A New Approach for Intelligent Internal Model Controller Design Using the Genetic Algorithm

Abstract

This paper presents an advanced enhancement of the Internal Model Control (IMC) strategy by integrating a Genetic Algorithm (GA) to optimize the performance of robust control techniques applied to time-delay systems. Precise temperature regulation in electric furnaces is a critical requirement in various industrial sectors, including metallurgy, microelectronics, and chemical processing, where even minor deviations can significantly affect product quality, safety, and energy efficiency. However, conventional controllers, particularly classical PID regulators, often exhibit limited adaptability to parameter uncertainties, nonlinearities, and external disturbances, leading to degraded performance and potential instability. To overcome these limitations, an intelligent IMC-based controller is proposed, in which the inversion gain of the temperature regulation loop is optimally tuned (offline) using a Genetic Algorithm. The proposed approach ensures optimal tuning by employing multiple performance indices, including the Integral of Squared Error (ISE), Integral of Absolute Error (IAE), Integral of Time-weighted Absolute Error (ITAE), and Integral of Time-weighted Squared Error (ITSE), while also explicitly minimizing overshoot and improving transient response characteristics. Simulation results demonstrate that the optimized controller significantly enhances system stability, achieves zero overshoot, and improves robustness against external disturbances and abrupt setpoint variations. Moreover, the proposed method ensures accurate temperature tracking, faster settling time, and reduced energy consumption. These findings highlight the effectiveness of combining IMC with evolutionary optimization techniques and confirm its strong potential for improving control performance in industrial processes sensitive to time delays and thermal dynamics.