Bingöl, Özhan; Güzey, Hacı Mehmet

This study develops a fixed-time neuro-sliding mode controller to enhance the transient response and robustness of a quadrotor that is subjected to external disturbances and parameter uncertainties along with the dynamics of a suspended payload. A simple neural network with a fixed-time adaptive law is used to estimate unknown dynamics; therefore, no previous knowledge of dynamics is required to develop controllers. The neural network component will also help to eliminate the chattering effect of the sliding mode controller through its learning structure. Fixed-time convergence to desired trajectories is proven by detailed theoretical analysis based on the Lyapunov stability theorem, and to show the proposed controller's effective performance, thorough numerical simulations are run.

Bingöl, Özhan; Güzey, Hacı Mehmet

Due to the quadrotor’s underactuated nature, suspended payload dynamics, parametric uncertainties, and external disturbances, designing a controller for tracking the desired trajectories for a quadrotor that carries a suspended payload is a challenging task. Furthermore, one of the most significant disadvantages of designing a controller for nonlinear systems is the infinite-time convergence to the desired trajectory. In this paper, a finite-time neuro-sliding mode controller (FTNSMC) for a quadrotor with a suspended payload that is subject to parametric uncertainties and external disturbances is designed. By constructing a finite-time sliding mode controller, the quadrotor can follow the reference trajectories in finite time. Furthermore, despite time-varying nonlinear dynamics, parametric uncertainties, and external disturbances, a neural network structure is added to the controller to effectively reduce chattering phenomena caused by high switching gains, and significantly reduce the size of the control signals. Following the completion of the controller design, the system’s stability is demonstrated using the Lyapunov stability criterion. Extensive numerical simulations with various scenarios are run to demonstrate the effectiveness of the proposed controller.