Framework for state and unknown input estimation of linear time-varying systems

The design of unknown-input decoupled observers and filters requires the assumption of an existence condition in the literature. This paper addresses an unknown input filtering problem where the existence condition is not satisfied. Instead of designing a traditional unknown input decoupled filter, a Double-Model Adaptive Estimation approach is extended to solve the unknown input filtering problem. It is proved that the state and the unknown inputs can be estimated and decoupled using the extended Double-Model Adaptive Estimation approach without satisfying the existence condition. Numerical examples are presented in which the performance of the proposed approach is compared to methods from literature.Comment: This paper has been accepted by Automatica. It considers unknown input estimation or fault and disturbances estimation. Existing approaches considers the case where the effects of fault and disturbance can be decoupled. In our paper, we consider the case where the effects of fault and disturbance are coupled. This approach can be easily extended to nonlinear system

Invariant EKF Design for Scan Matching-aided Localization

Localization in indoor environments is a technique which estimates the robot's pose by fusing data from onboard motion sensors with readings of the environment, in our case obtained by scan matching point clouds captured by a low-cost Kinect depth camera. We develop both an Invariant Extended Kalman Filter (IEKF)-based and a Multiplicative Extended Kalman Filter (MEKF)-based solution to this problem. The two designs are successfully validated in experiments and demonstrate the advantage of the IEKF design

Real-time flutter identification

The techniques and a FORTRAN 77 MOdal Parameter IDentification (MOPID) computer program developed for identification of the frequencies and damping ratios of multiple flutter modes in real time are documented. Physically meaningful model parameterization was combined with state of the art recursive identification techniques and applied to the problem of real time flutter mode monitoring. The performance of the algorithm in terms of convergence speed and parameter estimation error is demonstrated for several simulated data cases, and the results of actual flight data analysis from two different vehicles are presented. It is indicated that the algorithm is capable of real time monitoring of aircraft flutter characteristics with a high degree of reliability

System identification of a free floating telerobot using Kalman filtering and a stereoscopic vision sensor

Due to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to [email protected], referencing the URI of the item.Includes bibliographical references (leaves 78-79).Issued also on microfiche from Lange Micrographics.A telerobot has been acquired that floats on air bearings and is intended to simulate the dynamics of a spacecraft in a two-dimensional plane. The robot was delivered without a computer, sensors or documentation so an effort has been launched to determine how the apparatus worked and to identify the model parameters associated with mass, moment of inertia and thrust. The robot has been modified to accommodate a laptop as the onboard computer and a unique stereoscopic vision sensor as a navigation system. The unknown model parameters are then identified using both least squares estimation and Kalman filtering. The unique stereoscopic vision sensor system is based on one-dimensional position sensing diodes (PSD's) and active targets that broadcast a modulated signal. The active targets are mounted at known points in the robot frame of reference and broadcast their signal to the PSD sensors that are stationary in the inertial coordinate frame. This system enables real-time attitude measurements with no moving parts. The robot's mass, moment of inertia and the forces generated by its thrusters are identified using direct measurements and the well known linear least squares estimation algorithm. Identification using these techniques required experiments specifically designed to characterize the system. Some of the parameters may change over time, so a means of conducting on-line system identification was developed. A Kalman filter was designed which could simultaneously perform state estimation and parameter identification on-line. This technique did not require an experiment specifically designed for identification purposes and could accurately find the unknown model parameters during normal robot maneuvering

A modified extended kalman filter as a parameter estimator for linear discrete-time systems

This thesis presents the derivation and implementation of a modified Extended Kalman Filter used for Joint state and parameter estimation of linear discrete-time systems operating in a, stochastic Gaussian environment. A novel derivation for the discrete-time Extended Kalman Filter is also presented. In order to eliminate the main deficiencies of the Extended Kalman Filter, which are divergence and biasedness of its estimates, the filter algorithm has been modified. The primary modifications are due to Ljung, who stated global convergence properties for the modified Extended Kalman Filter, when used as a parameter estimator for linear systems. Implementation of this filter is further complicated by the need to initialize the parameter estimate error covariance inappropriately small, to assure filter stability. In effect, due to this inadequate initialization process the parameter estimates fail to converge. Several heuristic methods have been developed to remove the effects of the inadequate initial parameter estimate covariance matrix on the filter\u27s convergence properties. Performance of the improved modified Extended Kalman Filter is compared with the Recursive Extended Least Squares parameter estimation scheme

Glucose-Insulin regulator for type 1 diabetes using high order neural networks

In this paper a Glucose-Insulin regulator for Type 1 Diabetes using artificial neural networks (ANN) is proposed. This is done using a discrete recurrent high order neural network in order to identify and control a nonlinear dynamical system which represents the pancreas? beta-cells behavior of a virtual patient. The ANN which reproduces and identifies the dynamical behavior system, is configured as series parallel and trained on line using the extended Kalman filter algorithm to achieve a quickly convergence identification in silico. The control objective is to regulate the glucose-insulin level under different glucose inputs and is based on a nonlinear neural block control law. A safety block is included between the control output signal and the virtual patient with type 1 diabetes mellitus. Simulations include a period of three days. Simulation results are compared during the overnight fasting period in Open-Loop (OL) versus Closed- Loop (CL). Tests in Semi-Closed-Loop (SCL) are made feedforward in order to give information to the control algorithm. We conclude the controller is able to drive the glucose to target in overnight periods and the feedforward is necessary to control the postprandial period