Non-linear Closed-Loop Identification of CSTR in the presence of a Non-stationary Disturbances

Aljamaan, Ibrahim; Westwick, David T.; Foley, Michael

doi:10.1016/j.ifacol.2015.12.266

Cited by 9 publications

(7 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(8) If the cost V n decreases. (9) If V n is significant en (10) Exit (11) else increase speed of convergence by setting the update step δ � δ/c, then go back to step 3 (12) End (13) else the cost, V n , increases en (14) Decrease the speed of convergence by setting: δ � δc and go back to step 5 (15) end ALGORITHM 1: Summary of the proposed identification algorithm. (Figure 4(c) in [14]).…”

Section: Resultsmentioning

confidence: 99%

“…Once J s has been computed, θ n can be updated using any quasi-Newton optimization, and Levenberg-Marquardt is proposed in this work. e separable least squares approach is described in more detail by Aljamaan et al [12]. e nonlinear vector parameters θ n can be updated using any quasi-Newton optimization.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

“…ere are a few attempts to apply nonlinear system identification techniques to common applications in different engineering fields reported in the literature. In the area of chemical process control, Aljamaan et al [12] identified the dynamics of the continuously stirred tank reactor (CSTR) in the presence of nonstationary disturbances, Zhu [13] identified a distillation column, and Birpoutsoukis et al [14] estimated a Volterra series model of the cascaded two-tank system. Similarly, in biomedical engineering applications, Vlaar et al [15] identified brain signals by electroencephalography (EEG) while Cescon et al [16] identified models of the blood sugar level inside the human body.…”

Section: Introductionsmentioning

confidence: 99%

See 2 more Smart Citations

Hammerstein Box-Jenkins System Identification of the Cascaded Tanks Benchmark System

Aljamaan

Al‐Dhaifallah

Westwick

2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

A common process control application is the cascaded two-tank system, where the level is controlled in the second tank. A nonlinear system identification approach is presented in this work to predict the model structure parameters that minimize the difference between the estimated and measured data, using benchmark datasets. The general suggested structure consists of a static nonlinearity in cascade with a linear dynamic filter in addition to colored noise element. A one-step ahead prediction error-based technique is proposed to estimate the model. The model is identified using a separable least squares optimization, where only the parameters that appear nonlinearly in the output of the predictor are solved using a modified Levenberg–Marquardt iterative optimization approach, while the rest are fitted using simple least squares after each iteration. Finally, MATLAB simulation examples using benchmark data are included.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Section: Introductionsmentioning

confidence: 99%

See 1 more Smart Citation

Hammerstein Box-Jenkins System Identification of the Cascaded Tanks Benchmark System

Aljamaan

Al‐Dhaifallah

Westwick

2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…A more detailed formulation of different nonlinear Box-Jenkins approaches is presented in Refs. [21], [40]- [42].…”

Section: A Prediction Error Methodsmentioning

confidence: 99%

“…The second approach is the block structure system identification, such as the autoregressive integrated moving average [21], best linear approximation [22], normalized least mean square [23], polynomial non-linear state-space [24], Bayesian nonlinear stochastic [25], blind nonlinear identification [26], nonlinear least square [27] and nonlinear linear fractional representation [22], [24], [27], [28]. Moreover, there are review papers that compare the results of these prediction methods from different points of view such as the accuracy, sensitivity, and prediction horizon [3], [6], [29], [30].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Blood Glucose Level Using Nonlinear System Identification Approach

Aljamaan

Al-Naib

2022

IEEE Access

Self Cite

View full text Add to dashboard Cite

Predicting the blood glucose level of type 1 diabetes mellitus of patients could prevent hypo/hyperglycemia incidents that are threats for the patients with this disease. A nonlinear system identification approach is proposed in this work to develop a mathematical model, which can be used to predict the blood glucose level over a given period with high accuracy. More specifically, the Hammerstein Box-Jenkins model is used to approximate the system, where two infinite impulse response filters represent the linear and noise processes, and a polynomial basis function represents the nonlinearity. The proposed identification method is based on the Steiglitz-McBride approach to predict the model parameters. Moreover, a simulation software licensed by the USA Food and Drug Administration that simulates the dynamics of the glucose-insulin system metabolism inside the body, called Type I Diabetes Metabolic Simulator (T1DMS), was used to generate the data. Thirty subjects of different age groups were considered, and the data was generated for a week with a sample per minute, i.e. 302430 data points. This data was then processed using a developed MATLAB code to predict the blood glucose level. Various scenarios were established to validate the proposed approach. The simulations showed very promising results with a very low average root mean square error of 1 mg/dl, which is seven times less compared to other prediction techniques. Other cost functions have also been used and they showed very good results. In the future, this approach can be embedded in closed-loop continuous blood glucose monitoring systems in order to give alerts to the patients and help in calculating the needed insulin dose.INDEX TERMS Blood glucose level; Box-Jenkins model; Hammerstein model; nonlinear system identification; Steiglitz-McBride approach; T1DMS simulator software; Type 1 diabetes.

show abstract

Phase Portrait Analysis of Continuous Stirred Tank Reactor (CSTR)

Ranjan

Chakraborty

2020

Nanoelectronics, Circuits and Communication Systems

View full text Add to dashboard Cite

Non-linear Closed-Loop Identification of CSTR in the presence of a Non-stationary Disturbances

Cited by 9 publications

References 12 publications

Hammerstein Box-Jenkins System Identification of the Cascaded Tanks Benchmark System

Hammerstein Box-Jenkins System Identification of the Cascaded Tanks Benchmark System

Prediction of Blood Glucose Level Using Nonlinear System Identification Approach

Phase Portrait Analysis of Continuous Stirred Tank Reactor (CSTR)

Contact Info

Product

Resources

About