Model reference adaptive control using a low-order controller
β Scribed by Daniel E. Miller
- Publisher
- John Wiley and Sons
- Year
- 2001
- Tongue
- English
- Weight
- 168 KB
- Volume
- 15
- Category
- Article
- ISSN
- 0890-6327
- DOI
- 10.1002/acs.666
No coin nor oath required. For personal study only.
β¦ Synopsis
Abstract
In the model reference adaptive control problem, the goal is to force the error between the plant output and the reference model output asymptotically to zero. The classical assumptions on a singleβinputβsingleβoutput (SISO) plant is that it is minimum phase, and that the plant relative degree, the sign of the highβfrequency gain, and an upper bound on the plant order are known. Here we consider a modified problem in which the objective is weakened slightly to that of requiring that the asymptotic value of the error be less than a (arbitrarily small) preβspecified constant. Using recent results on the design of generalized holds for model reference tracking, here we present a new switching adaptive controller of dimension two which achieves this new objective for every minimum phase SISO system; no structural information is required. Copyright Β© 2001 John Wiley & Sons, Ltd.
π SIMILAR VOLUMES
The discrete-time version of continuous-time combined model reference adaptive control (CMRAC) is presented in this paper. A global stability proof of the overall adaptive scheme is given using arguments similar to those used in discrete-time direct model reference adaptive control (DMRAC) but prope
In Part I, an approach to deriving low-order models suitable for use in the development of active control strategies for separated ows was presented. The methodology proposed was applied to a numerical simulation of the incompressible, unsteady wake ow behind a circular cylinder at Re = 100, with co
A new variable structure model reference adaptive control scheme with integration is proposed to achieve robustness to modelling uncertainties in this paper. A key feature of this scheme is that the knowledge of neither the parameters of the upper bounding function on the modelling uncertainties nor