An Improved Design of an Adaptive Sliding Mode Controller for Chattering Attenuation and Trajectory Tracking of the Quadcopter UAV

Abstract

Quadcopter unmanned aerial vehicles (UAVs) systems are receiving remarkable attention from researchers due to their daily use in numerous applications, particularly at the current time where UAVs are playing a significant role in combating the COVID-19 pandemic. This paper is concerned with the problem of UAV navigation and control in the presence of uncertainty and external disturbances. It addresses this issue by proposing an improved adaptive sliding mode control (IASMC). Improved control law generates an adaptive switching gain achieving fast adaptation and robustness against both parameter uncertainties and external disturbances. Simultaneously, the control law allows for maintaining robust trajectory tracking with chattering attenuation. The performance of the proposed IASMC controller has been evaluated using a Matlab/Simulink platform. The obtained simulation results showed that the proposed IASMC control scheme as an inner loop controller is robust when dealing with uncertainties and can effectively track the desired trajectories while significantly attenuating the well-known chattering introduced by the SMC controller.

Adaptive Continuous Sliding Mode Control of Buck Converters With Multidisturbances Based on Zero-Crossing Detection

“…Then by combining (31) and (34), it can be obtained that Step 2: This step is to prove the existence of ṡ(t B ) = 0 while the condition in Theorem 1 is satisfied.…”

“…And the literature [33] proposes a compound control method using an improved non-singular fast terminal sliding mode controller and the disturbance observer. Although the addition of the disturbance observers complements the SMC to some extent, which provides the possible control schemes for improving the response time and steady-state accuracy for the control of buck converters, this combination still suffers from two fatal drawbacks derived from traditional first-order SM (1-SM), i.e., the chattering phenomenon and the problem of unnecessary constant high control gain [34], [35].…”

Fuzzy Gain-Scheduling PID for UAV Position and Altitude Controllers

“…To improve the UAV flight performance, several researchers proposed advanced approaches for stabilizing the attitude and improving the UAV positions and tracking [ 13 , 14 ]. Most of these works are based on fuzzy logic [ 15 , 16 ], sliding-mode controller (SMC) [ 17 ], neural network-based control [ 18 ], backstepping-based adaptive control [ 19 ], and a linear quadratic regulator (LQR) [ 20 ], among others.…”

Improved Performance in Quadrotor Trajectory Tracking Using MIMO PIλ-D Control

“…Moreover, an adaptive nonlinear tracking controller was developed by [23], [24] in 2019. In addition to this, improvements in sliding mode control were also done [25], [26]. Then an interesting study was published by [27] in 2019 where a neural network-based quadcopter UAV system was introduced.…”