Adaptive Neural-Sliding Mode Control of a Quadrotor Vehicle with Uncertainties and Disturbances Compensation

Abstract

This paper addresses the quadrotor vehicle control problem in the presence of parametric uncertainties and exogenous disturbances by introducing a finite-time extended disturbance observer-based adaptive neural sliding mode control (FTEDO-ANSMC) approach. The proposed FTEDO makes the controller robust to exogenous disturbances while eliminating the chattering issue in the control input. The designed SMC utilizes an adaptive neural network to tune its parameters online while a sliding mode concept-based weight update law is employed in the neural network to auto-update its weight parameters instead of conventional error-based weight update law without increasing the computational complexities, thereby enhancing the network's learning speed. The stability of the proposed control strategy is verified via Lyapunov theory. The simulation results of the proposed control strategy and its comparison with the conventional control strategy confirm its validity and efficacy.