Optimal Design of Generalized Multiple Model Adaptive Controllers
Doctoral thesis Nov 1998-Mar 2004
AIR FORCE INST OF TECH WRIGHT-PATTERSON AFB OH SCHOOL OF ENGINEERING AND MANAGEMENT
Pagination or Media Count:
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 parameters of the system model.
- Theoretical Mathematics
- Computer Systems