Search results for "Invariant extended Kalman filter"
showing 10 items of 11 documents
Adaptive-gain extended Kalman filter: Extension to the continuous-discrete case
2009
In the present article we propose a nonlinear observer that merges the behaviors 1) of an extended Kalman filter, mainly designed to smooth off noise , and 2) of high-gain observers devoted to handle large perturbations in the state estimation. We specifically aim at continuous-discrete systems. The strategy consists in letting the high-gain self adapt according to the innovation. We define innovation computed over a time window and justify its usage via an important lemma. We prove the general convergence of the resulting observer.
Adaptive Consensus-Based Distributed Kalman Filter for WSNs with Random Link Failures
2016
Wireless Sensor Networks have emerged as a very powerful tool for the monitoring and control, over large areas, of diverse phenomena. One of the most appealing properties of these networks is their potentiality to perform complex tasks in a total distributed fashion, without requiring a central entity. In this scenario, where nodes are constrained to use only local information and communicate with one-hop neighbors, iterative consensus algorithms are extensively used due to their simplicity. In this work, we propose the design of a consensus-based distributed Kalman filter for state estimation, in a sensor network whose connections are subject to random failures. As a result of this unrelia…
Using an Adaptive High-Gain Extended Kalman Filter With a Car Efficiency Model
2010
The authors apply the Adaptive High-Gain Extended Kalman Filter (AEKF) to the problem of estimating engine efficiency with data gathered from normal driving. The AEKF is an extension of the traditional Kalman Filter that allows the filter to be reactive to perturbations without sacrificing noise filtering. An observability normal form of the engine efficiency model is developed for the AEKF. The continuous-discrete AEKF is presented along with strategies for dealing with asynchronous data. Empiric test results are presented and contrasted with EKF-derived results.Copyright © 2010 by ASME
Speed and rotor flux estimation of induction motors via on-line adjusted extended kalman filter
2006
This paper deals with the estimation of speed and rotor flux of induction motors via Extended Kalman Filter (EKF) with on-line adjusting of the system noise covariance matrix. The predictor of EKF consists of a discrete time model obtained by means of a second order discretization of the original nonlinear model of the induction motor. In order to obtain accurate estimation of the above mentioned variables, the load torque is included in the state variables and then estimated. Three different system noise models are also illustrated and compared each other by simulations carried out in Matlab/Simulink environment. For one of these models, EKF is adjusted on-line by means of an additional PI…
Descriptor-type Robust Kalman Filter and Neural Adaptive Speed Estimation Scheme for Sensorless Control of Induction Motor Drive Systems
2012
Abstract This paper deals with robust estimation of speed and rotor flux for sensorless control of motion control systems which use induction motors as actuators. Due to the observability lack of five and six order Extended Kalman Filters, speed is here estimated by means of a Total Least Square algorithm with Neural Adaptive mechanism. This allows the use of a fourth-order Kalman Filter for estimating rotor flux and to filter stator currents. To cope with motor-load parameter variations, a descriptor-type robust Kalman Filter is designed taking explicitly into account these variations. The descriptor-type structure allows direct translation of parameter variations into variations of the co…
Robustness Analysis of an Extended Kalman Filter for Sensorless Control of Induction Motors
2010
This paper deals with robustness analysis of Extended Kalman Filters (EKFs) for sensorless motion control of induction motors. Analysis is carried out by means of simulation experiments considering a conventional EKF, in which system and measurement noise covariance matrices are constant, and an adaptive EKF in which the system noise covariance matrix is updated on-line using a PID-type algorithm driven by the stator current estimation errors.
Convergence analysis of cubature Kalman filter
2014
This paper investigates the stability analysis of cubature Kalman filter (CKF) for nonlinear systems with linear measurement. The certain conditions to ensure that the estimation error of CKF remains bounded are proved. Then, the effect of process noise covariance is investigated and an adaptive process noise covariance is proposed to deal with large estimation error. Accordingly, a modified CKF (MCKF) is developed to enhance the stability and accuracy of state estimation. The performance of the MCKF is compared to the CKF by two case studies. Simulation results demonstrate that the large estimation error may lead to instability of CKF while the MCKF is successfully able to estimate the sta…
Invariant pattern recognition based on 1-D Wavelet functions and the polynomial decomposition
1997
Abstract A new filter, consisting of 1-D Wavelet functions is suggested for achieving optical invariant pattern recognition. The formed filter is actually a real function, hence, it is theoretically possible to be implemented under both spatially coherent and spatially incoherent illuminations. The filter is based on the polynomial expansion, and is constructed out of a scaled bank of filters multiplied by 1-D Wavelet weight functions. The obtained output is shown to be invariant to 2-D scaling even when different scaling factors are applied on the different axes. The computer simulations and the experimental results demonstrate the potential hidden in this technique.
Extended Kalman Filter for sensorless control of induction motors
2010
This paper deals with speed and rotor flux estimation of induction motors via Extended Kalman Filter (EKF). The filter is designed starting from a discrete time model obtained by means of a first order discretization of the original nonlinear model of the induction motor (IM). In order to obtain accurate estimation of the above mentioned variables, the load torque is included into the state variables and then estimated, thus constructing a sixth order EKF. Experimental results are shown with reference to a closed loop sensorless control system, consisting of a 750 W induction motor supplied by a voltage source inverter, a cascade controller consisting of four PI control loops and the design…
Supercapacitor diagnosis using an Extended Kalman Filtering approach
2016
This paper deals with the model-based analysis of a Supercapacitor for diagnostic purposes. A two legs nonlinear physical model is assumed for the Supercapacitor and the corresponding second-order nonlinear state-space mathematical model is obtained. Then, an Extended Kalman Filter is tuned so that the estimated outputs reproduce the voltages at the equivalent capacitance terminals; they give information on the state of health of the supercapacitor but are not directly measurable. In particular, an optimization problem is firstly formulated, involving the experimental input-output data and those given by the Extended Kalman Filter.