Edwin Engin Yaz

Brief paper: Robust minimum variance linear state estimators for multiple sensors with different failure rates

Linear minimum variance unbiased state estimation is considered for systems with uncertain parameters in their state space models and sensor failures. The existing results are generalized to the case where each sensor may fail at any sample time independently of the others. For robust performance, stochastic parameter perturbations are included in the system matrix. Also, stochastic perturbations are allowed in the estimator gain to guarantee resilient operation. An illustrative example is included to demonstrate performance improvement over the Kalman filter which does not include sensor failures in its measurement model.

Sliding-Mode Adaptive Observer Approach to Chaotic Synchronization

Extensions of sliding-mode adaptive observer are presented for state reconstruct nonlinear systems with uncertainty having unknown bounds. The observer uses non gains that are smoothened versions of classical sliding-mode gains and they are co ously updated to guarantee a globally stable observation error. This observer is ap to Chua’s circuit in a chaotic synchronization scheme. A generalization to known w form type disturbances and measurement uncertainties is pointed out. @S0022-0434 ~00!02304-2#

Recursive estimator for linear and nonlinear systems with uncertain observations

Abstract The state estimation problem with observations which may or may not contain a signal at any sample time is considered from a covariance assignment viewpoint. The closed form solution for directly assigning steady state estimation error covariances and their assignability conditions are derived for the linear case. For the nonlinear case, upper bounds on the estimation error covariance are assigned. Examples are given for illustration in which the robustness of the proposed schemes are assessed.

Observer design for discrete and continuous non-linear stochastic systems

An observer design methodology that is applicable to a general class of non-linear stochastic system and measurement models is given. It is proved that, under the conditions given, discrete- and continuous-time state estimation is possible with guaranteed exponential rate of convergence. The superior performance of the observer is illustrated with two examples.

Minimum variance generalized state estimators for multiple sensors with different delay rates

In this paper, linear minimum variance unbiased state estimation is considered for signals with sensor delay. The solutions that have been proposed for the sensor delay problem so far only involve sensors with identical delay characteristics. However, in a true sensor network system, there may be multiple sensors which may not have the same delay characteristics. Therefore, the main goal of this research is to extend and generalize the existing solutions by modeling multiple sensors having different delay characteristics. The probability of occurrence of the delay is assumed to be known from the queuing characteristics. Illustrative examples are provided to support the theory developed in this work. Simulation comparison of the solution developed in this framework to the traditional Kalman Filter shows superiority and efficiency of our technique in the case of sensor delay. Furthermore, the robustness of the proposed method is also shown by simulations.

Nonlinear observer design with general criteria

A class of nonlinear system and measurement equations involving incrementally conic nonlinearities with finite energy disturbances is considered. A linear matrix inequality based observer design approach is presented that guarantees the satisfaction of a variety of performance criteria ranging from simple estimation error boundedness to dissipativity. Simple simulation examples are included to explore the freedom in design and to illustrate and provide support to the proposed design methodology.

On LMI formulations of some problems arising in nonlinear stochastic system analysis

This work involves formulation of some problems arising in the analysis of a class of discrete-time nonlinear stochastic systems in terms of linear matrix inequalities. This allows one to utilize a wide variety of numerical techniques available for tackling both the feasibility problem and the actual numerical solution in an efficient manner.

State estimation of uncertain nonlinear stochastic systems with general criteria

Abstract A general class of discrete-time uncertain nonlinear stochastic systems corrupted by finite energy disturbances and estimation performance criteria are considered. These performance criteria include guaranteed-cost suboptimal versions of estimation objectives like H2, H∞, stochastic passivity, etc. Linear state estimators that satisfy these criteria are presented. A common matrix inequality formulation is used in characterization of estimator design equations.

A Reconfigurable Motor for Experimental Emulation of Stator Winding Interturn and Broken Bar Faults in Polyphase Induction Machines

The benefits and drawbacks of a 5-hp reconfigurable induction motor, which was designed for experimental emulation of stator winding interturn and broken rotor bar faults, are presented in this paper. It was perceived that this motor had the potential of quick and easy reconfiguration to produce the desired stator and rotor faults in a variety of different fault combinations. Hence, this motor was anticipated to make a useful test bed for evaluation of the efficacy of existing and new motor fault diagnostics techniques and not the study of insulation failure mechanisms. Accordingly, it was anticipated that this reconfigurable motor would eliminate the need to permanently destroy machine components such as stator windings or rotor bars when acquiring data from a faulty machine for fault diagnostic purposes. Experimental results under healthy and various faulty conditions are presented in this paper, including issues associated with rotor bar-end ring contact resistances that showed the drawbacks of this motor in so far as emulation of rotor bar breakages. However, emulation of stator-turn fault scenarios was successfully accomplished.

Robust and resilient state-dependent control of continuous-time nonlinear systems with general performance criteria

A novel state-dependent control approach for discrete-time nonlinear systems with general performance criteria is presented. This controller is robust for unstructured model uncertainties, resilient against bounded feedback control gain perturbations in achieving optimality for general performance criteria to secure quadratic optimality with inherent asymptotic stability property together with quadratic dissipative type of disturbance reduction. For the system model, unstructured uncertainty description is assumed, which incorporates commonly used types of uncertainties, such as norm-bounded and positive real uncertainties as special cases. By solving a state-dependent linear matrix inequality (LMI) at each time step, sufficient condition for the control solution can be found which satisfies the general performance criteria. The results of this paper unify existing results on nonlinear quadratic regulator, H∞ and positive real control to provide a novel robust control design. The effectiveness of the proposed technique is demonstrated by simulation of the control of inverted pendulum.