0000000000247909
AUTHOR
Bassam Bamieh
Identification for a general class of LPV Models
Abstract In this paper we consider the problem of identifying discrete-time Linear Parameter Varying (LPV) models of non-linear or time-varying systems. LPV models are considered for their connection with the industrial practice of gain-scheduling. We assume that inputs, outputs and the scheduling parameters are measured, and a form of the functional dependence of the coefficients on the parameters is known. We show how the identification problem can be reduced to a linear regression so that a Least Mean Square identification algorithm can be reformulated. Conditions on the persistency of excitation in terms of the inputs and parameter trajectories are given to ensure the consistency of the…
Identification of linear parameter varying models
We consider identification of a certain class of discrete-time nonlinear systems known as linear parameter varying system. We assume that inputs, outputs and the scheduling parameters are directly measured, and a form of the functional dependence of the system coefficients on the parameters is known. We show how this identification problem can be reduced to a linear regression, and provide compact formulae for the corresponding least mean square and recursive least-squares algorithms. We derive conditions on persistency of excitation in terms of the inputs and scheduling parameter trajectories when the functional dependence is of polynomial type. These conditions have a natural polynomial i…
LPV Model Identification For The Stall And Surge Control of a Jet Engine
Abstract The problem of identifying discrete-time Linear Parameter Varying (LPV) models of non-linear or time-varying systems for gain scheduling control is considered assuming that inputs, outputs and the scheduling parameters are measured, and a form of the functional dependence of the coefficients on the parameters is known. The identification procedure is applied to the controlled model of compressors for jet engines. The model is controlled in order to avoid rotating stall and surge. Aim of the present paper is to identify the LPV model based on the nonlinear model of compressors in order to design a robust gain scheduling predictive controller.
LPV models: Identification for gain scheduling control
In this paper the use of discrete-time Linear Parameter Varying (LPV) models for the gain scheduling control and identification methods for non-linear or time-varying system is considered. We report an overview on the existing literature on LPV systems for gain scheduling control and identification. Moreover, assuming that inputs, outputs and the scheduling parameters are measured, and a form of the functional dependence of the coefficients on the parameters is known, we show how the identification problem can be reduced to a linear regression so that a Least Mean Square and Recursive Least Square identification algorithm can be reformulated. Our methodology is applied for the identificatio…
Identification of linear parameter varying models
We consider the problem of identifying discrete-time linear parameter varying models of nonlinear or time-varying systems. We assume that inputs, outputs and the scheduling parameters are measured, and a form of the functional dependence of the coefficients on the parameters. We show how the identification problem can be reduced to a linear regression, and we give conditions on persistency of excitation in terms of the inputs and parameter trajectories.
LPV model identification for gain scheduling control: An application to rotating stall and surge control problem
Abstract We approach the problem of identifying a nonlinear plant by parameterizing its dynamics as a linear parameter varying (LPV) model. The system under consideration is the Moore–Greitzer model which captures surge and stall phenomena in compressors. The control task is formulated as a problem of output regulation at various set points (stable and unstable) of the system under inputs and states constraints. We assume that inputs, outputs and scheduling parameters are measurable. It is worth pointing out that the adopted technique allows for identification of an LPV model's coefficients without the requirements of slow variations amongst set points. An example of combined identification…
Regularized LMS methods for baseline wandering removal in wearable ECG devices
The acquisition of electrocardiogram (ECG) signals by means of light and reduced size devices can be usefully exploited in several health-care applications, e.g., in remote monitoring of patients. ECG signals, however, are affected by several artifacts due to noise and other disturbances. One of the major ECG degradation is represented by the baseline wandering (BW), a slowly varying change of the signal trend. Several BW removal algorithms have been proposed into the literature, even though their complexity often hinders their implementation into wearable devices characterized by limited computational and memory resources. In this study, we formalize the BW removal problem as a mean-square…
On Discovering Low Order Models in Biochemical Reaction Kinetics
We develop a method by which a large number of differential equations representing biochemical reaction kinetics may be represented by a smaller number of differential equations. The basis of our technique is a conjecture that the high dimension equations of biochemical kinetics, which involve reaction terms of specific forms, are actually implementing a low dimension system whose behavior requires right hand sides that can not be biochemically implemented. For systems that satisfy this conjecture, we develop a simple approximation scheme based on multilinear algebra that extracts the low dimensional system from simulations of the high dimension system. We demonstrate this technique on a st…