Modeling a Takagi-Sugeno (T-S) fuzzy for unmanned aircraft vehicle using fuzzy controller

Drone technology has the potential to disrupt and augment our quality of life, as it is rapidly growing in popularity and being utilized in various applications, such as agriculture, emergency response, border control, asset inspection, and intelligent transportation. On the other side, Artificial intelligent instruments that possess a variety of input and output (I/O) mechanisms are employed to achieve model stabilizing with data estimation. Firstly, in the present study, a linear mathematical model was developed for a quad–copter Unmanned Aerial Vehicle (UAV), in which the Takagi–Sugeno (T–S) Fuzzy logic framework was integrated. The crisp variables have been used to make the interference between the input and output of the T–S fuzzy system. Secondly, to control a quadcopter model with inherent dynamic instability, these state space models are crucial. Inputs of fuzzy controller are data generated by sensors and Bluetooth connected to IoT. The state–space model of the quad copter, which consists of six Degrees Of Freedom (6–DOF), is derived by utilizing fundamental Newtonian equations. This establishment of the model holds significant value in effectively governing the quad copter system. Thirdly, the system stabilizing has been proved by linear matrix inequalities (LMIs) with an associated Lyapunov function with the γ performance index. Simulation results have been presented to demonstrate the efficiency of our proposed algorithm with additional computational burden analysis.