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<p>Interactions in multi-input multi-output (MIMO) plants can be a challenge in process industries, and effective control methodologies are necessary to overcome this challenge. This paper introduces two model-free approaches for tuning centralized proportional–integral (PI) controllers employing metaheuristic algorithms instead of trial-and-error techniques. The first method utilizes only the first Markov parameter and steady-state gain (SSG), while the second relies only on the SSG. Metaheuristic algorithms, including particle swarm optimization (PSO), differential evolution (DE), invasive weed optimization (IWO), and teaching–learning-based optimization (TLBO), are employed to determine optimal controller parameters. To validate the efficacy of the proposed methods, they are applied to the Wood and Berry distillation column (WBDC), an important system in the process industry used for product separation. The model-free control designs in the proposed controllers are suitable for practical implementation, as they are based on minimal knowledge of the system without requiring a mathematical model. This approach is considered due to the potential inaccuracy of the model, which could affect the control design. Furthermore, a comparative study using performance characteristics and indices is conducted with four previously proposed PI controllers in the existing literature, demonstrating superior performance. Additionally, a closed-loop stability analysis is performed, along with an assessment of the robustness of the proposed controllers for practical considerations.</p>

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Design of optimal model-free PI controllers using metaheuristic algorithms for a distillation column

  • 期刊论文

  • 发表日期：

    2025年8月28日

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  • Danial Pazoki,
  • Moein Sarbandi,
  • Reza Kazemi,
  • Mohammad Hossein Modirrousta,
  • Amirhossein Nikoofard,
  • Ali Khaki-Sedigh

摘要

Interactions in multi-input multi-output (MIMO) plants can be a challenge in process industries, and effective control methodologies are necessary to overcome this challenge. This paper introduces two model-free approaches for tuning centralized proportional–integral (PI) controllers employing metaheuristic algorithms instead of trial-and-error techniques. The first method utilizes only the first Markov parameter and steady-state gain (SSG), while the second relies only on the SSG. Metaheuristic algorithms, including particle swarm optimization (PSO), differential evolution (DE), invasive weed optimization (IWO), and teaching–learning-based optimization (TLBO), are employed to determine optimal controller parameters. To validate the efficacy of the proposed methods, they are applied to the Wood and Berry distillation column (WBDC), an important system in the process industry used for product separation. The model-free control designs in the proposed controllers are suitable for practical implementation, as they are based on minimal knowledge of the system without requiring a mathematical model. This approach is considered due to the potential inaccuracy of the model, which could affect the control design. Furthermore, a comparative study using performance characteristics and indices is conducted with four previously proposed PI controllers in the existing literature, demonstrating superior performance. Additionally, a closed-loop stability analysis is performed, along with an assessment of the robustness of the proposed controllers for practical considerations.