Smith predictor based fractional order controller design for improved performance and robustness of unstable FOPTD processes

Performance and robustness are essential characteristics for the application of unstable time-delayed systems. As tasks become more complex, traditional control methods cannot meet such demands for performance and robustness. The present work aims to develop fractional order-based controllers for enhanced Smith predictor-based unstable first-order plus time-delayed systems (FOPTD) with improved performance and robustness. In the current work, fractional order controllers using a Genetic Algorithm (GA) are designed with enhanced SP (Smith Predictor) structure to control unstable first-order time-delayed processes to improve performance. Furthermore, in the feedback path a fractional order (FO) filter is used to further improve robustness and performance. A systematic methodology is proposed for obtaining the optimum fractional order filter parameters based on the minimization of Integral Absolute Error (IAE). The recommended approach is beneficial to balance the necessary tradeoff between performance and robustness. Also, the proposed method provides flexibility in tuning the degree of freedom by adding a fractional order integrator, thus leading to robust performance. The efficacy of the recommended controller is analyzed by simulating numerical examples from the literature. The proposed controller provides enhanced performance and robustness compared to the literature.