Solving Vibration Control Problems Using Reduced Order Models for Flexible Structures: A Genetic Algorithm Approach

1997 ◽  
Author(s):  
Sourav Kundu ◽  
Kentaro Kamagata ◽  
Shigeru Sugino ◽  
Takeshi Minowa ◽  
Kazuto Seto
Keyword(s):  
Genetic Algorithm ◽  
Vibration Control ◽  
Controller Design ◽  
Fitness Function ◽  
Flexible Structures ◽  
Design Solution ◽  
Reduced Order Models ◽  
Reduced Order ◽  
Fitness Value ◽  
Genetic Algorithm Approach

Abstract A Genetic Algorithm (GA) based approach for solution of optimal control design of flexible structures is presented in this paper. The method for modeling flexible structures with distributed parameters as reduced-order models with lumped parameters, which has been developed previously, is employed. Due to some restrictions on controller design it is necessary to make a reduced-order model of the structure. Once the model is established the design of flexible structures is considered as a feedback search procedure where a new solution is assigned some fitness value for the GA and the algorithm iterates till some satisfactory design solution is achieved. We propose a pole assignment method to determine the evaluation (fitness) function to be used by the GA to find optimal damping ratios in passive elements. This paper demonstrates the first results of a genetic algorithm approach to solution of the vibration control problem for practical control applications to flexible tower-like structures.

Model-Based Control of Electroslag Remelting Process Using Unscented Kalman Filter

2008 ◽  
Author(s):  
Seokyoung Ahn ◽  
Joseph J. Beaman ◽  
Rodney L. Williamson ◽  
David K. Melgaard
Keyword(s):  
Kalman Filter ◽  
Temperature Distribution ◽  
Controller Design ◽  
Unscented Kalman Filter ◽  
Liquid Pool ◽  
Electroslag Remelting ◽  
Superior Performance ◽  
Process Conditions ◽  
Reduced Order ◽  
Highly Nonlinear

Electroslag Remelting (ESR) is used widely throughout the specialty metals industry. The process generally consists of a regularly shaped electrode that is immersed a small amount in liquid slag at a temperature higher than the melting temperature of the electrode. Melting droplets from the electrode fall through the lower density slag into a liquid pool constrained by a crucible and solidify into an ingot. High quality ingots require that electrode melt rate and immersion depth be controlled. This can be difficult when process conditions are such that the temperature distribution in the electrode is not at steady state. A new method of ESR control has been developed that incorporates an accurate, reduced-order melting model to continually estimate the temperature distribution in the electrode. The ESR process is highly nonlinear, noisy, and has coupled dynamics. An extended Kalman filter and an unscented Kalman filter were chosen as possible estimators and compared in the controller design. During the highly transient periods in melting, the unscented Kalman filter showed superior performance for estimating and controlling the system.

Sign in / Sign up

Export Citation Format

Share Document