Genetic design of multivariable control systems
Dong, YW 2015, Genetic design of multivariable control systems , PhD thesis, University of Salford.

PDF
Download (5MB)  Preview 
Abstract
In the real world there are three types of multivariable control systems. The first one is when the number of inputs is equal to the number of the outputs, this type of multivariable control system is defined as a squared multivariable control system and the main type of controller designed is a decoupling controller which minimizes interactions and gives good setpoint tracking. The second type of multivariable control system is where the number of inputs is greater than the number of the outputs, for this type of system the main controller designed is a failsafe controller. This controller remains stable if a subset of actuator fail. The third type of multivariable control system is the number of outputs is greater than the number of inputs, for this type of system the main controller designed is an override control system. This controller only controls a subset of outputs based on a lowest wins control strategy. All the three types of multivariable control systems are included in this thesis. In this thesis the design of multivariable decoupling control, multivariable failsafe control and multivariable override control as considered. The invention of evolutionary computing techniques has changed the design philosophy for control system design. Rather than using conventional techniques such as Nyquest plots or rootloci control systems can be designed using evolutionally algorithm. Such algorithms evolve solutions using cost functions and optimization. There are a variety of system performance indicators such as integral squared error operator has been used as cost functions to design controllers using such algorithms. The design of both failsafe and override multivariable controllers is a difficult problem and there are very few analytical design methods for such controllers. Therefore, the main objective of this thesis is to use the genetic algorithms to involve both failsafe and override controller multivariable controllers, such that they perform well in the timedomain.
Item Type:  Thesis (PhD) 

Schools:  Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre (SIRC) 
Funders:  Non funded research 
Depositing User:  YW Dong 
Date Deposited:  09 Nov 2015 15:13 
Last Modified:  09 Nov 2015 15:13 
URI:  http://usir.salford.ac.uk/id/eprint/36014 
Actions (login required)
Edit record (repository staff only) 
Downloads
Downloads per month over past year