Improvements to stochastic multiple model adaptive control: hypothesis test switching and a modified model arrangement
Public Deposited- Resource Type
- Creator
- Abstract
This work demonstrates the fusion of two concepts in switching systems, namely, hypothesis testing and multiple model adaptive control. A hypothesis test switching method is defined to detect parameter jumps in a stochastic environment and perform model selection. The control of a discrete-time stochastic system with rapidly time-varying parameters is simulated. Hypothesis test switching is compared to performance index switching, the most researched and popular switching method. It is found that the hypothesis test method is unique because it operates optimally, without user adjustment or a priori knowledge of the time-varying conditions and model placement. Furthermore, it provides more accurate switching and lower control error.In addition, a major modification to the way multiple models are arranged is proposed. The change decouples stability and performance. As a result, stability is proven more easily, previously required assumptions can be relaxed, new switching methods can be applied, and performance increases are simulated using current switching methods.
- Subject
- Language
- Publisher
- Thesis Degree Level
- Thesis Degree Name
- Thesis Degree Discipline
- Identifier
- Rights Notes
Copyright © 2005 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.
- Date Created
- 2005
Relations
- In Collection:
Items
Thumbnail | Title | Date Uploaded | Visibility | Actions |
---|---|---|---|---|
campbell-improvementstostochasticmultiplemodeladaptive.pdf | 2023-05-03 | Public | Download |