3,451 research outputs found
A New Recursive Least-Squares Method with Multiple Forgetting Schemes
We propose a recursive least-squares method with multiple forgetting schemes
to track time-varying model parameters which change with different rates. Our
approach hinges on the reformulation of the classic recursive least-squares
with forgetting scheme as a regularized least squares problem. A simulation
study shows the effectiveness of the proposed method
Recursive least squares for online dynamic identification on gas turbine engines
Online identification for a gas turbine engine is vital for health
monitoring and control decisions because the engine electronic
control system uses the identified model to analyze the performance
for optimization of fuel consumption, a response to the pilot
command, as well as engine life protection. Since a gas turbine engine
is a complex system and operating at variant working conditions, it
behaves nonlinearly through different power transition levels and at
different operating points. An adaptive approach is required to capture
the dynamics of its performance
Matrix estimation using matrix forgetting factor and instrumental variable for nonstationary sequences with time variant matrix gain
Consider us the problem of time-varying parameter estimation. The most immediate and simple idea is to include a discounting procedure in an estimation algorithm i.e., a procedure for discarding (forgetting) old information. The most common way to do is to introduce an exponential forgetting factor (FF) into the corresponding estimation procedure (to see: Ljung and Gunnarson (1990)).
In this paper, the authors going to describe a good enough estimator considering a system with nonstationary time variant properties with respect to input and output qualities. The techniques used are Instrumental Variable (IV) and Matrix Forgetting Factor (MFF). The results previously obtained by (Poznyak and Medel 1999a, 1999b) were the basis of this paper. The theoretical description illustrates the advantages with respect to others filters below cited.Eje: IV - Workshop de procesamiento distribuido y paraleloRed de Universidades con Carreras en Informática (RedUNCI
Matrix estimation using matrix forgetting factor and instrumental variable for nonstationary sequences with time variant matrix gain
Consider us the problem of time-varying parameter estimation. The most immediate and simple idea is to include a discounting procedure in an estimation algorithm i.e., a procedure for discarding (forgetting) old information. The most common way to do is to introduce an exponential forgetting factor (FF) into the corresponding estimation procedure (to see: Ljung and Gunnarson (1990)).
In this paper, the authors going to describe a good enough estimator considering a system with nonstationary time variant properties with respect to input and output qualities. The techniques used are Instrumental Variable (IV) and Matrix Forgetting Factor (MFF). The results previously obtained by (Poznyak and Medel 1999a, 1999b) were the basis of this paper. The theoretical description illustrates the advantages with respect to others filters below cited.Eje: IV - Workshop de procesamiento distribuido y paraleloRed de Universidades con Carreras en Informática (RedUNCI
Recursive Least Squares Filtering Algorithms for On-Line Viscoelastic Characterization of Biosamples
The mechanical characterization of biological samples is a fundamental issue in biology
and related fields, such as tissue and cell mechanics, regenerative medicine and diagnosis of diseases.
In this paper, a novel approach for the identification of the stiffness and damping coefficients
of biosamples is introduced. According to the proposed method, a MEMS-based microgripper
in operational condition is used as a measurement tool. The mechanical model describing the
dynamics of the gripper-sample system considers the pseudo-rigid body model for the microgripper,
and the Kelvin–Voigt constitutive law of viscoelasticity for the sample. Then, two algorithms based
on recursive least square (RLS) methods are implemented for the estimation of the mechanical
coefficients, that are the forgetting factor based RLS and the normalised gradient based RLS
algorithms. Numerical simulations are performed to verify the effectiveness of the proposed approach.
Results confirm the feasibility of the method that enables the ability to perform simultaneously two
tasks: sample manipulation and parameters identification
Identification of nonlinear time-varying systems using an online sliding-window and common model structure selection (CMSS) approach with applications to EEG
The identification of nonlinear time-varying systems using linear-in-the-parameter models is investigated. A new efficient Common Model Structure Selection (CMSS)
algorithm is proposed to select a common model structure. The main idea and key procedure is: First, generate K 1 data sets (the first K data sets are used for training, and theK 1 th one is used for testing) using an online sliding window method; then detect significant model terms to form a common model structure which fits over all the K
training data sets using the new proposed CMSS approach. Finally, estimate and refine the time-varying parameters for the identified common-structured model using a Recursive Least Squares (RLS) parameter estimation method. The new method can effectively detect and adaptively track the transient variation of nonstationary signals. Two examples are presented to illustrate the effectiveness of the new approach including an application to an EEG data set
A Supervisor for Control of Mode-switch Process
Many processes operate only around a limited number of operation points. In order to have adequate control around each operation point, and adaptive controller could be used. When the operation point changes often, a large number of parameters would have to be adapted over and over again. This makes application of conventional adaptive control unattractive, which is more suited for processes with slowly changing parameters. Furthermore, continuous adaptation is not always needed or desired. An extension of adaptive control is presented, in which for each operation point the process behaviour can be stored in a memory, retrieved from it and evaluated. These functions are co-ordinated by a ¿supervisor¿. This concept is referred to as a supervisor for control of mode-switch processes. It leads to an adaptive control structure which quickly adjusts the controller parameters based on retrieval of old information, without the need to fully relearn each time. This approach has been tested on experimental set-ups of a flexible beam and of a flexible two-link robot arm, but it is directly applicable to other processes, for instance, in the (petro) chemical industry
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