Adaptive backstepping flight control for a mini‐UAV

Abstract

The paper presents the design of a mini-unmanned aerial vehicle (UAV) attitude controller using the backstepping method. Starting from the nonlinear dynamic equations of the mini-UAV, by using the backstepping method, the authors of this paper obtained the expressions of the elevator, rudder, and aileron deflections, which stabilize the UAV at each moment to the desired values of the attitude angles. The attitude controller controls the attitude angles, the angular rates, the angular accelerations, and other variables describing the UAV longitudinal and lateral motions. The designed controller has been implemented in Matlab/Simulink environment, and its effectiveness has been tested with a campaign of numerical simulations using data from the UAV flight tests.

Research on Intelligent Flight Software Robot Based on Internet of Things

“…The French Ballistic and Pneumatic Research Laboratory [60] is designed based on the output feedback controller of sensors such as vision and gyroscope, which can improve the stability of the closed-loop control system of the aircraft. The Automation System Laboratory of the Federal Institute of Technology, Zurich, Switzerland [61,62]. Design and implementation of a nonlinear controller for six rotorcraft, which is designed to improve aircraft problems based on problems such as model uncertainty and external disturbances Control precision and its robustness.…”

A review of dielectrophoretic separation and classification of non‐biological particles

“…Lungu [106] presented a batch method to separate dry silver and gold particles from mineral waste (dielectric particles) at ppm‐level concentrations by applying a uniform ac electric field. The field gradient is induced by the dielectric mineral waste particles in the mixture.…”

Immersion and invariance control for Euler angles of a fixed‐wing unmanned aerial vehicle

“…Proportional-Integral-Derivative control (PID) [1], Linear Quadratic Regulator control (LQR), and Linear Quadratic Gaussian control (LQG) [2]. Besides, based on nonlinear models, there are studies that utilize model predictive [3], H ∞ [4][5][6], dynamic inversion [7], disturbance observer-based [8], sliding mode [9], adaptive sliding mode [10], adaptive model predictive [11], and adaptive backstepping control methods [12][13][14][15][16]. On the other hand, designing stabilizing controllers considering fault conditions is also an important problem for UAVs.…”

“…where i = p, q, r. By substituting the partial derivates of the mappings 𝜋 p , 𝜋 q , 𝜋 r with respect to x 𝜙 , x 𝜃 , x 𝜓 into (15) and utilizing system equations (1) for the derivative of x 𝜙 , x 𝜃 , x 𝜓 , x p , x q , x r in (15), the dynamics of the off-the-manifold coordinates can be written as follows:…”