Fractional order adaptive Kalman filter for sensorless speed control of DC motor

State estimation is a challenging and most crucial issue in the industry for proper monitoring and controlling of the plants. These kinds of control systems have the requirement of costly measurement sensors/equipment for measurable and unmeasurable state variables of the dynamical plants. These drawbacks can be overcome by designing a sensorless system to estimate the state variables. In the proposed work, sensorless speed control of DC motor is implemented by using a fractional-order adaptive Kalman filter (FOAKF). The FOAKF algorithm uses a fractional feedback loop of the previous Kalman gain along with the current Kalman gain. The motor shaft speed is estimated by the FOAKF state estimator. Furthermore, a performance comparison of extended Kalman filter (EKF) and FOAKF estimator under a similar condition is realised using MATLAB/Simulink environment. To validate the performance of the FOAKF estimator, a hardware prototype model has been presented with the help of the arduino board. In the proposed work, the root-mean-square error (RMSE) and Euclidean distance error between reference speed and estimated speed have been used for the performance metric. The performance comparison result shows that FOAKF is a more robust and accurate state estimator in comparison with EKF.