An Incremental Back-Stepping Control method for Compound Lift Unmanned Aerial Vehicle

This paper proposes the use of a novel control method based on interconnection and damping assignment–passivity-based control (IDA-PBC) in order to address the aerial physical interaction (APhI) problem for a quadrotor unmanned aerial vehicle (UAV). The apparent physical properties of the quadrotor are reshaped in order to achieve better APhI performances, while ensuring the stability of the interaction through passivity preservation. The robustness of the IDA-PBC method with respect to sensor noise is also analyzed. The direct measurement of the external wrench, needed to implement the control method, is compared with the use of a nonlinear Lyapunov-based wrench observer and advantages/disadvantages of both methods are discussed. The validity and practicability of the proposed APhI method is evaluated through experiments, where for the first time in the literature, a lightweight all-in-one low-cost force/torque (F/T) sensor is used onboard of a quadrotor. Two main scenarios are shown: a quadrotor responding to external disturbances while hovering (physical human–quadrotor interaction), and the same quadrotor sliding with a rigid tool along an uneven ceiling surface (inspection/painting-like task).

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Tracking Control Design for Quadrotor Unmanned Aerial Vehicle

Defence Science Journal ◽

10.14429/dsj.67.9536 ◽

2017 ◽

Vol 67 (3) ◽

pp. 245 ◽

Cited By ~ 1

Author(s):

Sudhir Nadda ◽

A. Swarup

Keyword(s):

Unmanned Aerial Vehicle ◽

Control Strategy ◽

Tracking Control ◽

Control Method ◽

Sliding Mode ◽

Backstepping Control ◽

Control Technique ◽

Quadrotor Uav ◽

Attitude Tracking ◽

Aerial Vehicle

The model of a quadrotor unmanned aerial vehicle (UAV) is nonlinear and dynamically unstable. A flight controller design is proposed on the basis of Lyapunov stability theory which guarantees that all the states remain and reach on the sliding surfaces. The control strategy uses sliding mode with a backstepping control to perform the position and attitude tracking control. This proposed controller is simple and effectively enhance the performance of quadrotor UAV. In order to demonstrate the robustness of the proposed control method, White Gaussian Noise and aerodynamic moment disturbances are taken into account. The performance of the nonlinear control method is evaluated by comparing the performance with developed linear quadratic regulator and existing backstepping control technique and proportional-integral-derivative from the literature. The comparative performance results demonstrate the superiority and effectiveness of the proposed control strategy for the quadrotor UAV.

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Model predictive control of an unmanned aerial vehicle

Aircraft Engineering and Aerospace Technology ◽

10.1108/aeat-03-2015-0074 ◽

2017 ◽

Vol 89 (2) ◽

pp. 193-202 ◽

Cited By ~ 5

Author(s):

Halit Firat Erdogan ◽

Ayhan Kural ◽

Can Ozsoy

Keyword(s):

Unmanned Aerial Vehicle ◽

Predictive Control ◽

Control Method ◽

Relevant Literature ◽

Control Effort ◽

Content Type ◽

Multiple Input ◽

Predictive Controller ◽

Aerial Vehicle ◽

Mimo Model

Purpose The purpose of this paper is to design a controller for the unmanned aerial vehicle (UAV). Design/methodology/approach In this study, the constrained multivariable multiple-input and multiple-output (MIMO) model predictive controller (MPC) has been designed to control all outputs by manipulating inputs. The aim of the autopilot of UAV is to keep the UAV around trim condition and to track airspeed commands. Findings The purpose of using this control method is to decrease the control effort under the certain constraints and deal with interactions between each output and input while tracking airspeed commands. Originality/value By using constraint, multivariable (four inputs and seven outputs) MPC unlike the relevant literature in this field, the UAV tracked airspeed commands with minimum control effort dealing with interactions between each input and output under disturbances such as wind.

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Tracking control of multiple unmanned aerial vehicles incorporating disturbance observer and model predictive approach

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331219879858 ◽

2019 ◽

Vol 42 (5) ◽

pp. 951-964 ◽

Cited By ~ 2

Author(s):

Boyang Zhang ◽

Xiuxia Sun ◽

Shuguang Liu ◽

Xiongfeng Deng

Keyword(s):

Model Predictive Control ◽

Unmanned Aerial Vehicle ◽

Predictive Control ◽

Disturbance Observer ◽

Control Method ◽

Differential Evolution Algorithm ◽

Reference Trajectory ◽

Control Approach ◽

Unknown Disturbances ◽

Aerial Vehicle

This paper studies the disturbance observer-based model predictive control approach to deal with the unmanned aerial vehicle formation flight with unknown disturbances. The distributed control problem for a class of multiple unmanned aerial vehicle systems with reference trajectory tracking and disturbance rejection is formulated. Firstly, a local distributed controller is designed by using the model predictive control method to achieve stable tracking, where the local optimization problem is solved by an adaptive differential evolution algorithm. Then, a feedforward compensation controller is introduced by using a disturbance observer to estimate and compensate disturbances, and improve the ability of anti-interference. Besides, the stability of the proposed composite controller is analyzed as well. Finally, the simulation examples are provided to illustrate the validity of proposed control structure.

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INTEGRATED PID CONTROLLER DESIGN FOR AN UNMANNED AERIAL VEHICLE WITH STATIC STABILITY

The ANZIAM Journal ◽

10.1017/s1446181113000199 ◽

2013 ◽

Vol 54 (3) ◽

pp. 200-215 ◽

Cited By ~ 1

Author(s):

R. LI ◽

Y. J. SHI ◽

H. L. XU

Keyword(s):

Fuzzy Control ◽

Unmanned Aerial Vehicle ◽

Pid Controller ◽

Control Method ◽

Controller Design ◽

Design Method ◽

Design Procedure ◽

Static Stability ◽

Aerial Vehicle ◽

Rolling Mode

AbstractThis paper presents an integrated guidance and control (IGC) design method for an unmanned aerial vehicle with static stability which is described by a nonlinear six-degree-of-freedom (6-DOF) model. The model is linearized by using small disturbance linearization. The dynamic characteristics of pitching mode, rolling mode and Dutch rolling mode are obtained by analysing the linearized model. Furthermore, an IGC design procedure is also proposed in conjunction with a proportional–integral–derivative (PID) control method and fuzzy control method. A PID controller is applied in the control loop of the elevator and aileron, and the attitude angle and attitude angular velocity are used as compensation feedback, giving a simple and low-order control law. A fuzzy control method is applied to perform the cross-coupling control of rolling and yawing. Finally, the 6-DOF simulation shows the effectiveness of the developed method.

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