Active Control of Generalized Complex Modal Structures in a Stochastic Environment

Abstract

This report deals with the active control of heavily damped vibratory mechanical systems in a stochastic environment. The systems of interest are multiple input, multiple output (MIMO)structures that are modeled by generalized complex modes. The control objective is to suppress the effects of impulsive disturbances within a short period of time as well as to provide specified reduction of vibration due to ever-present Gaussian and nonGaussian stochastic disturbances. The team effort, described separately, in this report can be viewed as three interactive parts: one group investigating the robust control issues; one, the nongaussian estimation problem and a third group working with a mechanical plate experiment, generating data and identification algorithms. The experimental model was constructed in the M. E. Department under DARPA funding. The robust control investigators used modern analysis and synthesis tools to incorporate the required performance directly into the design procedure. The approach to handle the nongaussian state estimation problem was in the use of an adaptive Gaussian sum state estimator.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
May 15, 1992
Accession Number
ADA251910

Entities

People

  • Bhaskar Gorti
  • Hugh F. Vanlandigham
  • Mauro J. Caputi
  • Richard L. Moose
  • Stephen H. Jones
  • William T. Baumann

Organizations

  • Virginia Tech

Tags

Communities of Interest

  • Cyber
  • Sensors
  • Space

DTIC Thesaurus Topics

  • Closed Loop Systems
  • Computational Science
  • Control Systems
  • Control Systems Engineering
  • Databases
  • Detection
  • Differential Equations
  • Electrical Engineering
  • Estimators
  • Filtration
  • Information Science
  • Kalman Filters
  • Mathematical Filters
  • Multiple Input Multiple Output
  • Random Variables
  • Resonant Frequency
  • Stochastic Processes

Fields of Study

  • Engineering

Readers

  • Computational Modeling and Simulation
  • Statistical inference.
  • Systems Analysis and Design