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<p>Many industrial processes display dynamic behavior that is better captured by fractional-order models than by traditional integer-order models. Controllers based on fractional-order dynamics provide improved robustness and increased design flexibility due to the additional tuning parameters they offer. This paper presents a unified Internal Model Control (IMC)-based control architecture aimed at achieving improved set-point tracking and disturbance rejection for stable time-delay processes. The proposed framework is applicable to systems of varying order and dynamics, encompassing both integer- and fractional-order models. Controller parameters are systematically determined using a maximum sensitivity constraint to ensure a desirable trade-off between performance and robustness. The effectiveness of the proposed controller is evaluated through multiple representative process models, including a DC servo system, liquid-level process, bioreactor, fuel cell system, and continuous stirred tank reactor. Comparative analyses are conducted against recently reported control strategies under nominal conditions as well as in the presence of plant uncertainties. Robustness is further examined by introducing ± 10\% variations in process parameters. Standard performance indices, namely integral squared error (ISE), integral time-weighted absolute error (ITAE), and integral absolute error (IAE), are employed for quantitative assessment. Finally, experimental results are provided to demonstrate the real-time feasibility and practical relevance of the proposed control scheme.</p>

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Experimentally validated IMC-based tuning of fractional filtered PI controller for time-delayed chemical processes

  • 期刊论文

  • 发表日期：

    2026年7月21日

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  • Rammurti Meena

摘要

Many industrial processes display dynamic behavior that is better captured by fractional-order models than by traditional integer-order models. Controllers based on fractional-order dynamics provide improved robustness and increased design flexibility due to the additional tuning parameters they offer. This paper presents a unified Internal Model Control (IMC)-based control architecture aimed at achieving improved set-point tracking and disturbance rejection for stable time-delay processes. The proposed framework is applicable to systems of varying order and dynamics, encompassing both integer- and fractional-order models. Controller parameters are systematically determined using a maximum sensitivity constraint to ensure a desirable trade-off between performance and robustness. The effectiveness of the proposed controller is evaluated through multiple representative process models, including a DC servo system, liquid-level process, bioreactor, fuel cell system, and continuous stirred tank reactor. Comparative analyses are conducted against recently reported control strategies under nominal conditions as well as in the presence of plant uncertainties. Robustness is further examined by introducing ± 10\% variations in process parameters. Standard performance indices, namely integral squared error (ISE), integral time-weighted absolute error (ITAE), and integral absolute error (IAE), are employed for quantitative assessment. Finally, experimental results are provided to demonstrate the real-time feasibility and practical relevance of the proposed control scheme.