Optimal Design of Generalized Multiple Model Adaptive Controllers

Abstract

Advanced analysis and optimal design techniques that achieve performance improvement for multiple model adaptive control (MMAC) and multiple model adaptive estimation (MMAE) based control are developed and tested for this dissertation research. An adjunct area of research yielded modified linear quadratic Gaussian (LQG) control design techniques that also can be applied to nonadaptive control. For the Modified LQG (MLQG) controller, the proposed designs remove the assumption that the Kalman filter as the observer and the controller gain matrix design are necessarily based on the same model as the best system model. The filter and controller gain matrices are both determined by models possibly other than the system model. In order to achieve optimal performance, the interrelationship of the system model to the filter and controller design models is established by minimizing a position correlation (mean square error on output) measure. Enhanced robustness is realized by considering the performance over the range of values of specified parameter(s) of the system model.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2004
Accession Number
ADA422596

Entities

People

  • Thomas E. Brehm

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Closed Loop Systems
  • Computational Fluid Dynamics
  • Computational Science
  • Control Systems
  • Data Science
  • Dynamic Response
  • Engineering
  • Equations Of State
  • Estimators
  • Filters
  • Information Science
  • Kalman Filters
  • Mathematical Filters
  • Monte Carlo Method
  • Statistical Algorithms

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.