Practical considerations for implementing the discrete extended Kalman filter in real time with a digital signal processor are discussed. The system considered is a permanent magnet synchronous motor (PMSM) without a position sensor, and the extended Kalman filter is designed for the online estimation of the speed and rotor position by only using measurements of the motor voltages and currents. The algorithms developed to allow efficient computation of the filter are presented. The computational techniques used to simplify the filter equations and their implementation in fixed-point arithmetic are discussed. Simulation and experimental results are presented to demonstrate the feasibility of this estimation process. >

Design and implementation of an extended Kalman filter for the state estimation of a permanent magnet synchronous motor

The vector control of an induction motor by speed estimation using an extended Kalman filter (EKF) is described. The rotor speed is regarded as a parameter, and the composite state is composed of the original state and the rotor speed. The EKF is employed to identify the speed of an induction motor and rotor magnetic flux based on the measured quantities such as stator currents and DC link voltage. The estimated speed is used for vector control and overall speed control. The current control is performed in the synchronous rotating reference frame, and the estimated speed information is used for reference frame transformation of the current controller. Computer simulation of the speed control has been carried out to confirm the usefulness of the speed estimation algorithm. The error between the real speed and the estimated speed is within a few RPM even in the low speed range of about 20 RPM. >

Speed sensorless vector control of an induction motor using an extended Kalman filter

The primary idea behind deploying sensor networks is to utilize the distributed sensing capability provided by tiny, low powered, and low cost devices. Multiple sensing devices can be used cooperatively and collaboratively to capture events or monitor space more effectively than a single sensing device. The realm of applications envisioned for sensor networks is diverse including military, aerospace, industrial, commercial, environmental, and health monitoring. Typical examples include: traffic monitoring of vehicles, cross-border infiltration detection and assessment, military reconnaissance and surveillance, target tracking, habitat monitoring, and structure monitoring, to name a few. Most of the applications envisioned with sensor networks demand highly reliable, accurate, and fault-tolerant data acquisition process. In this paper, we focus on innovative approaches to deal with multivariable, multispace problem domains (data integrity, energy-efficiency, and fault-tolerant framework) in wireless sensor networks and present novel ideas that have practical implementation in developing power-aware software components for designing robust networks of sensing devices.

/pdf/hierarchical-aggregation-and-intelligent-monitoring-and-12u8mn90au.pdf

Hierarchical Aggregation and Intelligent Monitoring and Control in Fault-Tolerant Wireless Sensor Networks

The extended Kalman filter (EKF) can be used for the purpose of training nonlinear neural networks to perform desired input-output mappings. To improve the computational requirements of the EKF, Puskorius proposed the decoupled EKF (DEKF) as a practical remedy for the proper management of computational resources. This approach, however, sacrifices computational accuracy of estimates because it ignores the interactions between the estimates of mutually exclusive weights. To overcome such a limitation, therefore, we proposed hybrid implementation based on EKF (HEKF) for respiratory motion estimation, which uses the channel number for the mutually exclusive groups and the coupling technique to compensate the computational accuracy. Moreover, the authors restricted to a DEKF algorithm in which the weights connecting the inputs to a node are grouped together. If there are multiple input training sequences with respect to the time stamp, the complexity can increase by the power of the input channel number. To improve the computational complexity, we split the complicated neural network into a couple of simple neural networks to adjust separate input channels. The experimental results validated that the prediction overshoot of the proposed HEKF was improved by 62.95% in the average prediction overshoot values. The proposed HEKF showed a better performance of 52.40% improvement in the average of the prediction time horizon. We have evaluated that the proposed HEKF can outperform DEKF by comparing the prediction overshoot values, the performance of the tracking estimation value, and the normalized root-mean-squared error.

/pdf/respiratory-motion-estimation-with-hybrid-implementation-of-5fmaip0ly5.pdf

Respiratory Motion Estimation With Hybrid Implementation of Extended Kalman Filter

The method is based on an adaptive flux observer in the rotor-speed reference frame, in which a second-order Kalman filter is employed to modify the estimated rotor flux to improve the performance of speed estimation. The Kalman filter modifies the estimated rotor flux based on the measured stator currents. The estimated speed is used in the speed feedback for vector control and in the co-ordinate transformation for current controller. The proposed method has the advantage of saving much computation time in comparison with the reduced-order extended Kalman filter. Compared with the conventional adaptive observer, the proposed method has the advantage of better accuracy to follow the speed command under heavy loads. Experimental results show the effectiveness of the proposed method.

/pdf/observer-based-speed-estimation-method-for-sensorless-vector-2m6x6pa6uh.pdf

Observer-based speed estimation method for sensorless vector control of induction motors

A Kalman filter for tracking moving objects has been implemented on a TMS32010 digital signal processor. Tracking accuracy and quantization effects of the implementation have been measured by comparing the filter to one implemented on a general-purpose computer with a 32 bit word length. The filter design has been optimized to minimize the program memory requirements and execution speed. Although the filter has been implemented on a specific signal processing chip, the design is general enough to be applicable to any other digital signal processor. The filter can be used for tracking objects for industrial or other applications where range and bearing measurements are available. For motion on a plane, the filter can be used to track objects where the maximum system bandwidth is 1680 Hz; for three-dimensional motion the system bandwidth is 1120 Hz. Using the approach presented, higher system bandwidth can be accommodated through higher-speed digital signal processors. >

N. Kyriakopoulos

Papers

Implementation of a tracking Kalman filter on a digital signal processor