Evolving fuzzy inference system based online identification and control of a quadcopter unmanned aerial vehicle

Among different rotary wing unmanned aerial vehicles; quadcopters are used most commonly in both civil and military sector. Comparatively higher portability, smaller size, simple method of assembly and reconstruction and lower expenditure have caused the rapid growth of the quadcopter. Precise mathematical model of quadcopter considering uncertainties is necessary for the better performance of the commonly used first principle based controllers. However, modelling quadcopter incorporating uncertainties is hard to achieve. A solution to the problem is the utilization of modelfree data-driven methods. An evolving intelligent system (EIS) based data-driven technique called evolving Takagi-Sugeno fuzzy inference system (eTS) is employed in this paper for online identification of a quadcopter from realtime experimental data. Besides, an adaptive fuzzy controller is developed using fuzzy c means clustering to control the altitude of the quadcopter.