Research Papers

System Identification and Robust Controller Design Using Genetic Algorithms for Flexible Space Structures

[+] Author and Article Information
Marco P. Schoen1

Measurement and Controls Engineering Research Center (MCERC), Department of Mechanical Engineering, Idaho State University, Pocatello, ID 83209

Randy C. Hoover

 Colorado State University, Fort Collings, CO 80523

Sinchai Chinvorarat

 King Mongkut’s Institute of Technology North Bangkok, Bangkok 10800, Thailand

Gerhard M. Schoen

Measurement and Control Laboratory, Swiss Federal Institute of Technology (ETH), CH-8092 Zurich, Switzerland


Corresponding author.

J. Dyn. Sys., Meas., Control 131(3), 031003 (Mar 19, 2009) (11 pages) doi:10.1115/1.3072106 History: Received October 27, 2005; Revised October 01, 2008; Published March 19, 2009

This paper is concerned with the problem of identifying and controlling flexible structures. The structures used exhibit some of the characteristics found in large flexible space structures (LFSSs). Identifying LFSS are problematic in the sense that the modes are of low frequency, lightly damped, and often closely spaced. The proposed identification algorithm utilizes modal contribution coefficients to monitor the data collection. The algorithm is composed of a two-step process, where the input signal for the second step is recomputed based on knowledge gained about the system to be identified. In addition, two different intelligent robust controllers are proposed. In the first controller, optimization is concerned with performance criteria such as rise time, overshoot, control energy, and a robustness measure among others. Optimization is achieved by using an elitism based genetic algorithm (GA). The second controller uses a nested GA resulting in an intelligent linear quadratic regulator/linear quadratic Gaussian (LQR/LQG) controller design. The GAs in this controller are used to find the minimum distance to uncontrollability of a given system and to maximize that minimum distance by finding the optimal coefficients in the weighting matrices of the LQR/LQG controller. The proposed algorithms and controllers are tested numerically and experimentally on a model structure. The results show the effectiveness of the proposed two-step identification algorithm as well as the utilization of GAs applied to the problem of designing optimal robust controllers.

Copyright © 2009 by American Society of Mechanical Engineers
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Figure 6

Model structure: (a) physical model and test setup, (b) schematic of the vibration model, and (c) schematic of the system

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Figure 7

Modal contribution factors for the experimental system without input design, 5000 data points, five contribution factors: (a) first step of the proposed input design and (b) second step of the proposed input design

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Figure 8

Comparison between uncontrolled and controlled responses of the model structure

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Figure 1

Lumped mass-damper-spring setup

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Figure 2

Modal contribution factors for a simulation model. (a) Theoretical modal contribution coefficient, (b) with original input design and 1% noise variance, and (c) using the proposed input design, 1% noise variance.

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Figure 3

Power spectrum for the random input and the modified input

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Figure 4

Impulse response of (a) the true system without compensation and (b) the compensated system

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Figure 5

(a) Convergence plot for GA using minimum distance to uncontrollability. (b) Step response of uncompensated and compensated systems.



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