Lead-Lag PSS Design Based on H∞ Control Theory and Genetic Algorithm

다국어 초록 (Multilingual Abstract)

The aim of this paper is to explain the way to scale back the dimension of an influence system stabilizer (PSS) supported H∞ management theory. In recent years, sturdy PSS styles that adopt associate in nursing H∞ controller are investigated so as to ensure the performance once the state of the system configuration and power flow modification. However the H∞ controller has not been wide adopted into sensible use attributable to the tangled nature of its theory and structure. We tends to think about the H∞ management downside below the condition that PSS structure is mounted to be a lead-lag compensator. Infinity norm of transfer function from disturbance to output is subjected to be minimized via searching and evolutionary computation. The resulted optimal parameters make the system stable and also guarantee robust performance. We applied the evolutionary robust controller to a pneumatic servo system. For performances comparison, three controllers; PID with derivative first order filter controller, PI controller and H- loop shaping controller are investigated. We tends to optimize the parameters of the Lead-Lag PSS by a genetic algorithm program that has associate in nursing analysis perform that takes under consideration a closed-loop system H∞ norm and a desired response. During this manner, we tends to design a PSS that encompasses a standard managementler structure and guarantees its control performance.

목차 (Table of Contents)

• Abstract
• 1. INTRODUCTION
• 2. H∞ Control Theory
• A. H∞ Control Theory
• B. Dimensional Reduction by Direct Method
• 3. Genetic Algorithms
• 4. Formulation of Proposed Evaluation Function for Dimensional Reduction
• A. Elements of the Evaluation Function
• B. Proposed Evaluation Function
• 5. Design of the Proposed PSS
• A. Definition of Control Target
• B. Definition of Weight Functions
• 6. Parameter Determination for Low-dimension PSS by GA
• A. Parameter Optimization using GA
• B. Evaluation of the Reduced-Dimension PSS
• 7. Simulations
• 8. Conclusion
• References