Starting from the current problems of drones,this paper aims to find solutions that are suitable for sustainable development.This paper mainly introduces a method of adjusting the control authority of UAV,including ma...Starting from the current problems of drones,this paper aims to find solutions that are suitable for sustainable development.This paper mainly introduces a method of adjusting the control authority of UAV,including managing the process,obtaining the operation of maneuvering performance and controlling the accuracy rate.This method enhances the fault tolerance of UAV flight control,improves the high precision of UAV flight control,and avoids the occurrence of accidents caused by improper control,such as“drone damage”or“personal injury to others”.展开更多
Tradeoff analysis of the factors,including external environment and unmanned aerial vehicle(UAV)aerodynamic attributes,which affect longitudinal carrier landing performance,is important for small UAV.First,small UAV l...Tradeoff analysis of the factors,including external environment and unmanned aerial vehicle(UAV)aerodynamic attributes,which affect longitudinal carrier landing performance,is important for small UAV.First,small UAV longitudinal carrier landing system is established,as well as the nonlinear dynamics and kinematics model,and then the longitudinal flight control system using backstepping technology with minimum information about the aerodynamic is designed.To assess the landing performance,a variety of influencing factors are considered,resulting in the constraints of aerodynamic attributes of carrier UAV.The simulation results show that the severe sea condition has the greatest influence on landing dispersion,while air wake is the primary factor on impact velocity.Among the longitudinal aerodynamic parameters,the lift curve slope is the most important factor affecting the landing performance,and increasing lift curve slope can improve the landing performance significantly.A better system performance will be achieved when the lift curve slope is larger than 2per radian.展开更多
An attempt is made to apply modern control technology to the roll and yaw control of a rudderless quad-tiltrotor Unmanned Aerial Vehicle(UAV)in the latter part of the flight mode transition,where aerodynamic forces on...An attempt is made to apply modern control technology to the roll and yaw control of a rudderless quad-tiltrotor Unmanned Aerial Vehicle(UAV)in the latter part of the flight mode transition,where aerodynamic forces on the tiltrotor’s wings start to take effect.A predictor-based adaptive roll and yaw controller is designed to compensate for system uncertainties and parameter changes.A dynamics model of the tiltrotor is built.A Radial-Basis Function(RBF)neural network and offline adaptation method are used to reduce flight controller workload and cope with the nonlinearities in the controls.Simulations are conducted to verify the reference model response tracking and yaw-roll control decoupling ability of the adaptive controller,as well as the validity of the offline adaptation method.Flight tests are conducted to confirm the ability of the adaptive controller to track different roll and yaw reference model responses.The decoupling of roll and yaw controls is also tested in flight via coordinated turn maneuvers with different rotor tilt angles.展开更多
The mathematical model of quadcopter-unmanned aerial vehicle （UAV） is derived by using two approaches： One is the Newton-Euler approach which is formulated using classical meehanics; and other is the Euler-Lagrange...The mathematical model of quadcopter-unmanned aerial vehicle （UAV） is derived by using two approaches： One is the Newton-Euler approach which is formulated using classical meehanics; and other is the Euler-Lagrange approach which describes the model in terms of kinetic （translational and rotational） and potential energy. The proposed quadcopter＇s non-linear model is incorporated with aero-dynamical forces generated by air resistance, which helps aircraft to exhibits more realistic behavior while hovering. Based on the obtained model, the suitable control strategy is developed, under which two effective flight control systems are developed. Each control system is created by cascading the proportional-derivative （PD） and T-S fuzzy controllers that are equipped with six and twelve feedback signals individually respectively to ensure better tracking, stabilization, and response. Both pro- posed flight control designs are then implemented with the quadcopter model respectively and multitudinous simulations are conducted using MATLAB/Simulink to analyze the tracking performance of the quadcopter model at various reference inputs and trajectories.展开更多
Modular Unmanned Aerial Vehicles(UAVs)can adapt to rapidly changing payload requirements based on the shape and weight of the load by adding or subtracting units,reconfiguring,or changing the type of units.The existin...Modular Unmanned Aerial Vehicles(UAVs)can adapt to rapidly changing payload requirements based on the shape and weight of the load by adding or subtracting units,reconfiguring,or changing the type of units.The existing research has addressed aerial docking and hover control post-docking but fails to achieve coordinated flight following combination,leading to delayed response and oscillations as the number of UAV units increases.Moreover,the configuration of modular UAVs is complex and variable,making it challenging to adjust the controller parameters of each unit online.Therefore,this paper presents:(A)Adaptive attitude allocation method for different combined UAV configurations:establishing a mapping relationship between constant controller parameters of the unit and the combination angular acceleration.The desired torque of the combination is allocated based on the size of the lever arm,enabling adaptive attitude control of the combination for varying configurations by controlling the attitude of the local unit;(B)A power allocation strategy based on a leader-wingman mode:employing a leader to control the entire combination,distributing the combination’s force and torque to wingman units according to the mapping relationship of the attitude allocation method.This transforms the complex control of the combination into unit control in the leader-wingman mode.Compared to current average allocation methods,the step response of attitude angle improves by about 60% on average,and spatial trajectory tracking increases by an average of 11.5%.As the number of units grows,the response of the combination becomes similar to that of a single,independently flying UAV,resolving the oscillation issue in combined flight.Additionally,this approach eliminates the need to change the controller parameters of all units,facilitating convenient reconfiguration and coordinated flight for modular UAVs post-combination.展开更多
The airborne base station(ABS) can provide wireless coverage to the ground in unmanned aerial vehicle(UAV) cellular networks.When mobile users move among adjacent ABSs,the measurement information reported by a single ...The airborne base station(ABS) can provide wireless coverage to the ground in unmanned aerial vehicle(UAV) cellular networks.When mobile users move among adjacent ABSs,the measurement information reported by a single mobile user is used to trigger the handover mechanism.This handover mechanism lacks the consideration of movement state of mobile users and the location relationship between mobile users,which may lead to handover misjudgments and even communication interrupts.In this paper,we propose an intelligent handover control method in UAV cellular networks.Firstly,we introduce a deep learning model to predict the user trajectories.This prediction model learns the movement behavior of mobile users from the measurement information and analyzes the positional relations between mobile users such as avoiding collision and accommodating fellow pedestrians.Secondly,we propose a handover decision method,which can calculate the users' corresponding receiving power based on the predicted location and the characteristic of air-to-ground channel,to make handover decisions accurately.Finally,we use realistic data sets with thousands of non-linear trajectories to verify the basic functions and performance of our proposed intelligent handover controlmethod.The simulation results show that the handover success rate of the proposed method is 8% higher than existing methods.展开更多
The technology of attitude control for quadrotor unmanned aerial vehicles(UAVs) is one of the most important UAVs' research areas.In order to achieve a satisfactory operation in quadrotor UAVs having proportional ...The technology of attitude control for quadrotor unmanned aerial vehicles(UAVs) is one of the most important UAVs' research areas.In order to achieve a satisfactory operation in quadrotor UAVs having proportional integration differential(PID) controllers,it is necessary to appropriately adjust the controller coefficients which are dependent on dynamic parameters of the quadrotor UAV and any changes in parameters and conditions could affect desired performance of the controller.In this paper,combining with PID control and fuzzy logic control,a kind of fuzzy self-adaptive PID control algorithm for attitude stabilization of the quadrotor UAV was put forward.Firstly,the nonlinear model of six degrees of freedom(6-DOF) for quadrotor UAV is established.Secondly,for obtaining the attitude of quadrotor,attitude data fusion using complementary filtering is applied to improving the measurement accuracy and dynamic performance.Finally,the attitude stabilization control simulation model of the quadrotor UAV is build,and the self-adaptive fuzzy parameter tuning rules for PID attitude controller are given,so as to realize the online self-tuning of the controller parameters.Simulation results show that comparing with the conventional PID controller,this attitude control algorithm of fuzzy self-adaptive PID has a better dynamic response performance.展开更多
基金funded by the special construction project of“Innovation Strong School Project”in Guangdong Province and Zhujiang College,South China Agricultural University.The Project name is“Destructive innovative tourism products based on UAV AR Interactive Game”(Project No.:201812623005S)funded by the Higher Education Teaching Reform Project in 2017(No.706),namely The application,exploration and practice of WeChat public platform in college curriculum teaching under the environment of mobile Internet Plus.
文摘Starting from the current problems of drones,this paper aims to find solutions that are suitable for sustainable development.This paper mainly introduces a method of adjusting the control authority of UAV,including managing the process,obtaining the operation of maneuvering performance and controlling the accuracy rate.This method enhances the fault tolerance of UAV flight control,improves the high precision of UAV flight control,and avoids the occurrence of accidents caused by improper control,such as“drone damage”or“personal injury to others”.
基金supported by the National Nature Science Foundation of China(Nos.61304223,61403197)the Aeronautical Science Foundation of China(No.2013ZA52002)the Research Fund for the Doctoral Program of Higher Education of China(No.20123218120015)
文摘Tradeoff analysis of the factors,including external environment and unmanned aerial vehicle(UAV)aerodynamic attributes,which affect longitudinal carrier landing performance,is important for small UAV.First,small UAV longitudinal carrier landing system is established,as well as the nonlinear dynamics and kinematics model,and then the longitudinal flight control system using backstepping technology with minimum information about the aerodynamic is designed.To assess the landing performance,a variety of influencing factors are considered,resulting in the constraints of aerodynamic attributes of carrier UAV.The simulation results show that the severe sea condition has the greatest influence on landing dispersion,while air wake is the primary factor on impact velocity.Among the longitudinal aerodynamic parameters,the lift curve slope is the most important factor affecting the landing performance,and increasing lift curve slope can improve the landing performance significantly.A better system performance will be achieved when the lift curve slope is larger than 2per radian.
文摘An attempt is made to apply modern control technology to the roll and yaw control of a rudderless quad-tiltrotor Unmanned Aerial Vehicle(UAV)in the latter part of the flight mode transition,where aerodynamic forces on the tiltrotor’s wings start to take effect.A predictor-based adaptive roll and yaw controller is designed to compensate for system uncertainties and parameter changes.A dynamics model of the tiltrotor is built.A Radial-Basis Function(RBF)neural network and offline adaptation method are used to reduce flight controller workload and cope with the nonlinearities in the controls.Simulations are conducted to verify the reference model response tracking and yaw-roll control decoupling ability of the adaptive controller,as well as the validity of the offline adaptation method.Flight tests are conducted to confirm the ability of the adaptive controller to track different roll and yaw reference model responses.The decoupling of roll and yaw controls is also tested in flight via coordinated turn maneuvers with different rotor tilt angles.
基金supported by the National Natural Science Foundation of China(Nos.61673209,61741313,61304223)the Aeronautical Science Foundation(Nos.2016ZA52009)+1 种基金the Jiangsu Six Peak of Talents Program(No.KTHY-027)the Fundamental Research Funds for the Central Universities(Nos.NJ20160026,NS2017015)
文摘The mathematical model of quadcopter-unmanned aerial vehicle （UAV） is derived by using two approaches： One is the Newton-Euler approach which is formulated using classical meehanics; and other is the Euler-Lagrange approach which describes the model in terms of kinetic （translational and rotational） and potential energy. The proposed quadcopter＇s non-linear model is incorporated with aero-dynamical forces generated by air resistance, which helps aircraft to exhibits more realistic behavior while hovering. Based on the obtained model, the suitable control strategy is developed, under which two effective flight control systems are developed. Each control system is created by cascading the proportional-derivative （PD） and T-S fuzzy controllers that are equipped with six and twelve feedback signals individually respectively to ensure better tracking, stabilization, and response. Both pro- posed flight control designs are then implemented with the quadcopter model respectively and multitudinous simulations are conducted using MATLAB/Simulink to analyze the tracking performance of the quadcopter model at various reference inputs and trajectories.
基金supported by the Funding of National Key Laboratory of Rotorcraft Aeromechanics,China(No.61422202108)the National Natural Science Foundation of China(No.52176009)the Postgraduate Research&Practice Innovation Program of NUAA,China(No.xcxjh20220214).
文摘Modular Unmanned Aerial Vehicles(UAVs)can adapt to rapidly changing payload requirements based on the shape and weight of the load by adding or subtracting units,reconfiguring,or changing the type of units.The existing research has addressed aerial docking and hover control post-docking but fails to achieve coordinated flight following combination,leading to delayed response and oscillations as the number of UAV units increases.Moreover,the configuration of modular UAVs is complex and variable,making it challenging to adjust the controller parameters of each unit online.Therefore,this paper presents:(A)Adaptive attitude allocation method for different combined UAV configurations:establishing a mapping relationship between constant controller parameters of the unit and the combination angular acceleration.The desired torque of the combination is allocated based on the size of the lever arm,enabling adaptive attitude control of the combination for varying configurations by controlling the attitude of the local unit;(B)A power allocation strategy based on a leader-wingman mode:employing a leader to control the entire combination,distributing the combination’s force and torque to wingman units according to the mapping relationship of the attitude allocation method.This transforms the complex control of the combination into unit control in the leader-wingman mode.Compared to current average allocation methods,the step response of attitude angle improves by about 60% on average,and spatial trajectory tracking increases by an average of 11.5%.As the number of units grows,the response of the combination becomes similar to that of a single,independently flying UAV,resolving the oscillation issue in combined flight.Additionally,this approach eliminates the need to change the controller parameters of all units,facilitating convenient reconfiguration and coordinated flight for modular UAVs post-combination.
基金supported in parts by the National Natural Science Foundation of China for Distinguished Young Scholar under Grant 61425012the National Science and Technology Major Projects for the New Generation of Broadband Wireless Communication Network under Grant 2017ZX03001014
文摘The airborne base station(ABS) can provide wireless coverage to the ground in unmanned aerial vehicle(UAV) cellular networks.When mobile users move among adjacent ABSs,the measurement information reported by a single mobile user is used to trigger the handover mechanism.This handover mechanism lacks the consideration of movement state of mobile users and the location relationship between mobile users,which may lead to handover misjudgments and even communication interrupts.In this paper,we propose an intelligent handover control method in UAV cellular networks.Firstly,we introduce a deep learning model to predict the user trajectories.This prediction model learns the movement behavior of mobile users from the measurement information and analyzes the positional relations between mobile users such as avoiding collision and accommodating fellow pedestrians.Secondly,we propose a handover decision method,which can calculate the users' corresponding receiving power based on the predicted location and the characteristic of air-to-ground channel,to make handover decisions accurately.Finally,we use realistic data sets with thousands of non-linear trajectories to verify the basic functions and performance of our proposed intelligent handover controlmethod.The simulation results show that the handover success rate of the proposed method is 8% higher than existing methods.
基金National Natural Science Foundation of China(No.61374114)Natural Science Foundation of Liaoning Province,China(No.2015020022)the Fundamental Research Funds for the Central Universities,China(No.3132015039)
文摘The technology of attitude control for quadrotor unmanned aerial vehicles(UAVs) is one of the most important UAVs' research areas.In order to achieve a satisfactory operation in quadrotor UAVs having proportional integration differential(PID) controllers,it is necessary to appropriately adjust the controller coefficients which are dependent on dynamic parameters of the quadrotor UAV and any changes in parameters and conditions could affect desired performance of the controller.In this paper,combining with PID control and fuzzy logic control,a kind of fuzzy self-adaptive PID control algorithm for attitude stabilization of the quadrotor UAV was put forward.Firstly,the nonlinear model of six degrees of freedom(6-DOF) for quadrotor UAV is established.Secondly,for obtaining the attitude of quadrotor,attitude data fusion using complementary filtering is applied to improving the measurement accuracy and dynamic performance.Finally,the attitude stabilization control simulation model of the quadrotor UAV is build,and the self-adaptive fuzzy parameter tuning rules for PID attitude controller are given,so as to realize the online self-tuning of the controller parameters.Simulation results show that comparing with the conventional PID controller,this attitude control algorithm of fuzzy self-adaptive PID has a better dynamic response performance.
基金supported by National Natural Science Foundation of China(61174102)Jiangsu Natural Science Foundation of China(SBK20130033)+1 种基金Aeronautical Science Foundation of China 20145152029)Specialized Research Fund for the Doctoral Program of Higher Education(20133218110013)
基金supported by the National Nature Science Foundation of China(61304223)the Aeronautical Science Foundation of China(2016ZA52009)the Research Fund for the Doctoral Program of Higher Education of China(20123218120015)
