A Comparative Study of Control Methods for X3D Quadrotor Feedback Trajectory Control

Abstract

Unmanned aerial vehicles (UAVs), particularly quadrotor, have seen steady growth in use over the last several decades. The quadrotor is an under-actuated nonlinear system with few actuators in comparison to the degree of freedom (DOF); hence, stabilizing its attitude and positions is a significant challenge. Furthermore, the inclusion of nonlinear dynamic factors and uncertainties makes controlling its maneuverability more challenging. The purpose of this research is to design, implement, and evaluate the effectiveness of linear and nonlinear control methods for controlling an X3D quadrotor’s intended translation position and rotation angles while hovering. The dynamics of the X3D quadrotor model were implemented in Simulink. Two linear controllers, linear quadratic regulator (LQR) and proportional integral derivate (PID), and two nonlinear controllers, fuzzy controller (FC) and model reference adaptive PID Controller (MRAPC) employing the MIT rule, were devised and implemented for the response analysis. In the MATLAB Simulink Environment, the transient performance of nonlinear and linear controllers for an X3D quadrotor is examined in terms of settling time, rising time, peak time, delay time, and overshoot. Simulation results suggest that the LQR control approach is better because of its robustness and comparatively superior performance characteristics to other controllers, particularly nonlinear controllers, listed at the same operating point, as overshoot is 0.0% and other factors are minimal for the x3D quadrotor. In addition, the LQR controller is intuitive and simple to implement. In this research, all control approaches were verified to provide adequate feedback for quadrotor stability.

Simultaneous arm morphing quadcopter and autonomous flight system design

“…The movements of the four propellers can be examined under two separate subheadings. These are translational and rotational dynamics (Shakeel et al, 2022).…”

“…The movements of the four propellers can be examined under two separate subheadings. These are translational and rotational dynamics (Shakeel et al , 2022). Here, F is the total force applied to the centroid of the four propellers, m is the mass of the four propellers and can be rewritten in the body frame as follows (Eraslan et al , 2020a, 2020b).…”

Design and Real-Time Implementation of a Cascaded Model Predictive Control Architecture for Unmanned Aerial Vehicles

“…To meet these requirements, some control strategies have been developed [2][3][4][5][6][7]. First, the well-known PID controller, which can handle both linear and nonlinear systems, can achieve zero steady-state errors with a simple-to-understand tuning principle [2].…”

“…First, the well-known PID controller, which can handle both linear and nonlinear systems, can achieve zero steady-state errors with a simple-to-understand tuning principle [2]. A linear-quadratic regulator (LQR) control can be easily implemented and can handle MIMO systems [3]. Feedback linearization (FL) provides the capacity to linearize a model with a model inversion procedure instead of making linearization assumptions [4].…”

A PyQt6-Based Platform for Real-Time Control and Monitoring of a Quadrotor Multibody System Using ROS2 and Gazebo

“…Backstepping is a recursive design method for designing controllers for nonlinear systems [17]. The Linearized Controller is a technique used to approximate nonlinear systems by a linear one around a given operating point [18]. The selection of a control strategy relies on the system's characteristics and the particular demands of the application.…”