Adaptive Estimation An adaptive estimator is an efficient estimator for a model that is only partially specified. For example, consider estimating a parameter that describes a sample of observations drawn from a distribution F . One natural question is: Is it possible that an estimator of the parameter constructed without knowledge of F could be as efficient (asymptotically) as any well-behaved...

An adaptive High-Gain observer (AHG) as well as an Extended (EKF) and Unscented Kalman filter (UKF) are implemented for joint state and parameter estimation of a novel multi-axial electromagnetically actuated punch. These observers are compared in terms of convergence and response time to erroneous parameter and state initialization, as well as parameter modifications during operation. The AHG ...

The increased power of small computers makes the use of parameter estimation methods attractive. Such methods have a number of uses in analytical chemistry. When valid models are available, many methods work well, but when models used in the estimation are in error, most methods fail. Methods based on the Kalman filter, a linear recursive estimator, may be modified to perform parameter estimati...

The image restoration via Reduced Order Model Kalman Filter (ROMKF) is accomplished in conjunction with a maximum likelihood technique for image/blur parameter estimation purposes. Traditionally, one uses initial condition sensitive optimization algorithms at the estimation stage. This work concerns the use of Evolutionary Programming (EP) in parameter estimation phase of the ROMKF space-adapti...

We introduce a novel motion estimation approach for Echo PIV for the laminar and steady flow model. We mathematically formalize the motion estimation problem as a parametrization of a dictionary of particle trajectories by the physical flow parameter. We iteratively refine this unknown parameter by subsequent sparse approximations. We show smoothness of the adaptive flow dictionary that is a ke...

A supersymmetric class of free damped oscillators with three parameters has been obtained in 1998 by Rosu and Reyes through the factorization of the Newton equation. The supplementary parameter is the integration constant of the general Riccati solution. The estimation of the latter parameter is performed here by employing the recent adaptive observer scheme of Besançon et al., but applied in a...

The permanent magnet synchronous motor is increasingly playing an important role in advanced motor drives. An encoder or resolver attached to the shaft of the motor typically supplies the feedbacks required for motor speed control. Many advanced motor drives cannot tolerate the use of these feedback devices because of reliability concerns in a harsh environment, price, space and weight limitati...

In this paper, the design and implementation of adaptive speed controller for a sensorless synchronous reluctance motor (SynRM) drive system is proposed. A combination of well-known adaptive input-output feedback linearization (AIOFL) and adaptive backstepping (ABS) techniques are used for speed tracking control of SynRM. The AIOFL controller is capable of estimating motor two-axis inductances ...

We investigate the adaptive hybrid synchronization problem for a new hyperchaotic system with uncertain parameters. Based on the passivity theory and the adaptive control theory, corresponding controllers and parameter estimation update laws are proposed to achieve hybrid synchronization between two identical uncertain hyperchaotic systems with different initial values, respectively. Numerical ...

State of charge (SOC) estimation is essential to battery management systems in electric vehicles (EVs) to ensure the safe operations of batteries and providing drivers with the remaining range of the EVs. A number of estimation algorithms have been developed to get an accurate SOC value because the SOC cannot be directly measured with sensors and is closely related to various factors, such as a...