1990
DOI: 10.2514/3.20518
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Robust linear quadratic Gaussian control for flexible structures

Abstract: A procedure is developed to deal with performance and robustness issues in the design of multiple-input multiple-output compensators for lightly damped flexible structures. The procedure is based upon representing errors in the plant design model as structured uncertainties, and applying a modified linear quadratic Gaussian design method. The cost function in the regulator problem and the process noise model in the estimator problem are varied in a manner that reflects specific parameter uncertainty including … Show more

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Cited by 10 publications
(3 citation statements)
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“…III, experimental results have demonstrated the effectiveness of INDI-QP-V in GLA, MLA, and simultaneous GLA and MLA tasks. In the literature, the LQG control is one of the most popular methods for load alleviation [10,13,30,35]. Therefore, the proposed INDI-QP-V control will be compared with LQG control.…”
Section: Comparisons With Linear Quadratic Gaussian Controlmentioning
confidence: 99%
“…III, experimental results have demonstrated the effectiveness of INDI-QP-V in GLA, MLA, and simultaneous GLA and MLA tasks. In the literature, the LQG control is one of the most popular methods for load alleviation [10,13,30,35]. Therefore, the proposed INDI-QP-V control will be compared with LQG control.…”
Section: Comparisons With Linear Quadratic Gaussian Controlmentioning
confidence: 99%
“…III, experimental results have demonstrated the effectiveness of INDI-QP-V in GLA, MLA and simultaneous GLA and MLA tasks. In the literature, the Linear Quadratic Gaussian (LQG) control is one of the most popular methods for load alleviation [10,13,28,32]. Therefore, the proposed INDI-QP-V control will be compared to LQG control.…”
Section: Comparisons With Linear Quadratic Gaussian Controlmentioning
confidence: 99%
“…De-sensitizing techniques usually take an already proven unrobust method such as LQG and de-sensitize it to variations in parameters. For example, Blelloch and Mingori [80] modify the state and noise weighting matrices in the LQG compensator design to account for structured parametric uncertainty, thus reducing optimality. [81] add sensitivity states which penalize the variation of the performance objective with respect to parameter variations.…”
Section: Controller Synthesismentioning
confidence: 99%