Abstract

The control of quadrotor equipped with a robotic arm has received growing challenges. This article proposes a new adaptive control strategy of quadrotor equipped with a 2-degree-of-freedom robotic arm. To consider the positional variety of the center of gravity caused by the motion of the robotic arm, the kinematic and dynamic models are built. Based on the presented models, a backstepping and sliding mode controller with a terminal sliding mode manifold is first applied to cope with the condition in which the robotic arm is motionless relative to the quadrotor. As the evolvement of the backstepping and sliding mode controller, a novel adaptive backstepping and sliding mode controller is then designed for the vehicle with the robotic arm wavering. The robustness and effectiveness of the proposed control law are investigated through both simulations and flight tests. With the proposed control laws, several simulations are conducted in conditions of both a variable and a constant center of gravity, and the performance of hovering is tested with a variable center of gravity in an experiment. Overall results show that the proposed adaptive backstepping control could estimate and compensate the variable center of gravity which may seriously influence the stabilization of quadrotor flying in the air.

Highlights

  • Unmanned aerial vehicles (UAVs), which could hover and move around in three-dimensional Euclidean space, are increasingly used as human assistants in various aspects

  • The motion of robotic arms may lead to positional variety of the center of gravity (COG) in the

  • The kinematic and dynamic models are presented at first considering the robotic arm as an element affecting the COG of the vehicle

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Summary

Introduction

Unmanned aerial vehicles (UAVs), which could hover and move around in three-dimensional Euclidean space, are increasingly used as human assistants in various aspects. To consider the quadrotor and the attached robot arm as two independent entities, an adaptive controller is built which could estimate and compensate for dynamical changes in the COG of the UAV in real time. The kinematic and dynamic models are presented at first considering the robotic arm as an element affecting the COG of the vehicle.

Results
Conclusion

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