Modeling and control of a quadrotor unmanned aerial vehicle using type-2 fuzzy systems

This chapter presents the applications of an interval type-2 (IT2) Takagi–Sugeno (TS) fuzzy system for modeling and controlling the dynamics of a quadcopter unmanned aerial vehicle. In addition to being complex and nonlinear, the dynamics of a quadcopter are underactuated and uncertain, making the modeling and control tasks across its full flight envelope nontrivial. The popularity of fuzzy systems stems from the fact that they are a universal approximator, making them capable of explaining complex relations among variables in the form of fuzzy “if-then” rules. Addressing current research gaps, we performed a nonlinear system identification, leveraging the benefits of the TS fuzzy system to model the attitude dynamics of a quadcopter drone. The data were collected from real-time flight tests in an indoor flight test facility, instrumented with a VICON motion capture system. We designed a robust IT2 fuzzy logic controller (IT2FLC) for trajectory tracking and we improved the performance of the fixed IT2FLC by designing an adaptive control law, which was derived using the sliding mode control theory. The efficacy of our fuzzy controller was investigated in the face of multiple external disturbances, where superior outcomes were obtained compared to traditional methods.