Electric vehicle speed tracking control using an ANFIS-based fractional order PID controller

Electric vehicles (EVs) have assumed prominence due to their enhanced performance, efficiency, and zero carbon emission. This paper proposes an efficient adaptive neuro-fuzzy inference system (ANFIS) based fractional order PID (FOPID) controller for an EV speed tracking control driven by a DC motor. The optimal controller parameters of the FOPID controller are found via an Ant Colony Optimization (ACO) method. The ANFIS controllers are well trained, tested, and validated using the data set sextracted from the fuzzy-based controllers. The performance and accuracy of the ANFIS model are evaluated using statistical parameters such as mean square error (MSE), coefficient of correlation (R), and root mean square error (RMSE). The controller performance, energy consumption, and robustness are tested using the new European drive cycle (NEDC) test. The efficacy of the ANFIS-based controller is demonstrated by comparing its performance with properly tuned fuzzy-based controllers. The proposed controller shows robustness towards external disturbances and offers promising EV speed regulation control. The comparative results illustrate the superior performance of ANFIS-based FOPID controller with high prediction and low error rates. MATLAB- Simulink platform is used for system modeling, controller design, and numerical simulation.