On the computational analysis of the genetic algorithm for attitude control of a carrier system

TL;DR

This paper explores the use of a genetic algorithm to optimize a fuzzy-PID controller for attitude control of a launch vehicle, analyzing how different GA parameters affect optimization performance.

Contribution

It introduces a combined fuzzy-PID control approach optimized by a genetic algorithm and investigates the impact of various GA parameters on system performance.

Findings

GA parameter adjustments significantly influence optimization outcomes

Fuzzy-PID controller effectively manages attitude control in launch vehicles

Sensitivity analysis identifies optimal GA settings for system stability

Abstract

This paper intends to cover three main topics. First, a fuzzy-PID controller is designed to control the thrust vector of a launch vehicle, accommodating a CanSat. Then, the genetic algorithm (GA) is employed to optimize the controller performance. Finally, through adjusting the algorithm parameters, their impact on the optimization process is examined. In this regard, the motion vector control is programmed based on the governing dynamic equations of motion for payload delivery in the desired altitude and flight-path angle. This utilizes one single input and one preferential fuzzy inference engine, where the latter acts to avoid the system instability in large angles for the thrust vector. The optimization objective functions include the deviations of the thrust vector and the system from the equilibrium state, which must be met simultaneously. Sensitivity analysis of the parameters of the genetic algorithm involves examining nine different cases and discussing their impact on the optimization results.