Simplified fuzzy ‐Padé controller for attitude control of quadrotor helicopters

Non‐linear adaptive tracking control for quadrotor aerial robots under uncertain dynamics

“…The control input F , 𝜏 x , 𝜏 y , and 𝜏 z with the consideration of adaptive laws for dynamic uncertainties m, I Theorem 1. Consider the quadrotor system described by ( 8)- (13). For the force and torque inputs given by (16) with the nominal control inputs as Proof.…”

“…Figure 11 illustrates that the quadrotor system is able to follow the desired position and velocity. While the object with the FIGURE 12 Tracking errors of position and Euler angle of the quadrotor system to follow a minimum snap trajectory with the use of the proposed adaptive controller while added mass in the midway FIGURE 13 Motor speeds of the quadrotor using the proposed controller in minimum snap trajectory FIGURE 14 Trajectory tracking in 3-D, motor thrust, and estimate of mass in the experiment using the proposed controller to follow a minimum snap trajectory while attaching/detaching mass in the midway FIGURE Position and angle tracking using the proposed adaptive controller for the quadrotor track a circle while attaching/detaching object with unknown mass in the midway mass of 50g is added at around t = 20 s and detached from at around t = 65 s, the tracking performance is ensured under the change of mass. The tracking of Euler angles and the position tracking errors are illustrated in Figure 12.…”

“…Although quadrotor systems have the excellence in vertical takeoff and landing (VTOL) with simple mechanical design, the non‐linear, strong coupling, and underactuated characteristics make the development of control algorithms more crucial than grounded mobile robots. Over the past years, researchers have paid significant attention in proposing efficient control schemes for quadrotor systems by PID controller [8, 9], feedback linearization [10, 11], model predictive control [12], geometric control [2], fuzzy control [13], and sliding mode control [14, 15]. The altitude and position tracking control for linearized quadrotor systems with wind gusts is addressed by model predictive control [12].…”

Adaptive Interval Type-2 Fuzzy Logic Control of a Three Degree-of-Freedom Helicopter

“…The resultant controller prevented the angle drift in real-time. Then, altitude control of the quadrotor is developed using a simplified fuzzy controller [12]. A simulation model is used to demonstrate the effectiveness of the proposed controller along with several performance indices such as rise time, settling time, percentage overshoot, integral absolute error, central processing unit time, and energy consumption.…”

“…A mathematically ill-defined designed controller that is subjected to various disturbances and uncertainties can be approximated with computational intelligence tool, such as artificial neural networks and fuzzy logic systems, since these intelligent tools with high accuracy can uniformly approximate any real continuous function [28][29][30][31]. Hence, such an advancement in neural network, can lead to modeling many complex models [11,12,32,33]. The conventional adaptive control strategies perform well with structured (parametric) uncertainties, but fail to achieve robustness in the presence of unstructured (non-parametric) uncertainties like external disturbances.…”