Abstract
The paper aims at incorporating intelligent modelling paradigm-the Takagi-Sugeno(TS) model and a conventional pole placement control methods in achieving stability for a single area power system network. Four different operating points describing four different local linear state models are used in obtaining the TS fuzzy model, state estimation based on Ackermann's principle was used to determine the state feedback matrix for the four selected operating points, the system in open loop and in closed loop is simulated at varying parameter conditions. Results indicating effectiveness of the developed controller over other reported control method are generated.
Highlights
Fuzzy logic control is a soft computing control method
We seek to introduce a T–S fuzzy modeling frame work which carries with it, some form of system knowledge into the classical pole placement control design, with the aim of achieving robust control system performance even in the presence of varying system parameter conditions
We proposed a method of modifying a pole placement based control law using a T–S fuzzy model parameters
Summary
Fuzzy logic control is a soft computing control method. It employs a number of statements or rules each expressed in IF- format as a basis for implementing a control law. Application of pole placement strategy has been reported within the frame work of T–S fuzzy model control. Due to competitive market demand on the control of industrial systems, the control problem become too complex to handle, partly due to varying nature of the system parameter conditions Classical control method such as pole placement technique suffers because of lack of adequate math model necessary for developing effective controllers. We seek to introduce a T–S fuzzy modeling frame work which carries with it, some form of system knowledge into the classical pole placement control design, with the aim of achieving robust control system performance even in the presence of varying system parameter conditions. We proposed a method of modifying a pole placement based control law using a T–S fuzzy model parameters. We shall present results that would shows the effectiveness of the control scheme
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