Piece-Wise Linear Dynamic Systems With One-Way Clutches

2004 ◽  
Vol 127 (5) ◽  
pp. 475-482 ◽  
Author(s):  
Eric M. Mockensturm ◽  
Raghavan Balaji
Keyword(s):  
Dynamic Systems ◽  
Power Transmission ◽  
Piezoelectric Actuators ◽  
Belt Drive ◽  
Linear Dynamic Systems ◽  
System Parameters ◽  
Belt Drives ◽  
Linear Dynamic ◽  
Serpentine Belt ◽  
Simple Systems

One-way clutches and clutch bearings are being used in a wide variety of dynamic systems. Motivated by their recent use as ratchets in piezoelectric actuators and decoupling devices in serpentine belt drives, a method of analysis of systems containing one-way clutches is presented. Two simple systems are analyzed. The goal of the first is the power transmission which would be of concern in an actuator. The goal of the second is decoupling large inertia elements to reduce loads in an oscillating system, the objective of the clutch in a serpentine belt drive. Results show how system parameters can be tuned to meet the desired performance of these piece-wise linear systems.

scholarly journals Identification of Two-Time Scaled Systems Using Prefilters

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Anderson L. O. Cavalcanti ◽  
Karl H. Kienitz ◽  
Visakan Kadirkamanathan
Keyword(s):  
Iterative Algorithm ◽  
Dynamic Systems ◽  
Time Scale ◽  
Important Class ◽  
Wavelet Filters ◽  
Linear Dynamic Systems ◽  
System Parameters ◽  
Linear Dynamic ◽  
Multiscale Systems ◽  
Identification Processes

This paper deals with the identification of two-time scale linear dynamic systems, which are an important class of multiscale systems. Classical identification processes may fail to yield accurate parameters for systems of this class and, for this reason, the authors propose two different techniques to estimate the system parameters. The first technique utilizes two prefilters that are iteratively tuned. The second one considers wavelet filters that are tuned based on the results of the first iterative algorithm. Identification and analysis results for a dynamical aircraft model are shown to demonstrate the algorithm’s performance.

scholarly journals On the Uncertainty Identification for Linear Dynamic Systems Using Stochastic Embedding Approach with Gaussian Mixture Models

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3837
Author(s):  
Rafael Orellana ◽  
Rodrigo Carvajal ◽  
Pedro Escárate ◽  
Juan C. Agüero
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Dynamic Systems ◽  
Gaussian Mixture ◽  
Error Model ◽  
Fault Detection And Diagnosis ◽  
Deterministic Models ◽  
Linear Dynamic Systems ◽  
Linear Dynamic

In control and monitoring of manufacturing processes, it is key to understand model uncertainty in order to achieve the required levels of consistency, quality, and economy, among others. In aerospace applications, models need to be very precise and able to describe the entire dynamics of an aircraft. In addition, the complexity of modern real systems has turned deterministic models impractical, since they cannot adequately represent the behavior of disturbances in sensors and actuators, and tool and machine wear, to name a few. Thus, it is necessary to deal with model uncertainties in the dynamics of the plant by incorporating a stochastic behavior. These uncertainties could also affect the effectiveness of fault diagnosis methodologies used to increment the safety and reliability in real-world systems. Determining suitable dynamic system models of real processes is essential to obtain effective process control strategies and accurate fault detection and diagnosis methodologies that deliver good performance. In this paper, a maximum likelihood estimation algorithm for the uncertainty modeling in linear dynamic systems is developed utilizing a stochastic embedding approach. In this approach, system uncertainties are accounted for as a stochastic error term in a transfer function. In this paper, we model the error-model probability density function as a finite Gaussian mixture model. For the estimation of the nominal model and the probability density function of the parameters of the error-model, we develop an iterative algorithm based on the Expectation-Maximization algorithm using the data from independent experiments. The benefits of our proposal are illustrated via numerical simulations.

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