A nonlinear regression model-based predictive control algorithm.

This paper presents a unique approach for designing a nonlinear regression model-based predictive controller (NRPC) for single-input-single-output (SISO) and multi-input-multi-output (MIMO) processes that are common in industrial applications. The innovation of this strategy is that the controller structure allows nonlinear open-loop modeling to be conducted while closed-loop control is executed every sampling instant. Consequently, the system matrix is regenerated every sampling instant using a continuous function providing a more accurate prediction of the plant. Computer simulations are carried out on nonlinear plants, demonstrating that the new approach is easily implemented and provides tight control. Also, the proposed algorithm is implemented on two real time SISO applications; a DC motor, a plastic injection molding machine and a nonlinear MIMO thermal system comprising three temperature zones to be controlled with interacting effects. The experimental closed-loop responses of the proposed algorithm were compared to a multi-model dynamic matrix controller (MPC) with improved results for various set point trajectories. Good disturbance rejection was attained, resulting in improved tracking of multi-set point profiles in comparison to multi-model MPC.

[1]  Julian Morris,et al.  Dynamic neural networks in non-linear predictive control (an industrial application) , 1996 .

[2]  S. Joe Qin,et al.  A survey of industrial model predictive control technology , 2003 .

[3]  R. Dubay,et al.  An investigation on the application of predictive control for controlling screw position and velocity on an injection molding machine , 2007 .

[4]  SHAUN MCGINNITY,et al.  Nonlinear state estimation using fuzzy local linear models , 1997, Int. J. Syst. Sci..

[5]  Astrom.  Computer Controlled Systems , 1990 .

[6]  Phillip D. Schnelle,et al.  Model predictive control of an industrial packed bed reactor using neural networks , 1995 .

[7]  Tor Arne Johansen,et al.  Non-linear predictive control using local models-applied to a batch fermentation process , 1995 .

[8]  Jay H. Lee,et al.  Model predictive control: past, present and future , 1999 .

[9]  Michael A. Henson,et al.  Nonlinear model predictive control: current status and future directions , 1998 .

[10]  Manfred Morari,et al.  Model predictive control: Theory and practice - A survey , 1989, Autom..

[11]  G. Gattu,et al.  Nonlinear Quadratic Dynamic Matrix Control with State Estimation , 1992 .

[12]  R. Dubay.  Self-optimizing MPC of melt temperature in injection moulding. , 2002, ISA transactions.

[13]  Bjarne A. Foss,et al.  Interpolating optimizing process control , 1997 .

[14]  R. Gorez,et al.  A fuzzy clustering method for the identification of fuzzy models for dynamic systems , 1994, Proceedings of 1994 9th IEEE International Symposium on Intelligent Control.

[15]  C. R. Cutler,et al.  Dynamic matrix control¿A computer control algorithm , 1979 .

[16]  A. C. Hoffmann,et al.  AIChE Symposium Series , 1999 .

[17]  B. Bequette.  Nonlinear control of chemical processes: a review , 1991 .

[18]  T. Johansen,et al.  Constructing NARMAX models using ARMAX models , 1993 .

[19]  Friedrich Johannaber.  Injection Molding Machines , 1994 .

[20]  Douglas J. Cooper,et al.  A novel tuning strategy for multivariable model predictive control , 1997 .