Fuzzy State-Feedback PDC and LQR Control of Nonlinear Quadrotor

In this study, the Takagi-Sugeno Multi-Model technique is used to linearize the quadrotor and design an LQR controller. Local models are first created by linearizing the Quadrotor's nonlinear model about a collection of operating points using the Taylor series. The Fuzzy Takagi-Sugeno (FTS) approach is then used to interpolate these local models. We shall compare the T -S model with the nonlinear model. Next, the Parallel Distributed Compensation (PDC) is used to create a nonlinear state-feedback controller. Linear Quadratic Regulator (LQR) optimization is used to obtain the controller's gains to stabilize the system and produce the intended response. The unit step response for both the nonlinear and linearized models is obtained through simulations. It has been noted that the suggested T -S control provides a satisfactory response.