Modified Adaptive Sliding Mode Control for Trajectory Tracking of Mini-drone Quadcopter Unmanned Aerial Vehicle

Abstract

Unmanned Aerial Vehicles are among the most widely attracting interest, especially in the applications of transportation, inspection, and surveillance. The great mechanisms for motion control are set to become a vital factor in performing the robust and accurate stabilized flight relied on perturbations and disturbances. This paper presents modified adaptive sliding mode control for trajectory tracking of mini-drone quadcopter unmanned aerial vehicles, which aims at demonstrating the effectiveness of nonlinear adaptive control strategy for achieving the desired performance of the mini-drone quadcopter system. Besides providing mathematical modeling and nonlinear dynamic characteristic details of mini-drone quadcopter actuated system, the modified adaptive sliding mode algorithm is developed using adaptation law based on Lyapunov stability approach then applied on the attitude loop and the altitude loop control system so that the nonlinear adaptive behavior of the controller enables the compensation of disturbances and parameter perturbations. The effectiveness validations of the proposed control technique compared with the traditional approach are performed through the Matlab simulation. The results have been illustrated that the modified adaptive sliding mode control can decrease the error performance indexes to the minimum ISE at 1.041 m 2 and the zero percentage of overshoot while enables excellent stability and robustness even in the presence of parameter perturbations and disturbance.

Airborne Computer System Based Collision-Free Flight Path Finding Strategy Design for Drone Model

“…When the thrust force of (TF1) and (TF3) are increased while the thrust force of (TF2) and (TF4) are decreased, the aerodynamic torque effect generates a yawing moment (ψ) that makes the drone rotate around the z-axis. The state equation with the consideration of air drag of the dynamic drone model is taken from [5] as follows:…”

“…Table 7 demonstrates the effectiveness of this work by comparing the simulation results of the proposed algorithm with other types of algorithms taken from [5,15,24].…”

“…In addition, the drone quadcopter is useful in different fields such as monitoring systems and inspection systems in dangerous areas. These drone systems have different sizes and shapes [5].…”

“…Moreover, in [23] the model predictive of a nonlinear controller for UAV was proposed for tracking the desired trajectory and the constrained control of an UAV quadrotor system. In [5], a modified adaptive sliding mode control for trajectory tracking of mini-drone was presented for enhancing the performance of the mini-drone motion control. In addition, in [7] a second-order sliding mode controller with PSO tuning control algorithm was presented for achieving the desired altitude and attitude of the drone.…”

Performance Evaluation Based on Multi-UAV in Airborne Computer Network System

“…The drawbacks in [19,20] OMNET has been used, several drones and one ground station, the protocol problem arises when some messages are lost and there is a loss of time, complexity, and energy. In [21], the Modified Slip Mode Control System (MASMC) for unmanned aerial vehicle movement control demonstrated the effectiveness of a non-linear adaptive control strategy when used for small quadcopter drones only. To apply altitude and trajectory tracking control, a modified adaptive slip mode algorithm was developed using an adaptive law based on the Lyapunov stability approach.…”