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<p>A novel reduction approach is introduced to design the controller and simplify the complexity of large-scale continuous dynamic systems. This technique involves a generalized adaptation of the standard pole clustering method, which is used to derive the reduced denominator coefficients for the simplified model. The numerator polynomial coefficients are then determined using the Cauer second form. The generalized pole clustering (GPC) algorithm ensures that the key characteristics, such as stability and dominant poles, are preserved in the reduced system. To validate the effectiveness of the proposed method and gauge the closeness of the reduced model to the original system, various performance error indices are calculated. The technique has been applied to several benchmark systems, consistently yielding minimal error indices. After obtaining the reduced-order model, its transfer function is used to design the PID and lead/lag compensators via a moment matching algorithm. When the controller designed from the reduced model is applied to the original dynamical system, the closed-loop plant gives approximately the same response as required. Additionally, unit step responses and time domain specifications of the closed-loop plants are evaluated to demonstrate the usefulness of the proposed algorithm.</p>

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A New Model Reduction Technique for the Design of Controller for the Large-Scale Dynamical Systems

  • 期刊论文

  • 发表日期：

    2026年4月26日

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  • Arvind Kumar Prajapati,
  • Rajnish Bhasker,
  • Vivek Anand Verma,
  • Adnan Daraghmeh,
  • Amit Kumar Manocha,
  • Sachidananda Sen

摘要

A novel reduction approach is introduced to design the controller and simplify the complexity of large-scale continuous dynamic systems. This technique involves a generalized adaptation of the standard pole clustering method, which is used to derive the reduced denominator coefficients for the simplified model. The numerator polynomial coefficients are then determined using the Cauer second form. The generalized pole clustering (GPC) algorithm ensures that the key characteristics, such as stability and dominant poles, are preserved in the reduced system. To validate the effectiveness of the proposed method and gauge the closeness of the reduced model to the original system, various performance error indices are calculated. The technique has been applied to several benchmark systems, consistently yielding minimal error indices. After obtaining the reduced-order model, its transfer function is used to design the PID and lead/lag compensators via a moment matching algorithm. When the controller designed from the reduced model is applied to the original dynamical system, the closed-loop plant gives approximately the same response as required. Additionally, unit step responses and time domain specifications of the closed-loop plants are evaluated to demonstrate the usefulness of the proposed algorithm.