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An Adaptive Neuro-Fuzzy Inference System to Improve Fractional Order Controller Performance

N. Kanagaraj*

Electrical Engineering Department, College of Engineering at Wadi Al-dawasir, Prince Sattam Bin Abdulaziz University, Wadi Al-dawasir, 11991, Saudi Arabia

* Corresponding Author: N. Kanagaraj. Email: Array

Intelligent Automation & Soft Computing 2023, 35(3), 3213-3226.


The design and analysis of a fractional order proportional integral derivate (FOPID) controller integrated with an adaptive neuro-fuzzy inference system (ANFIS) is proposed in this study. A first order plus delay time plant model has been used to validate the ANFIS combined FOPID control scheme. In the proposed adaptive control structure, the intelligent ANFIS was designed such that it will dynamically adjust the fractional order factors (λ and µ) of the FOPID (also known as PIλDµ) controller to achieve better control performance. When the plant experiences uncertainties like external load disturbances or sudden changes in the input parameters, the stability and robustness of the system can be achieved effectively with the proposed control scheme. Also, a modified structure of the FOPID controller has been used in the present system to enhance the dynamic performance of the controller. An extensive MATLAB software simulation study was made to verify the usefulness of the proposed control scheme. The study has been carried out under different operating conditions such as external disturbances and sudden changes in input parameters. The results obtained using the ANFIS-FOPID control scheme are also compared to the classical fractional order PIλDµ and conventional PID control schemes to validate the advantages of the controllers. The simulation results confirm the effectiveness of the ANFIS combined FOPID controller for the chosen plant model. Also, the proposed control scheme outperformed traditional control methods in various performance metrics such as rise time, settling time and error criteria.


Cite This Article

N. Kanagaraj, "An adaptive neuro-fuzzy inference system to improve fractional order controller performance," Intelligent Automation & Soft Computing, vol. 35, no.3, pp. 3213–3226, 2023.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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