Nonlinear Offset-Free Model Predictive Control based on Dynamic PLS Framework

Qiang Zhao,Huapeng Yu,Xin Jin,Shan Lu

doi:10.3390/pr9101784

Qiang Zhao, Huapeng Yu + Show 2 more

Open Access

https://doi.org/10.3390/pr9101784

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Journal: Processes	Publication Date: Oct 7, 2021
Citations: 2	License type: CC BY 4.0

Affiliation: Liaoning Shihua University, Shenzhen Polytechnic

Abstract

A nonlinear offset-free model predictive control based on a dynamic partial least square (PLS) framework is proposed in this paper. A multi-output multi-input system is projected into latent variable space by a PLS outer model. For each latent variable model, the T–S fuzzy model is used to describe the nonlinear characteristics of the system; while the state-space model is used in T–S fuzzy model consequent parameters to describe the dynamic characteristics. A disturbance model is introduced in the state-space model. For model state variables, a state observer is used to compensate for the mismatch of the model. The case study results for the pH neutralization process show that the MPC controller based on this method can guarantee the tracking performance of the nonlinear system without static error.

Highlights

Nonlinearity is a common characteristic in industrial processes
Escano et al [13] proposed a complexity reduction Takagi–Sugeno fuzzy model that is obtained by finding a function basis via a functional principal component analysis and used for model predictive control (MPC)
In the nonlinear iterative partial least square (NIPALS) method, X and Y are projected into latent variable space as a sum of a series of vectors, as shown in Equation (14)

Summary

Introduction

Nonlinearity is a common characteristic in industrial processes. Many control algorithms are proposed for nonlinear systems [1]. Escano et al [13] proposed a complexity reduction Takagi–Sugeno fuzzy model that is obtained by finding a function basis via a functional principal component analysis and used for MPC It is applied in a low-cost microcontroller system. Junghui Chen et al [19] proposed another dynamic PLS framework with the ARX model Besides these modeling methods, many attempts have been made to put forward new control strategies that compromise the merits of PLS. A nonlinear MPC in a dynamic PLS framework is given, and an offset-free control method based on it is discussed.

T–S-Fuzzy Model-Overview

Nonlinear Dynamic Fuzzy PLS Model

Dynamic PLS Control Framework

Offset-Free Fuzzy Model Predictive Control Based on Dynamic PLS Framework

Case Study