GENETIC ALGORITHM-BASED CONTROL OF GYRO-MIRROR LINE-OF-SIGHT STABILIZATION PLATFORM

Abstract

This thesis develops a technique to design genetic algorithm-based controllers for a multivariable gyro-mirror line-of-sight stabilization platform. Two types of widely adopted controllers are investigated in this project: PID and Fuzzy logic controller design. Both types of controllers are designed and implemented using a full-matrix control system configuration, as the plant is multivariable, nonlinear and highly coupled in nature. In PID controller design, GA can easily determine the optimal values of Kp, Ki and Kd to achieve a good closed-loop performance. In FLC design, the technique is capable of automatically selecting near optimal fuzzy rule-bases and high-performance membership functions for good transient and steady-state response. Simulation and implementation results show that both genetic-based design PID and Fuzzy logic controllers offer an satisfactory transient and tracking performance for both output channels of the multivariable system. Moreover, the highly coupling effect of the nonlinear system has also been reduced significantly.