Startseite Maximum sensitivity constrained modified Smith predictor for delayed integrating type stirred tank reactors model
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Maximum sensitivity constrained modified Smith predictor for delayed integrating type stirred tank reactors model

  • Maneesh Kumar Gupta ORCID logo EMAIL logo und Moina Ajmeri ORCID logo
Veröffentlicht/Copyright: 10. Dezember 2024

Abstract

A maximum sensitivity constrained modified Smith predictor consisting of two PD controllers is developed for integrating type continuously stirred tank reactors (CSTRs) model. This CSTR type model approximated as integrating first order plus time delay with and without inverse response processes is challenging to control as it contains integrating term, process time delay and a zero lying in the right half of s-plane. The inner and outer loop PD blocks are tuned to achieve the desired set-point tracking and load disturbance rejection responses using direct synthesis approach. The proposed control is free from integrating action and therefore, smooth setpoint tracking response with no overshoot is obtained which is highly desirable in the process industries. Two design parameters λ and τ are involved in the suggested control whose suitable values are recommended to achieve the desired robustness level by specifying the maximum sensitivities of the inner and outer loop transfer functions. Various illustrative models are considered to show the effectiveness and superiority of the proposed control structure. The system robustness towards process parameter perturbations and load disturbances are investigated in all the examples. Performance of the suggested modified Smith predictor is found superior as compared to the other recently published works.


Corresponding author: Maneesh Kumar Gupta, Department of Electrical Engineering, Veer Bahadur Singh Purvanchal University, Jaunpur, India; and Department of Electrical Engineering, National Institute of Technology Patna, Patna, India, E-mail:

Acknowledgments

The authors are very grateful to NIT Patna, India and Veer Bahadur Singh Purvanchal university, Jaunpur, India for providing the facilities and research environment to complete this work.

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: We will take responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

  6. Research funding: There is no funding in the support of this work.

  7. Data availability: All data generated or analysed during this study are included in this article.

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Received: 2024-06-16
Accepted: 2024-11-14
Published Online: 2024-12-10

© 2024 Walter de Gruyter GmbH, Berlin/Boston

Heruntergeladen am 16.11.2025 von https://www.degruyterbrill.com/document/doi/10.1515/ijcre-2024-0122/pdf
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