Beam analogy for optimal control of linear dynamic systems

2000 ◽  
Vol 25 (5) ◽  
pp. 489-500 ◽  
Author(s):  
W. Szyszkowski ◽  
I. S. Grewal
Keyword(s):  
Optimal Control ◽  
Dynamic Systems ◽  
Linear Dynamic Systems ◽  
Linear Dynamic

Stochastic Optimal Control of Linear Dynamic Systems

AIAA Journal ◽  
1974 ◽  
Vol 12 (8) ◽  
pp. 1013-1020 ◽  
Author(s):  
JASON L. SPEYER ◽  
DONALD E. GUSTAFSON
Keyword(s):  
Optimal Control ◽  
Dynamic Systems ◽  
Stochastic Optimal Control ◽  
Linear Dynamic Systems ◽  
Linear Dynamic

scholarly journals Control of stage by stage changing linear dynamic systems

2012 ◽  
Vol 22 (1) ◽  
pp. 31-39 ◽  
Author(s):  
V.R. Barseghyan
Keyword(s):  
Optimal Control ◽  
Control Problem ◽  
Dynamic Systems ◽  
Sufficient Conditions ◽  
Control Problems ◽  
Time Intervals ◽  
Linear Dynamic Systems ◽  
Linear Dynamic ◽  
Necessary And Sufficient ◽  
Total Controllability

In this paper, the control problems of linear dynamic systems stage by stage changing and the optimal control with the criteria of quality set for the whole range of time intervals are considered. The necessary and sufficient conditions of total controllability are also stated. The constructive solving method of a control problem is offered, as well as the definitions of conditions for the existence of programmed control and motions. The explicit form of control action for a control problem is constructed. The method for solving optimal control problem is offered, and the solution of optimal control of a specific target is brought.

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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