Adaptive Generalized Predictive Control Based on Just-in-Time Learning in Latent Space

An adaptive generalized predictive control approach based on just-in-time learning(JITL) in latent space is proposed to deal with the problems associating with multivariate, nonlinearity and time-varying characteristics in industrial process systems. To begin with, the latent variable space is constructed by the partial least squares algorithm, thus the complicated multivariable control problem can be decomposed into univariate ones, subsequently the local model of each SISO subsystem can be established online by JITL at every sampling instant in latent space, where the generalized predictive control is implemented to these subsystems. To improve the real-time performance of modeling, the similarity measure will be utilized to determine whether or not to update the current local model at each sampling instant. The proposed approach not only can obtain the satisfactory control results for nonlinear and multivariate system, but also can solve the unstable problem caused by model mismatch. The proposed adaptive predictive control approach is applied to a pH neutralization process. Simulation studies are presented to verify the advantage of the proposed approach.

[1]  Rafiqul Gani,et al.  Multivariable Adaptive Lyapunov Fuzzy Controller for pH Neutralisation Process , 2015 .

[2]  David W. Clarke,et al.  Generalized predictive control - Part I. The basic algorithm , 1987, Autom..

[3]  Ping Wang,et al.  Adaptive Nonlinear Model Predictive Control Using an On-line Support Vector Regression Updating Strategy , 2014 .

[4]  Abdelkrim Boukabou,et al.  Predictive control and synchronization of chaotic and hyperchaotic systems based on a T-S fuzzy model , 2014, Math. Comput. Simul..

[5]  Dae Sung Lee,et al.  Nonlinear dynamic partial least squares modeling of a full-scale biological wastewater treatment plant , 2006 .

[6]  Raman K. Mehra,et al.  Model algorithmic control (MAC); basic theoretical properties , 1982, Autom..

[7]  Xiao Wu,et al.  Fuzzy modeling and predictive control of superheater steam temperature for power plant. , 2015, ISA transactions.

[8]  E. Camacho,et al.  Generalized Predictive Control , 2007 .

[9]  Min-Sen Chiu,et al.  Adaptive generalized predictive control based on JITL technique , 2009 .

[10]  Li Shi,et al.  Nonlinear model predictive control of an intensified continuous reactor using neural networks , 2015, 2015 34th Chinese Control Conference (CCC).

[11]  Li Zhao,et al.  A model predictive control approach with relevant identification in dynamic PLS framework , 2014 .

[12]  Han Yu.  A just-in-time learning approach for sewage treatment process monitoring with deterministic disturbances , 2015, IECON 2015 - 41st Annual Conference of the IEEE Industrial Electronics Society.

[13]  Min-Sen Chiu,et al.  Partitioned model-based IMC design using JITL modeling technique , 2007 .

[14]  Jun Liang,et al.  Multi-loop nonlinear internal model controller design under nonlinear dynamic PLS framework using ARX-neural network model , 2012 .

[15]  Anna Vasičkaninová,et al.  Control of a heat exchanger using neural network predictive controller combined with auxiliary fuzzy controller , 2015 .

[16]  Mohd Azlan Hussain,et al.  Multivariable Adaptive Lyapunov Fuzzy Controller for pH Neutralisation Process , 2015 .

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

[18]  Min-Sen Chiu,et al.  A New Strategy of Locality Enhancement for Justin-Time Learning Method , 2012 .

[19]  Xin Yang,et al.  Adaptive decentralized PID controllers design using JITL modeling methodology , 2012 .

[20]  S. Wold,et al.  PLS-regression: a basic tool of chemometrics , 2001 .

[21]  Karim Djouani,et al.  Fuzzy dynamic modelling and predictive control of a coagulation chemical dosing unit for water treatment plants , 2014 .

[22]  G. Lim,et al.  A nonlinear partial least squares algorithm using quadratic fuzzy inference system , 2009 .

[23]  M. Chiu,et al.  A new data-based methodology for nonlinear process modeling , 2004 .

[24]  T. Sato,et al.  Design of a GPC-based PID controller for controlling a weigh feeder , 2010 .