Optimal Controllers Desing for Finite Word Length Implementation Using Genetic Learning Algorithm

• Main Authors: Ching-Chou Feng, 馮經宙
• Other Authors: Wen-Shyong Yu
• Format: Others
• Language: en_US
• Published: 2001
• Online Access: http://ndltd.ncl.edu.tw/handle/25792629750221092960

碩士 === 大同大學 === 電機工程研究所 === 89 === In this thesis, a linear quadratic gaussion (LQG) control scheme with genetic learning algorithm (GLA) is proposed to tackle the numerical errors due to the conversions of the analog to digital (A/D) and digital to analog (D/A) converters in the digital computer. This scheme can be directly used for the design of the ideal LQG and also is optimal in the presence of the numerical errors due to the finite word length. By converting the stochastic problem to a deterministic game theoretic one, we find that the estimation states using GLA and controller not only can stabilize the controlled plant but can minimize the performance index. Finally, a simulation examples are used to validate the theoretical developments and illustrate the usefulness of the proposed control scheme.