Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Multi-disciplinary Studies | en_US |
| dc.creator | Wong, Yum-hong Eric | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/4418 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | - |
| dc.rights | All rights reserved | en_US |
| dc.title | Qualitative modeling and control of dynamic systems | en_US |
| dcterms.abstract | Since many real systems are of high order, time-varying and non-linear, the modeling and analysis of complicated systems are usually very difficult. The use of qualitative reasoning can avoid complex mathematical operations, and can be employed to overcome some of the above difficulties. Qualitative reasoning is attractive because of the generality of the description of physical systems, which means that one particular qualitative model can be used to describe a large range of operating conditions. Most of applications of qualitative reasoning in engineering have so far been involved in process monitoring and diagnosis. This dissertation illustrates how to apply qualitative reasoning techniques to control problems. Based on the constraint-centred approach, practical physical systems are abstracted to qualitative models with qualitative variables related by qualitative constraints. The qualitative behavior of the model is then simulated by a qualitative simulator. Based on the principles of qualitative simulation, a C program is developed to simulate the behavior of a second-order system. The results generated agree with common sense reasoning. The principles of qualitative reasoning are then applied to a liquid level control system. The two tanks system is considered as the process. The objective of the system is to control the level of liquid in tank 2 by regulating the inflow in tank 1. A C program is developed to generate the states of the system variables using model equations. A qualitative controller is developed based on casual reasoning. The control rules are stored in the program as look-up table. When the program is executed, it starts with some initial conditions and generates the required states of system variables. The control action will be determined by the qualitative control rules to bring the output to the desired setpoint. The results show that the qualitative controller works well for both linear and non-linear models, and when time delay is included in the system. | en_US |
| dcterms.extent | v, 101 leaves : ill. ; 30 cm | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 1996 | en_US |
| dcterms.educationalLevel | All Master | en_US |
| dcterms.educationalLevel | M.Sc. | en_US |
| dcterms.LCSH | Control theory | en_US |
| dcterms.LCSH | System analysis | en_US |
| dcterms.LCSH | Artificial intelligence | en_US |
| dcterms.LCSH | Reasoning | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | restricted access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| b12306988.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 2.4 MB | Adobe PDF | View/Open |
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