Quadrotor unmanned aerial vehicle (UAV) has superior characteristics such as ability to take off and land vertically, to hover in a stable air condition and to perform fast maneuvers. However, developing a high-performance quadrotor UAV controller is a difficult problem as quadrotor is an unstable and underactuated nonlinear system. The effort in this article focuses on designing and optimizing an autonomous quadrotor UAV controller. First, the aerial vehicle's dynamic model is presented. Then it is suggested an optimal backstepping controller (OBC). Traditionally, backstepping controller (BC) parameters are often selected arbitrarily. The gravitational search algorithm (GSA) is used here to determine the BC parameter optimum values. In the algorithm, the control parameters are calculated using an integral absolute error to minimize the fitness function. As the control law is based on the theorem of Lyapunov, the asymptotic stability of the scheme can be ensured. Finally, several simulation studies are conducted to show the efficacy of the suggested OBC.

https://www.beei.org/index.php/EEI/article/download/2159/1569

Optimal backstepping control of quadrotor UAV using gravitational search optimization algorithm

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An adaptive neural observer design is presented for the nonlinear quadrotor unmanned aerial vehicle (UAV). This proposed observer design is motivated by the practical quadrotor where the whole dynamical model of system is unavailable. In this paper, dynamics of the quadrotor UAV system and its state space model are discussed and a neural observer design, using a back propagation algorithm is presented. The steady state error is reduced with the neural network term in the estimator design and the transient performance of the system is improved. This proposed methodology reduces the number of sensors and weight of the quadrotor which results in the decrease of manufacturing cost. A Lyapunov-based stability analysis is utilized to prove the convergence of error to the neighborhood of zero. The performance and capabilities of the design procedure are demonstrated by the Simulation results.

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An Adaptive Neural Network State Estimator for Quadrotor Unmanned Air Vehicle

PID control of quadrotor UAVs: A survey

Purpose




The purpose of this paper is to present a control strategy which uses two independent PID controllers to realize the hovering control for unmanned aerial systems (UASs). In addition, the aim of using two PID controller is to achieve the position control and velocity control simultaneously.




Design/methodology/approach




The dynamic of the UASs is mathematically modeled. One PID controller is used for position tracking control, while the other is selected for the vertical component of velocity tracking control. Meanwhile, fuzzy logic algorithm is presented to use the actual horizontal component of velocity to compute the desired position.




Findings




Based on this fuzzy logic algorithm, the control error of the horizontal component of velocity tracking control is narrowed gradually to be zero. The results show that the fuzzy logic algorithm can make the UASs hover still in the air and vertical to the ground.




Social implications




The acquired results are based on simulation not experiment.




Originality/value




This is the first study to use two independent PID controllers to realize stable hovering control for UAS. It is also the first to use the velocity of the UAS to calculate the desired position.

Yunzhou Sun

Papers

Fuzzy logic algorithm of hovering control for the quadrotor unmanned aerial system