Abstract
The paper deals with different adaptive filtering algorithms (AFAs) and is implemented to the non-linear systems. As Brushless DC (BLDC) motors are non-linear in nature under the presence of noise signals like gaussian white signals, these AFAs are implemented to BLDC motor by the estimation of state and output measurements. The performance characteristics (both steady state and dynamic state characteristics) of BLDC Motor are improved by using filtering techniques like Extended Kalman Filtering (EKF) and Particle Filtering (PF). The consistency of EKF and PF methods is determined by using consistency tests, and the obtained simulation results proves that the consistency of PF technique is much better than the EKF technique. PF technique shows the better stability, strong robustness with better convergency and reliability and low sensitivity on the estimation of state and output measurements of Sensorless BLDC Motor control with non-linearities.
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References
Applications of aero space technology report on “Brushless DC Motors” NASA technologies utilities publications prepared by Midwest research institute
Y. Narendra Kumar, A. Venkatesh, Ch. Anusha, K. Vani, D. Siddarda, Estimation of speed based on flux oriented control of an induction motor drive model with reference adaptive system scheme. Trans. Eng. Sci. 2(4) (2014)
A. Venkatesh, Research Scholar.: An improved adaptive SMO for speed estimation of sensorless Dsfoc induction motor drives and stability analysis using lyapunov theorem at low frequencies. Int. J. Eng. Res. Technol. (IJERT), 8(09). ISSN: 2278-0181 (2019)
M. Siva Kumar, N. V. Anand, A novel approach for the design of controller for higher order discrete—time systems via its reduced model (IEEE, 2010)
A. Tollkuhn, F. Particke, J. Thielecke, Gaussian state estimation with non-Gaussian measurement noise, sensor data fusion: trends, solutions, applications (SDF) (2018)
Y. Xu, K. Xu, J. Wan, Z. Xiong, Y. Li, Research on particle filter tracking method based on Kalman filter, in 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC) (IEEE, 2018)
F.L. Lewis, L. Xie, D. Popa, Automation and Control Engineering—Optimal and Robust Estimation with an Introduction to Stochastic Control Theory, 2nd edn. (CRC Press, 2007)
A. Ejlali, J. Soleimani, Sensorless vector control of 3-phase BLDC motor using a novel Extended Kalman (IEEE, 2012)
Y. Salih, A.S. Malik, 3D object tracking using three Kalman filters, in Symposium on Computers & Informatics (IEEE)
Y. Xu, K. Xu, Z. Xiong, Y. Li, Research on particle filter tracking method based on Kalman filter. IMCEC (IEEE, 2018)
Y. Xu, K. Xu, J. Wan, Z. Xiong, Y. Li, Research on Particle filter tracking method based on Kalman Filter. IMCEC (2018)
E. Suzdaleva, I. Nagy, L. Pavelkova, Bayesian filtering with discrete-valued state (IEEE, 2009)
B. Chen, L. Xing, J. Liang, N. Zheng, J.C. Príncipe, Steady-state mean-square error analysis for adaptive filtering under the maximum correntropy criterion. Sig. Process. Lett. 21(7), 070–9908. IEEE (2014)
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Venkatesh, A., Nalinakshan, S., Tony Aby Varkey, M. (2021). Consistency of Extended Kalman Filtering and Particle Filtering Techniques for the State Estimation of Brushless DC Motor. In: Kumar, J., Jena, P. (eds) Recent Advances in Power Electronics and Drives. Lecture Notes in Electrical Engineering, vol 707. Springer, Singapore. https://doi.org/10.1007/978-981-15-8586-9_30
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DOI: https://doi.org/10.1007/978-981-15-8586-9_30
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