Enhanced Backstepping Controller Design with Application to Autonomous Quadrotor Unmanned Aerial Vehicle

Abstract

Quadrotor unmanned aerial vehicle (UAV) is an underactuated multi-input and multi-output (MIMO) system which has nonlinear dynamic behavior such as high coupling degree and unknown nonlinearities. It is a great challenge to design a quadrotor control system due to these features. In this paper, the contribution is focused on the backstepping-based robust control design of the quadrotor UAV. Firstly, the dynamic model of the aerial vehicle is mathematically formulated. Then, a robust controller is designed for the stabilization and tracking control of the vehicle. The developed robust control system comprises a backstepping and a proportional-derivative (PD) controller. Backstepping is a recursive design methodology that uses Lyapunov theorem which can guarantee the stability of the nominal model system, while PD control is used to attenuate the effects caused by system uncertainties. For the problem of determining the backstepping control parameters, particle swarm optimization (PSO) algorithm has been employed. In addition, the genetic algorithm (GA) technique is also adopted for the purpose of performance comparison with PSO scheme. Finally, the designed controller is experimentally evaluated on a quadrotor simulation environment to demonstrate the effectiveness and merits of the theoretical development.

Vertical surface contact with a Micro Air Vehicle

“…The number of projects which consider an UAV system with a manipulator has considerably and suddenly increased in recent years. 22–26 However, these initial works on aerial manipulation focused on the stability of the system in controlled scenarios where the main issue is the weight, and the disturbance induced by the manipulator.…”

Embedded Flight Control Based on Adaptive Sliding Mode Strategy for a Quadrotor Micro Air Vehicle

“…Hence, tasks conducted by these MAVs require control approaches able to provide accuracy, efficient consumption of energy, and robustness. In order to control a quadrotor MAV subject to uncertainties and external disturbances, adaptive control [4], robust control [5], optimal control [6], and intelligent control [7] have been developed. Nevertheless, the aforementioned results depend of the knowledge of the system or a training process for the last one.…”

Fuzzy robust backstepping with estimation for the control of a robot manipulator

“…This control method is applicable to nonlinear systems. With those useful and appealing properties this control method has found application areas such as mobile robot control (Chang et al, 2004; Rudra et al, 2016), antilock brake system control (Wang et al, 2005; Yu et al, 2015), unmanned aerial vehicle control (Mahony and Hamel, 2005; Mohd Basri et al, 2015), and so on. In recent years, controllers that bring together the backstepping and fuzzy logic approaches were also designed.…”