Abstract
In this paper, a novel efficient optimization method based on reinforcement learning automata (RLA) for optimum parameters setting of conventional proportional-integral-derivative (PID) controller for AVR system of power synchronous generator is proposed. The proposed method is Combinatorial Discrete and Continuous Action Reinforcement Learning Automata (CDCARLA) which is able to explore and learn to improve control performance without the knowledge of the analytical system model. This paper demonstrates the full details of the CDCARLA technique and compares its performance with Particle Swarm Optimization (PSO) as an efficient evolutionary optimization method. The proposed method has been applied to PID controller design. The simulation results show the superior efficiency and robustness of the proposed method.
| Original language | English |
|---|---|
| Pages (from-to) | 1609-1625 |
| Number of pages | 17 |
| Journal | Journal of Circuits, Systems and Computers |
| Volume | 18 |
| Issue number | 8 |
| DOIs | |
| State | Published - Dec 2009 |
Bibliographical note
Funding Information:M. Kashki acknowledges the support of Key Sun Pars Consultants Engineering Co., which is a famous and good sound engineering company in the fields of oil, gas and petrochemical. Dr. Y. Abdel-Magid and Dr. M. A. Abido acknowledge the support of the Petroleum Institute, Abu Dhabi, UAE, and King Fahd University of Petroleum & Minerals, Saudi Arabia, respectively.
Keywords
- AVR
- Evolutionary computations
- PID
- Reinforcement learning automata
- Synchronous generator
ASJC Scopus subject areas
- Hardware and Architecture
- Electrical and Electronic Engineering