On the computational analysis of the genetic algorithm for attitude control of a carrier system
This addresses attitude control for a specific carrier system, representing an incremental application of existing methods.
The paper designed a fuzzy-PID controller for thrust vector control of a launch vehicle carrying a CanSat and used a genetic algorithm to optimize it, examining how nine different parameter settings affect optimization results.
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.