Non-linear state estimation of PMSM using derivative-free and square-root Cubature Kalman Filter

2017 ◽  
Author(s):  
Divya G Pillai ◽  
A Vivek ◽  
V Srikanth
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Square Root ◽  
Cubature Kalman Filter ◽  
Derivative Free ◽  
Non Linear ◽  
Linear State

scholarly journals Derivative‐free square‐root cubature Kalman filter for non‐linear brushless DC motors

2016 ◽  
Vol 10 (5) ◽  
pp. 419-429 ◽  
Author(s):  
Devendra Potnuru ◽  
Kumar Pakki Bharani Chandra ◽  
Ienkaran Arasaratnam ◽  
Da‐Wei Gu ◽  
Karlapudy Alice Mary ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Square Root ◽  
Brushless Dc Motors ◽  
Brushless Dc ◽  
Dc Motors ◽  
Cubature Kalman Filter ◽  
Derivative Free ◽  
Non Linear

Applying GP-EKF and GP-SCKF for non-linear state estimation and fault detection in a continuous stirred-tank reactor system

2016 ◽  
Vol 39 (10) ◽  
pp. 1486-1496 ◽  
Author(s):  
Elham Kowsari ◽  
Behrooz Safarinejadian
Keyword(s):  
Kalman Filter ◽  
Fault Detection ◽  
Gaussian Process ◽  
Stirred Tank ◽  
Square Root ◽  
Continuous Stirred Tank Reactor ◽  
Tank Reactor ◽  
Cubature Kalman Filter ◽  
Non Linear ◽  
Continuous Stirred Tank

This paper proposes two novel methods for fault detection in non-linear processes. These methods apply a Gaussian process (GP) to model the underlying process, and then the extended Kalman filter (EKF) and square root cubature Kalman filter (SCKF) are used to detect faults. Accordingly, two approaches called the Gaussian process–extended Kalman filter (GP-EKF) and Gaussian process–square root cubature Kalman filter (GP-SCKF) are proposed. The most important characteristic of these proposed methods is that there is no need for an accurate model of the system. Therefore, these methods are considered non-parametric approaches of fault detection in non-linear systems. To illustrate the performance of these algorithms in fault detection, they have been used in a continuous stirred-tank reactor system (CSTR). Both proposed methods are able to detect sensor faults at an early stage.

scholarly journals Motion State Estimation of Target Vehicle under Unknown Time-Varying Noises Based on Improved Square-Root Cubature Kalman Filter

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2620 ◽  
Author(s):  
Shiping Song ◽  
Jian Wu
Keyword(s):  
Kalman Filter ◽  
Time Varying ◽  
Square Root ◽  
Maximum Increase ◽  
Target Vehicle ◽  
Motion State ◽  
Noise Statistic ◽  
Cubature Kalman Filter ◽  
Non Linear ◽  
Vehicle Motion

In the advanced driver assistance system (ADAS), millimeter-wave radar is an important sensor to estimate the motion state of the target-vehicle. In this paper, the estimation of target-vehicle motion state includes two parts: the tracking of the target-vehicle and the identification of the target-vehicle motion state. In the unknown time-varying noise, non-linear target-vehicle tracking faces the problem of low precision. Based on the square-root cubature Kalman filter (SRCKF), the Sage–Husa noise statistic estimator and the fading memory exponential weighting method are combined to derive a time-varying noise statistic estimator for non-linear systems. A method of classifying the motion state of the target vehicle based on the time window is proposed by analyzing the transfer mechanism of the motion state of the target vehicle. The results of the vehicle test show that: (1) Compared with the Sage–Husa extended Kalman filtering (SH-EKF) and SRCKF algorithms, the maximum increase in filtering accuracy of longitudinal distance using the improved square-root cubature Kalman filter (ISRCKF) algorithm is 45.53% and 59.15%, respectively, and the maximum increase in filtering the accuracy of longitudinal speed using the ISRCKF algorithm is 23.53% and 29.09%, respectively. (2) The classification and recognition results of the target-vehicle motion state are consistent with the target-vehicle motion state.

Square Root Cubature Particle Filter

2011 ◽  
Vol 219-220 ◽  
pp. 727-731 ◽  
Author(s):  
Jing Mu ◽  
Yuan Li Cai ◽  
Jun Min Zhang
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
State Estimation ◽  
Numerical Stability ◽  
Current Measurement ◽  
Square Root ◽  
Proposal Distribution ◽  
Unscented Particle Filter ◽  
Cubature Kalman Filter ◽  
Simulation Results

The square root cubature particle filter (SRCPF) uses the square root cubature Kalman filter (SRCKF) for generating the proposal distribution. The SRCPF algorithm is easy to be implemented and has numerical stability. Moreover, the SRCKF based proposal distribution approximates the optimal importance distribution by incorporating the current measurement. Simulation results demonstrate that the SRCPF algorithm has the better performance for state estimation than the generic particle filter (GPF), extended particle filter (EPF) and unscented particle filter (UPF), and its calculation cost decreases largely.

Sign in / Sign up

Export Citation Format

Share Document