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Suboptimal stochastic control for semi-active vibration isolation systems

✍ Scribed by S. Bellizzi


Publisher
Elsevier Science
Year
1994
Tongue
English
Weight
345 KB
Volume
8
Category
Article
ISSN
0888-3270

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πŸ“œ SIMILAR VOLUMES


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A stochastic optimal semi-active control strategy for randomly excited systems using electrorheological/magnetorheological (ER/MR) dampers is proposed. A system excited by random loading and controlled by using ER/MR dampers is modelled as a controlled, stochastically excited and dissipated Hamilton

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This work concerns the development of a dual suboptimal controller for discrete time systems with stochastic parameters. The control signal is computed in such a way as to minimize the variance of the output around a reference value two steps ahead in time. The higher-order statistical moments are a

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✍ M. AbΓ©; T. Igusa πŸ“‚ Article πŸ“… 1996 πŸ› Elsevier Science 🌐 English βš– 440 KB

Semi-active dynamic vibration absorbers have one or both of the following capabilities: (a) initial displacements set to non-zero values and (b) damping adjustable in real time. Such devices are feasible because of the development of electro-rheological fluids and electromagnetic dampers. Experiment

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Passive supplemental damping in a seismically isolated structure provides the necessary energy dissipation to limit the isolation system displacement. However, damper forces can become quite large as the passive damping level is increased, resulting in the requirement to transfer large forces at the

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A feedback controller designed to minimise the vibration transmitted to a flexible beam from a vibrating machine via two active isolation mounts is described. The state-space equation for the mechanical structure is obtained by performing modal analysis on the associated conservative structure. Ha c

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✍ T. Yoshimura; K. Edokoro; N. Ananthanarayana πŸ“‚ Article πŸ“… 1993 πŸ› Elsevier Science 🌐 English βš– 305 KB

This paper describes an active suspension model for rail/vehicle systems based on preview and stochastic optimal control. The vehicle is modelled as a linear system with three degrees of freedom subject to irregular rail inputs which are described by first order systems with Gaussian white random in