Valve friction quantification and nonlinear process model identification

2010 ◽  
Vol 43 (5) ◽  
pp. 115-120 ◽  
Author(s):  
Rodrigo A. Romano ◽  
Claudio Garcia
Keyword(s):  
Process Model ◽  
Model Identification ◽  
Nonlinear Process

Model Identification and Predictive Controller Design for a Nonlinear Process

2014 ◽  
Vol 6 (3) ◽  
pp. 178-187
Author(s):  
Sivanandam Venkatesh ◽  
Sukanya R. Warier ◽  
K. Ramkumar ◽  
Rengarajan Amirtharaj
Keyword(s):  
Controller Design ◽  
Model Identification ◽  
Nonlinear Process ◽  
Predictive Controller

Non Linear and Non Gaussian States and Parameters Estimation using Bayesian Methods-Comparatives Studies

2014 ◽  
pp. 711-748 ◽  
Author(s):  
Majdi Mansouri ◽  
Moustafa Mohamed-Seghir ◽  
Hazem Nounou ◽  
Mohamed Nounou ◽  
Haitham A. Abu-Rub
Keyword(s):  
Kalman Filter ◽  
Bayesian Methods ◽  
Process Model ◽  
Unscented Kalman Filter ◽  
Nonlinear Process ◽  
Parameters Estimation ◽  
Gaussian States ◽  
Estimation Algorithms ◽  
Non Linear ◽  
Non Gaussian

This chapter deals with the problem of non-linear and non-Gaussian states and parameters estimation using Bayesian methods. The performances of various conventional and state-of-the-art state estimation techniques are compared when they are utilized to achieve this objective. These techniques include the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Particle Filter (PF). In the current work, the authors consider two systems (biological model and power system) to perform evaluation of estimation algorithms. The results of the comparative studies show that the UKF provides a higher accuracy than the EKF due to the limited ability of EKF to accurately estimate the mean and covariance matrix of the estimated states through lineralization of the nonlinear process model. The results also show that the PF provides a significant improvement over the UKF because, unlike UKF, PF is not restricted by linear-Gaussian assumptions which greatly extends the range of problems that can be tackled.

A New Signal Design Tool for Process Model Identification

2004 ◽  
Vol 37 (9) ◽  
pp. 23-28
Author(s):  
V.C. Machado ◽  
J.O. Trierweiler ◽  
A.R. Secchi
Keyword(s):  
Process Model ◽  
Model Identification ◽  
Design Tool ◽  
Signal Design

scholarly journals Advanced Construction of the Dynamic Matrix in Numerically Efficient Fuzzy MPC Algorithms

Algorithms ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 25
Author(s):  
Piotr M. Marusak
Keyword(s):  
Process Model ◽  
Linear Models ◽  
Control Plant ◽  
Fuzzy Model ◽  
Nonlinear Process ◽  
Linear Constraints ◽  
Good Prediction ◽  
Dynamic Matrix ◽  
Prediction And Control ◽  
And Control

A method for the advanced construction of the dynamic matrix for Model Predictive Control (MPC) algorithms with linearization is proposed in the paper. It extends numerically efficient fuzzy algorithms utilizing skillful linearization. The algorithms combine the control performance offered by the MPC algorithms with nonlinear optimization (NMPC algorithms) with the numerical efficiency of the MPC algorithms based on linear models in which the optimization problem is a standard, easy-to-solve, quadratic programming problem with linear constraints. In the researched algorithms, the free response obtained using a nonlinear process model and the future trajectory of the control signals is used to construct an advanced dynamic matrix utilizing the easy-to-obtain fuzzy model. This leads to obtaining very good prediction and control quality very close to those offered by NMPC algorithms. The proposed approach is tested in the control system of a nonlinear chemical control plant—a CSTR reactor with the van de Vusse reaction.

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