitation of the control technique.

WANG, L. Model Predictive Control System Design and Implementation Using MATLAB. 2009.

Model Predictive Control System Design and Implementation Using MATLAB, 2009

The use of registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant laws and regulations and therefore free for general use. The publisher makes no representation, express or implied, with regard to the accuracy of the information contained in this book and cannot accept any legal responsibility or liability for any errors or omissions that may be made.

An Overview Of Industrial Model Predictive Control Technology

1997

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

A survey of industrial model predictive control technology

Control Engineering Practice, 2003

This paper provides an overview of commercially available Model Predictive Control (MPC) technology, based primarily on data provided by MPC vendors. A brief history of industrial MPC technology is presented rst, followed by results of our vendor survey of MPC control and identi cation technology. A general MPC control algorithm is presented, and approaches taken by each vendor for the di erent aspects of the calculation are described. Identi cation technology is then reviewed to determine similarities and di erences between the various approaches. MPC applications performed by each vendor are summarized by application area. The nal section presents a vision of the next generation of MPC technology, with an emphasis on potential business and research opportunities.

Evolution of an Industrial Nonlinear Model Predictive Controller

2001

Motivated by a specific manufacturing problem in 1990, Exxon Chemical Company embarked on the development of a nonlinear multivariable model-based predictive controller. The controller's evolution has included collaboration among academic researchers, engineers from industry, and process control software vendors. The resulting control algorithm was patented by Exxon Chemical Company and commercialized by Dynamic Optimization Technology Products, Inc. At the same time, several other academic interactions produced results supporting the implemention of these controllers in our manufacturing facilities. This paper chronicles the evolution of the controller development, and presents the details of the control algorithm. The control algorithm features are discussed, and where applicable, compared to other model predictive control (MPC) algorithms. Finally, two industrial examples are presented to illustrate the methodology.