Comparison of Extended-Kalman- and Particle-Filter-Based Sensorless Speed Control

State estimation process is one of the major concerns for controlling and monitoring systems in industry which requires high-cost measurements or unmeasurable variables of nonlinear systems. These drawbacks can be highly eliminated by designing systems without using any kind of sensors. In this paper, sensorless speed control of a dc motor was performed by using extended Kalman filter (EKF) and particle filter (PF). The speed information is estimated by using armature current data measured from a dc motor which is controlled in various speed references with a closed-loop controller. Furthermore, a performance comparison of the EKF and the PF by taking into consideration their estimation errors under the same conditions was realized in a simulation environment. The comparison results showed that the estimation performance of the PF is more accurate but slower than the EKF. The quantitative values of accurateness and slowness are depended on the particle number of the PF. The obtained computation times of the PF having ten particles and the EKF are 180 and 15 μs, respectively.

[1]  Robert D. Lorenz,et al.  Carrier Signal Selection for Sensorless Control of PM Synchronous Machines at Zero and Very Low Speeds , 2008, 2008 IEEE Industry Applications Society Annual Meeting.

[2]  M. Tomizuka,et al.  Benefits of acceleration measurement in velocity estimation and motion control , 2004 .

[3]  Rene de Jesus Romero-Troncoso,et al.  Sensorless jerk monitoring using an adaptive antisymmetric high-order FIR filter , 2009 .

[4]  H.A. Toliyat,et al.  Artificial-neural-network-based sensorless nonlinear control of induction motors , 2005, IEEE Transactions on Energy Conversion.

[5]  Roberto Oboe,et al.  Sensorless full-digital PMSM drive with EKF estimation of speed and rotor position , 1999, IEEE Trans. Ind. Electron..

[6]  Rene de Jesus Romero-Troncoso,et al.  DSP algorithm for the extraction of dynamics parameters in CNC machine tool servomechanisms from an optical incremental encoder , 2008 .

[7]  Dong-Seok Hyun,et al.  Speed sensorless stator flux-oriented control of induction motor in the field weakening region using Luenberger observer , 2005 .

[8]  Sa dan Unnewehr Le Nasar.  Electromechanics and Electric Machines / SA. Nasar and LE. Unnewehr , 1983 .

[9]  P. Djurić,et al.  Particle filtering , 2003, IEEE Signal Process. Mag..

[10]  Peter Vas,et al.  Sensorless vector and direct torque control , 1998 .

[11]  Frede Blaabjerg,et al.  A Class of Speed-Sensorless Sliding-Mode Observers for High-Performance Induction Motor Drives , 2009, IEEE Transactions on Industrial Electronics.

[12]  S. Sul,et al.  Sensorless drive of surface-mounted permanent-magnet motor by high-frequency signal injection based on magnetic saliency , 2003 .

[13]  Johann Fichou,et al.  Particle-based methods for parameter estimation and tracking: Numerical experiments , 2004 .

[14]  Gerasimos G Rigatos,et al.  Particle and Kalman filtering for state estimation and control of DC motors. , 2009, ISA transactions.

[15]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[16]  Damian Giaouris,et al.  MRAS Sensorless Vector Control of an Induction Motor Using New Sliding-Mode and Fuzzy-Logic Adaptation Mechanisms , 2010, IEEE Transactions on Energy Conversion.

[17]  Andrew Harvey,et al.  Forecasting, Structural Time Series Models and the Kalman Filter , 1990 .

[18]  Nicolas Boizot,et al.  Adaptive-gain extended Kalman filter : application to a series connected DC motor , 2007 .

[19]  Timothy J. Robinson,et al.  Sequential Monte Carlo Methods in Practice , 2003 .

[20]  Branko Ristic,et al.  Beyond the Kalman Filter: Particle Filters for Tracking Applications , 2004 .

[21]  Surapun Yimman,et al.  Sensorless speed control of DC servo motor using Kalman filter , 2009, 2009 7th International Conference on Information, Communications and Signal Processing (ICICS).

[22]  Stephen J. Chapman,et al.  Electric Machinery Fundamentals , 1991 .

[23]  Simo Särkkä,et al.  Recursive Bayesian inference on stochastic differential equations , 2006 .

[24]  K.S.P. Kumar,et al.  On the observability of nonlinear systems: I , 1971 .

[25]  Frede Blaabjerg,et al.  Comparative study of adaptive and inherently sensorless observers for variable-speed induction-motor drives , 2006, IEEE Transactions on Industrial Electronics.

[26]  Silverio Bolognani,et al.  Extended Kalman filter tuning in sensorless PMSM drives , 2002, Proceedings of the Power Conversion Conference-Osaka 2002 (Cat. No.02TH8579).

[27]  Gerasimos G. Rigatos,et al.  Particle Filtering for State Estimation in Nonlinear Industrial Systems , 2009, IEEE Transactions on Instrumentation and Measurement.

[28]  D. Luenberger.  An introduction to observers , 1971 .