Notice of Violation of IEEE Publication Principles: Internal model control based on locally linear model tree (LOLIMOT) model with application to a PH neutralization process

Abstract

Notice of Violation of IEEE Publication Principles<br><br>"Internal model control based on locally linear model tree (LOLIMOT) model with application to a PH neutral process"<br>by M. Jalili-Kharaajoo, A. Rahmati and F. Rashidi,<br>in proceedings of the 2003 IEEE Internationa Conference on Systems, Man, and Cybernetics, vol 4, pp. 3051-3-55.<br><br>After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.<br><br>This paper contains portions of original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.<br><br>"Nonlinear Internal Model Control for MISO Systems Based on Local Linear Neuro-Fuzzy Models"<br> by A. Fink, O. Nelles and R. Isermann,<br>in 15th IFAC World Congress, Barcelona, Spain, 2002.<br><br> <br/> The internal model control (IMC) scheme has been widely applied in the field of process control. So far, IMC has been mainly applied to linear processes. This paper discusses the extension of the IMC scheme to nonlinear processes based on local linear models where the properties of linear design procedures can be exploited. The IMC scheme results in controllers that are comparable to conventional multi layer perceptron (MLP) networks. In practice, the tuning of conventional MLP based controllers can be very time-consuming whereas the IMC design procedure is very simple and reliable. The design effort of the IMC based on locally linear model tree (LOLIMOT) algorithm are discussed and the control results are compared by application to nonlinear control of an industrial-scale PH neutralization process. Simulation studies of a PH neutralization process confirm the excellent nonlinear modeling properties of the proposed locally linear network and illustrate the potential for set point tracking and disturbance rejection within an IMC framework.