Abstract
This thesis treats the problem of direct adaptive control of linear multivariable systems. The parametrization problem of adaptive control is discussed extensively. A poleplacement problem and a modelmatching problem are formulated and interpreted in terms of model reference control. The problem is solved via a discussion on system invariants of multivariable systems as presented by Pernebo. The attention is then directed towards problems of identification and two different estimation schemes are formulated. Parameter convergence is guaranteed provided some conditions on /a priori/ information are satisfied. The requested prior knowledge is formulated in terms of the noninvertible system for the suggested prediction error identification algorithm. The second parameter adjustment law is shown to converge when a certain approximant of the left structure matrix, /i.e./, the system invariant is known. This result relaxes a result by Elliott /et al./ where the interactor is required to be known.
The important question of stability of adaptive systems is also treated. The major result is a method for construction of Lyapunov functions for a class of singleinput systems. Stability in the sense of Lyapunov and exponential convergence are shown.
The important question of stability of adaptive systems is also treated. The major result is a method for construction of Lyapunov functions for a class of singleinput systems. Stability in the sense of Lyapunov and exponential convergence are shown.
Original language  English 

Qualification  Doctor 
Awarding Institution 

Supervisors/Advisors 

Award date  1983 May 31 
Publisher  
Publication status  Published  1983 
Subject classification (UKÄ)
 Control Engineering