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<p>This paper introduces a novel intelligent Internal Model Control (IMC) for trajectory tracking of a 2-DOF robotic arm, optimized using multi-objective genetic algorithms. Unlike conventional IMC, the proposed approach automatically tunes the model inversion gain by simultaneously optimizing the integral of squared error, percent overshoot, and settling time. The controller combines a proportional gain for model inversion with an adaptive gain for error correction, ensuring both stability and high tracking accuracy. Simulation results under nominal conditions, disturbances, and parametric uncertainties show that the proposed MOGA-IMC outperforms GA-ISE-IMC and GA-PO-IMC, achieving faster settling, lower overshoot, and enhanced robustness. These findings demonstrate the effectiveness of integrating IMC with multi-objective evolutionary optimization for controlling complex and uncertain robotic systems.</p>

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Intelligent internal model controller optimized via multi-objective genetic algorithms for robotic arm trajectory tracking

  • 期刊论文

  • 发表日期：

    2026年5月5日

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  • Imen Saidi,
  • Achraf Jabeur Telmoudi

摘要

This paper introduces a novel intelligent Internal Model Control (IMC) for trajectory tracking of a 2-DOF robotic arm, optimized using multi-objective genetic algorithms. Unlike conventional IMC, the proposed approach automatically tunes the model inversion gain by simultaneously optimizing the integral of squared error, percent overshoot, and settling time. The controller combines a proportional gain for model inversion with an adaptive gain for error correction, ensuring both stability and high tracking accuracy. Simulation results under nominal conditions, disturbances, and parametric uncertainties show that the proposed MOGA-IMC outperforms GA-ISE-IMC and GA-PO-IMC, achieving faster settling, lower overshoot, and enhanced robustness. These findings demonstrate the effectiveness of integrating IMC with multi-objective evolutionary optimization for controlling complex and uncertain robotic systems.