ROM integrated vibration control of stochastically parametered flexible structures
23 Sep 2020-pp 1797-1802
TL;DR: A reduced order model is developed to reduce the state space dimension of the problem and further integrated with the control algorithm, and controller gain is obtained using linear quadratic regulator in reduced subspace which is used as a feedback to the actuators to produce the required control force for vibration control.
Abstract: This study focusses on the vibration control of linear large scale engineering structures, modelled as continuous system, with varying system parameters. Because of manufacturing limitations or/and measurement errors, system parameters need to be modelled as random variables. Here, system parameters are modelled as non-Gaussian random variables. Mathematically, continuous system is governed by partial differential equations and solved using approximation methods. High fidelity finite element model is the starting point of the analysis. Since numerical approximation involves large number of degrees-of-freedom, solving the system in real time is computationally expensive and application of control algorithm is cumbersome. In this study, a reduced order model is developed to reduce the state space dimension of the problem and further integrated with the control algorithm. Next, controller gain is obtained using linear quadratic regulator in reduced subspace which is used as a feedback to the actuators to produce the required control force for vibration control. A numerical example of flexible cantilever beam is solved to demonstrate the efficacy of the algorithm and probabilistic characterisation is carried out using Monte Carlo simulation.
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Abstract: 1. Introduction. 2. Linear Algebra. 3. Linear Systems. 4. H2 and Ha Spaces. 5. Internal Stability. 6. Performance Specifications and Limitations. 7. Balanced Model Reduction. 8. Uncertainty and Robustness. 9. Linear Fractional Transformation. 10. m and m- Synthesis. 11. Controller Parameterization. 12. Algebraic Riccati Equations. 13. H2 Optimal Control. 14. Ha Control. 15. Controller Reduction. 16. Ha Loop Shaping. 17. Gap Metric and ...u- Gap Metric. 18. Miscellaneous Topics. Bibliography. Index.
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TL;DR: In this article, the use of macro-fiber composites (MFC) for vibration suppression and structural health monitoring has been presented, where an MFC could be used as a sensor and actuator to find modal parameters of an inflatable structure.
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09 Sep 2004
TL;DR: Academics working in research on structural dynamics, MEMS, vibration, finite elements and other computational methods in mechanical, aerospace and structural engineering will find Model Order Reduction Techniques of great interest while it is also an excellent resource for researchers working on commercial finite-element-related software such as ANSYS and Nastran.
Abstract: Despite the continued rapid advance in computing speed and memory the increase in the complexity of models used by engineers persists in outpacing them. Even where there is access to the latest hardware, simulations are often extremely computationally intensive and time-consuming when full-blown models are under consideration. The need to reduce the computational cost involved when dealing with high-order/many-degree-of-freedom models can be offset by adroit computation. In this light, model-reduction methods have become a major goal of simulation and modeling research. Model reduction can also ameliorate problems in the correlation of widely used finite-element analyses and test analysis models produced by excessive system complexity. Model Order Reduction Techniques explains and compares such methods focusing mainly on recent work in dynamic condensation techniques: - Compares the effectiveness of static, exact, dynamic, SEREP and iterative-dynamic condensation techniques in producing valid reduced-order models; - Shows how frequency shifting and the number of degrees of freedom affect the desirability and accuracy of using dynamic condensation; - Answers the challenges involved in dealing with undamped and non-classically damped models; - Requires little more than first-engineering-degree mathematics and highlights important points with instructive examples. Academics working in research on structural dynamics, MEMS, vibration, finite elements and other computational methods in mechanical, aerospace and structural engineering will find Model Order Reduction Techniques of great interest while it is also an excellent resource for researchers working on commercial finite-element-related software such as ANSYS and Nastran.
223Â citations
TL;DR: In this article, the authors demonstrate the feasibility of utilizing Shape Memory Actuators (SMA) in controlling the flexural vibrations of a flexible cantilevered beam, by using the finite element method, and integrated with the thermal and dynamic characteristics of SMA to develop a mathematical model of the composite beam-actuators system.
218Â citations
"ROM integrated vibration control of..." refers background in this paper
...Vibration control for such systems can be achieved using advanced smart materials like, piezo-composites [3], shape memory actuators [4]....
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