Extended Kalman Filter Based State Estimation of Stepper Motor
Sumathi K1, Vijayachitra S2
1Sumathi K, Electronics and Instrumentation Engineering, Kongu Engineering College, Erode, India.
2Vijayachitra S, Electronics and Instrumentation Engineering, Kongu Engineering College, Erode, India.
Manuscript received on December 01, 2012. | Revised Manuscript received on December 18, 2012. | Manuscript published on December 25, 2012. | PP: 1-5 | Volume-1 Issue-2, December 2012 | Retrieval Number: B0112121212/2012©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering & Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: State estimation process is one of the major concerns for controlling and monitoring systems in industry which requires high-cost measurements or involves unmeasurable variables of nonlinear systems. These drawbacks can be highly eliminated by designing systems without using any kind of sensors. In the proposed work, the state estimation technique is used for the state estimation of stepper motor. The theoretical basis of Extended Kalman Filter algorithm is explained in detail and its performance is tested with simulations. A stochastically nonlinear state estimator named Extended Kalman Filter is presented. The motor model designed for EKF application involves rotor speed, rotor position and stator currents of the stepper motor. Thus, by using this estimator the states of the stepper motor can be estimated.
Keywords: Extended Kalman Filter, non linear system, state estimation, stepper motor.