Optimal Parallel Controllers and Filters for a Class of Second-Order Linear Dynamic Systems

2013 ◽  
Vol 1 (2) ◽  
pp. 37-49 ◽  
Author(s):  
Verica Radisavljevic-Gajic
Keyword(s):  
Dynamic Systems ◽  
Second Order ◽  
Order Linear ◽  
Linear Dynamic Systems ◽  
Linear Dynamic

Exact Decoupling of Non-Classically Damped Matrix Second-Order Linear Mechanical Systems

2009 ◽  
Author(s):  
Verica Radisavljevic ◽  
Dobrila Skataric
Keyword(s):  
Dynamic Systems ◽  
Second Order ◽  
Distributed Parameter ◽  
Order Linear ◽  
Linear Dynamic Systems ◽  
Linear Dynamic ◽  
Subsystem Level ◽  
Damped Systems ◽  
And Control ◽  
Analytical Tools

Matrix second-order damped linear dynamic systems are frequently encountered in mechanical, structural, civil, aerospace engineering, and related fields. This class of systems is also obtained by approximating dynamics of systems described by partial differential equations (distributed parameter systems). There are many papers in the engineering literature on analysis and control of matrix second-order linear damped systems. They provide either approximate (simplified) analytical results or accurate numerical results (usually computationally involved). In this paper, we show how to decouple exactly a matrix second-order linear system into scalar second-order subsystems and study exactly the corresponding system dynamics at the subsystem level using simple analytical tools. Conditions are established under which the presented procedure is applicable. An example is included to demonstrate the efficiency of the proposed technique.

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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