PREGLEJ, Aleksander ;STEINER, Igor ;BLAŽIČ, Sašo . Multivariable Predictive Functional Control of an Autoclave. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 59, n.2, p. 89-96, june 2018. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/article/multivariable-predictive-functional-control-of-an-autoclave/>. Date accessed: 16 apr. 2024. doi:http://dx.doi.org/10.5545/sv-jme.2012.617.
Preglej, A., Steiner, I., & Blažič, S. (2013). Multivariable Predictive Functional Control of an Autoclave. Strojniški vestnik - Journal of Mechanical Engineering, 59(2), 89-96. doi:http://dx.doi.org/10.5545/sv-jme.2012.617
@article{sv-jmesv-jme.2012.617,
author = {Aleksander Preglej and Igor Steiner and Sašo Blažič},
title = {Multivariable Predictive Functional Control of an Autoclave},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {59},
number = {2},
year = {2013},
keywords = {predictive control; multivariable control; autoclave; temperature; pressure; interactions},
abstract = {This paper presents the predictive functional control of an autoclave, which is designed, tested and compared in uni- and multivariable manners. The control of the autoclave is based on our previously developed mathematical model for an autoclave, where we dealt with the heat-transfer and pressure-changing processes. First, we presented the principles of the predictive control algorithm, which are easy to understand. Next, the basic principles of predictive control were extended to a multivariable manner, so we presented the control law of the multivariable predictive algorithm. Furthermore, we depicted the suggested tuning rules for both control algorithms, which normally give satisfactory results, considering the trade-off between robustness and performance. We implemented both predictive algorithms in the linearized and simplified autoclave model, where we applied faster tuning rules due to the need for faster closed-loop responses. For the comparison we also designed and applied a classical compensating PI controller. The results show the superior performance of the multivariable predictive approach. All three algorithms rise similarly quickly, but then the PI and the simple predictive controller slowly approach the desired value due to slower tuning, because of the very noisy manipulated variable. The interactions in the autoclave model are not so strong, so by the interactions influence rejection, both predictive algorithms show similar performances, while the PI approach performs much worse. The multivariable predictive approach also proves its superior performance by the interactions influence rejection when controlling the processes with stronger interactions. We can conclude that the autoclave should be controlled as one multivariable process using a multivariable predictive functional approach.},
issn = {0039-2480}, pages = {89-96}, doi = {10.5545/sv-jme.2012.617},
url = {https://www.sv-jme.eu/article/multivariable-predictive-functional-control-of-an-autoclave/}
}
Preglej, A.,Steiner, I.,Blažič, S. 2013 June 59. Multivariable Predictive Functional Control of an Autoclave. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 59:2
%A Preglej, Aleksander %A Steiner, Igor %A Blažič, Sašo %D 2013 %T Multivariable Predictive Functional Control of an Autoclave %B 2013 %9 predictive control; multivariable control; autoclave; temperature; pressure; interactions %! Multivariable Predictive Functional Control of an Autoclave %K predictive control; multivariable control; autoclave; temperature; pressure; interactions %X This paper presents the predictive functional control of an autoclave, which is designed, tested and compared in uni- and multivariable manners. The control of the autoclave is based on our previously developed mathematical model for an autoclave, where we dealt with the heat-transfer and pressure-changing processes. First, we presented the principles of the predictive control algorithm, which are easy to understand. Next, the basic principles of predictive control were extended to a multivariable manner, so we presented the control law of the multivariable predictive algorithm. Furthermore, we depicted the suggested tuning rules for both control algorithms, which normally give satisfactory results, considering the trade-off between robustness and performance. We implemented both predictive algorithms in the linearized and simplified autoclave model, where we applied faster tuning rules due to the need for faster closed-loop responses. For the comparison we also designed and applied a classical compensating PI controller. The results show the superior performance of the multivariable predictive approach. All three algorithms rise similarly quickly, but then the PI and the simple predictive controller slowly approach the desired value due to slower tuning, because of the very noisy manipulated variable. The interactions in the autoclave model are not so strong, so by the interactions influence rejection, both predictive algorithms show similar performances, while the PI approach performs much worse. The multivariable predictive approach also proves its superior performance by the interactions influence rejection when controlling the processes with stronger interactions. We can conclude that the autoclave should be controlled as one multivariable process using a multivariable predictive functional approach. %U https://www.sv-jme.eu/article/multivariable-predictive-functional-control-of-an-autoclave/ %0 Journal Article %R 10.5545/sv-jme.2012.617 %& 89 %P 8 %J Strojniški vestnik - Journal of Mechanical Engineering %V 59 %N 2 %@ 0039-2480 %8 2018-06-28 %7 2018-06-28
Preglej, Aleksander, Igor Steiner, & Sašo Blažič. "Multivariable Predictive Functional Control of an Autoclave." Strojniški vestnik - Journal of Mechanical Engineering [Online], 59.2 (2013): 89-96. Web. 16 Apr. 2024
TY - JOUR AU - Preglej, Aleksander AU - Steiner, Igor AU - Blažič, Sašo PY - 2013 TI - Multivariable Predictive Functional Control of an Autoclave JF - Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2012.617 KW - predictive control; multivariable control; autoclave; temperature; pressure; interactions N2 - This paper presents the predictive functional control of an autoclave, which is designed, tested and compared in uni- and multivariable manners. The control of the autoclave is based on our previously developed mathematical model for an autoclave, where we dealt with the heat-transfer and pressure-changing processes. First, we presented the principles of the predictive control algorithm, which are easy to understand. Next, the basic principles of predictive control were extended to a multivariable manner, so we presented the control law of the multivariable predictive algorithm. Furthermore, we depicted the suggested tuning rules for both control algorithms, which normally give satisfactory results, considering the trade-off between robustness and performance. We implemented both predictive algorithms in the linearized and simplified autoclave model, where we applied faster tuning rules due to the need for faster closed-loop responses. For the comparison we also designed and applied a classical compensating PI controller. The results show the superior performance of the multivariable predictive approach. All three algorithms rise similarly quickly, but then the PI and the simple predictive controller slowly approach the desired value due to slower tuning, because of the very noisy manipulated variable. The interactions in the autoclave model are not so strong, so by the interactions influence rejection, both predictive algorithms show similar performances, while the PI approach performs much worse. The multivariable predictive approach also proves its superior performance by the interactions influence rejection when controlling the processes with stronger interactions. We can conclude that the autoclave should be controlled as one multivariable process using a multivariable predictive functional approach. UR - https://www.sv-jme.eu/article/multivariable-predictive-functional-control-of-an-autoclave/
@article{{sv-jme}{sv-jme.2012.617},
author = {Preglej, A., Steiner, I., Blažič, S.},
title = {Multivariable Predictive Functional Control of an Autoclave},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {59},
number = {2},
year = {2013},
doi = {10.5545/sv-jme.2012.617},
url = {https://www.sv-jme.eu/article/multivariable-predictive-functional-control-of-an-autoclave/}
}
TY - JOUR AU - Preglej, Aleksander AU - Steiner, Igor AU - Blažič, Sašo PY - 2018/06/28 TI - Multivariable Predictive Functional Control of an Autoclave JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 59, No 2 (2013): Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2012.617 KW - predictive control, multivariable control, autoclave, temperature, pressure, interactions N2 - This paper presents the predictive functional control of an autoclave, which is designed, tested and compared in uni- and multivariable manners. The control of the autoclave is based on our previously developed mathematical model for an autoclave, where we dealt with the heat-transfer and pressure-changing processes. First, we presented the principles of the predictive control algorithm, which are easy to understand. Next, the basic principles of predictive control were extended to a multivariable manner, so we presented the control law of the multivariable predictive algorithm. Furthermore, we depicted the suggested tuning rules for both control algorithms, which normally give satisfactory results, considering the trade-off between robustness and performance. We implemented both predictive algorithms in the linearized and simplified autoclave model, where we applied faster tuning rules due to the need for faster closed-loop responses. For the comparison we also designed and applied a classical compensating PI controller. The results show the superior performance of the multivariable predictive approach. All three algorithms rise similarly quickly, but then the PI and the simple predictive controller slowly approach the desired value due to slower tuning, because of the very noisy manipulated variable. The interactions in the autoclave model are not so strong, so by the interactions influence rejection, both predictive algorithms show similar performances, while the PI approach performs much worse. The multivariable predictive approach also proves its superior performance by the interactions influence rejection when controlling the processes with stronger interactions. We can conclude that the autoclave should be controlled as one multivariable process using a multivariable predictive functional approach. UR - https://www.sv-jme.eu/article/multivariable-predictive-functional-control-of-an-autoclave/
Preglej, Aleksander, Steiner, Igor, AND Blažič, Sašo. "Multivariable Predictive Functional Control of an Autoclave" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 59 Number 2 (28 June 2018)
Strojniški vestnik - Journal of Mechanical Engineering 59(2013)2, 89-96
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
This paper presents the predictive functional control of an autoclave, which is designed, tested and compared in uni- and multivariable manners. The control of the autoclave is based on our previously developed mathematical model for an autoclave, where we dealt with the heat-transfer and pressure-changing processes. First, we presented the principles of the predictive control algorithm, which are easy to understand. Next, the basic principles of predictive control were extended to a multivariable manner, so we presented the control law of the multivariable predictive algorithm. Furthermore, we depicted the suggested tuning rules for both control algorithms, which normally give satisfactory results, considering the trade-off between robustness and performance. We implemented both predictive algorithms in the linearized and simplified autoclave model, where we applied faster tuning rules due to the need for faster closed-loop responses. For the comparison we also designed and applied a classical compensating PI controller. The results show the superior performance of the multivariable predictive approach. All three algorithms rise similarly quickly, but then the PI and the simple predictive controller slowly approach the desired value due to slower tuning, because of the very noisy manipulated variable. The interactions in the autoclave model are not so strong, so by the interactions influence rejection, both predictive algorithms show similar performances, while the PI approach performs much worse. The multivariable predictive approach also proves its superior performance by the interactions influence rejection when controlling the processes with stronger interactions. We can conclude that the autoclave should be controlled as one multivariable process using a multivariable predictive functional approach.