摘要:

The problem of optimal model-following linear control of discrete stochastic adaptive systems is studied in the presence of unmodelled dynamics. The controller complexity is restricted to that corresponding to the perfectly modelled situation. The key for a control design to be robust in the presence of unmodelled dynamics is closely related to some parametrical conditions that ensure a certain linear filter (involving parametrical uncertainties related to unmodelled dynamics) maintains the output stationarity of any stationary random input signal, as in the nominal situation. Partial results concerning robustness are obtained from stability considerations. In any case,a prioriknowledge on the unmodelled dynamics is not available for the implementation of the control law.

ISSN:0020-7721    

DOI:10.1080/00207728908910145

出版商:Taylor & Francis Group    

年代:1989

数据来源: Taylor

摘要:

The task of synthesizing optimal discrete stochastic systems to control devices and developing controller tuning parameters that have optimal rates of convergence is examined in the presence of unmodelled dynamics. Several standard kinds of optimality are discussed in the context of the adjustable models and the adaptation algorithms.

ISSN:0020-7721    

DOI:10.1080/00207728908910146

出版商:Taylor & Francis Group    

年代:1989

数据来源: Taylor