Trajectory tracking control of a quadrotor UAV based on sliding mode active disturbance rejection control

Abstract

This paper proposes a sliding mode active disturbance rejection control scheme to deal with trajectory tracking control problems for the quadrotor unmanned aerial vehicle (UAV). Firstly, the differential signal of the reference trajectory can be obtained directly by using the tracking differentiator (TD), then the design processes of the controller can be simplified. Secondly, the estimated values of the UAV's velocities, angular velocities, total disturbance can be acquired by using extended state observer (ESO), and the total disturbance of the system can be compensated in the controller in real time, then the robustness and anti-interference capability of the system can be improved. Finally, the sliding mode controller based on TD and ESO is designed, the stability of the closed-loop system is proved by Lyapunov method. Simulation results show that the control scheme proposed in this paper can make the quadrotor track the desired trajectory quickly and accurately.

Control Strategies and Novel Techniques for Autonomous Rotorcraft Unmanned Aerial Vehicles: A Review

“…The superiority of the proposed control structure was clearly demonstrated when compared to the conventional PID. Further, Zhang et al [174] displayed a sliding mode ADRC scheme to improve tracking control of a quadcopter system with an efficient disturbance rejection capability. The proposed control strategy performed excellently in comparison to the classical ADRC.…”

An Online Adaptive Control Strategy for Trajectory Tracking of Quadrotors Based on Fuzzy Approximation and Robust Sliding Mode Algorithm

“…In general, it is difficult to control the aircraft under highfrequency random disturbances and multi-frequency random disturbances. Here, to solve this problem, two forms of wind gusts were presented in the simulation: high frequency random disturbances (high frequency sine signal) [17,27,[35][36] and multi frequency random disturbances (square wave signal) [34,37]. The disturbances used in this tracking simulation consist of the following two parts:…”

“…This is especially true for hybrid controllers combined with fuzzy control algorithm. By introducing human experience into the controlling process, these intelligent algorithms guarantee an accurate approximation to the model's uncertainties [30][31][32][33][34]. When combined with an adaptive strategy, the intelligent control system allowed for real-time regulation of system parameters according to its actual state [35][36][37].…”

“… [34]. { = 1.5 (0.5 ) = 1.5 (0.5 ) = = The simulation time is 20s, initial position coordinates and attitude of the quadrotor were set as [ , , ] = [0 0 0] , [ , , ] = [0 0 0].…”

Sliding mode tracking control using finite‐time differentiator in high‐orders nonlinear systems with output stochastic signals

“…Specifically, SMC using ESO realizes voltage signals estimation for three‐phase two‐level power converters 12 . To the UAV system, 13 the SMC controller combines ESO to obtain the velocities and angular velocities signals to track the objective trajectory. Although ESO has been widely used in several applications to estimate the unknown states even the external disturbances, the disturbances signals in these complex nonlinear systems is deterministic.…”