Adaptive tracking control of an unmanned aerial system based on a dynamic neural-fuzzy disturbance estimator. (June 2020) - Cervantes-Rojas, Jorge S.; Muñoz, Filiberto; Chairez, Isaac; González-Hernández, Iván; Salazar, Sergio
Abstract: The main goal of this study is developing an adaptive controller which can solve the trajectory tracking for a class of quadcopter unmanned aerial system (UAS), namely a quadrotor. The control design introduces a new paradigm for adaptive controllers based on the implementation of a set of differential neural networks (DNNs) in the consequence section of a Takagi–Sugeno (T–S) fuzzy inference system. This dynamic fuzzy inference structure was used to approximate the UAS description. The particular form of interaction between neural networks and fuzzy inference systems proposed in the present work received the name of dynamic neural fuzzy system (DNFS). An adaptive controller based on this DNFS form was the main solution attained in this study. This DNFS controller was focused on the estimation and compensation of the uncertain section of the Quadrotor dynamics and then, forced the UAS to perform a hover flight while the tracking of desired angular positions succeeded, which results in tracking a desired trajectory in the X-Y plane. The control design methodology supported on the Lyapunov stability theory guaranteed ultimate boundedness of the estimation and tracking errors simultaneously. Several experimental tests in an outdoor environment by using a real Quadrotor platform was performed by using an RTK-GPS (Real Time Kinematic) system to determine the position of the vehicle in the X-Y plane. The experimental results confirmed the superior performance of the proposed algorithm based on the combination of DNNs and T–S techniques with respect to classical robust controllers. Highlights: A neuro fuzzy adaptive controller is designed to solve trajectory tracking problem for a Quadrotor unmanned aerial system. The control algorithm uses a neuro-fuzzy structure by aggregating differential neural networks (DNNs) with a T–S inference system. An adaptive compensation was applied by using the estimation of the uncertain section of the Quadrotor dynamics. This adaptive neuro-fuzzy controller enforces the Quadrotor UAS to perform a hover flight. Experiments confirmed the superior performance of the proposed neuro-fuzzy controller with respect to classical controllers.