基于ADRC控制器的六旋翼姿态控制研究
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摘要
六旋翼无人机是带有冗余的复杂飞行系统。而六旋翼无人机的未建模动态、外扰和内扰,都会对无人机的姿态控制产生影响,增加控制的难度。为了提高六旋翼无人机系统的鲁棒性和动态性能,提出基于自抗扰控制器的六旋翼控制方案。先用二阶微分跟踪器对控制信号进行平滑和微分,然后用扩张状态观测器对未知扰动进行观测,然后利用非线性控制律进行补偿,把内扰和外扰统一进行观测,提高了系统的稳定性和抗扰能力。通过仿真验证和对比,发现该控制系统对比传统PID系统具有更强的抗扰能力。并且不会出现明显的超调,控制带宽对比串级PID有小幅提升。表明该控制系统具有较好的动态性能以及抗扰能力和鲁棒性。
The six rotor UAV is a complex flight system with redundancy. The unmodeled dynamics, external disturbances and internal disturbances of the six-rotor UAV affect the attitude control of the UAV and increase the difficulty of the control.In order to improve the robustness and dynamic performance of the six-rotor UAV system, a six-rotor control scheme based on ADRC is proposed. First, the two order differential tracker is used to smooth and differential the control signal. Then the unknown disturbance is observed with an extended state observer, and then the nonlinear control law is used to compensate the disturbance. The internal disturbance and the external disturbance are observed, and the stability and anti-interference ability of the system are improved. Through simulation verification and comparison, it is found that the control system has stronger anti-jamming ability compared with the traditional PID system. And there will be no obvious overshoot, and the control bandwidth has a slight improvement compared with the cascade PID. It shows that the control system has better dynamic performance, disturbance rejection and robustness.
The six rotor UAV is a complex flight system with redundancy. The unmodeled dynamics, external disturbances and internal disturbances of the six-rotor UAV affect the attitude control of the UAV and increase the difficulty of the control.In order to improve the robustness and dynamic performance of the six-rotor UAV system, a six-rotor control scheme based on ADRC is proposed. First, the two order differential tracker is used to smooth and differential the control signal. Then the unknown disturbance is observed with an extended state observer, and then the nonlinear control law is used to compensate the disturbance. The internal disturbance and the external disturbance are observed, and the stability and anti-interference ability of the system are improved. Through simulation verification and comparison, it is found that the control system has stronger anti-jamming ability compared with the traditional PID system. And there will be no obvious overshoot, and the control bandwidth has a slight improvement compared with the cascade PID. It shows that the control system has better dynamic performance, disturbance rejection and robustness.
引文
[1]Johnson,C,D.Real-Time Disturbance-Obser vers;Origin and Evolution of the Idea Part 1:The Early Years[C]//SSST 2008 40th Southeastern Symposium on System Theory.IEEE,2008:88-91.
[2]CHEN W,YANG J,GUO L,et al.DisturbanceObserver-Based Control and Related Methods-An Ove rview[J].Industrial Electronics,IEEE Transactions on IEEEJournals&Magazines,2016,63(2):1083-1095.
[3]韩京清.自抗扰控制技术:估计补偿不确定因素的控制技术[M].北京:国防工业出版社,2008.
[4]高志强.浅谈工程控制的信息问题[J].系统科学与数学,2016,36(7):908-923.
[5]韩京清,王伟.非线性跟踪─微分器[J].系统科学与数学,1994,(2):177-183.
[6]韩京清,袁露林.跟踪-微分器的离散形式[J].系统科学与数学,1999,(3):268-273.
[7]韩京清.一类不确定对象的扩张状态观测器[J].控制与决策,1995,(1):85-88.
[8]韩京清.非线性系统的状态观测器[J].控制与决策,1990,(3):57-60.
[9]韩京清.非线性状态误差反馈控制律─NLSEF[J].控制与决策,1995,(3):221-225,231.
[10]韩京清,许可康.用状态反馈实现稳定的抗干扰性[J].中国科学(A辑数学物理学天文学技术科学),1982,(10):941-950.
[11]朱斌.自抗扰控制入门[M].北京:北京航空航天大学出版社,2017.
[12]秦黎博.四旋翼无人机自抗扰控制系统设计[D].大连:大连理工大学,2017.
[13]蔡敏.六旋翼飞行器轨迹跟踪的控制方法研究[D].兰州:兰州交通大学,2017.
[14]养成顺,扬忠,徐德智,葛乐.新型六旋翼飞行器的轨迹跟踪控制[J].系统工程与电子技术,2012,34(10):2098-2105.
[15]AMIR M Y,ABBASS V.Modeling of Quadrotor Helicopter Dynamics[C]//Smart Manufacturing Applica tion,2008.ICSMA 2008.International Conference on IEEE,2008:100-105.
[16]ERGINER B,ALTUG E.Modeling and PD Con trol of a Quadrotor VTOL Vehicle[C]//Intelligent Vehicles Symposium,2007 IEEE.IEEE,2007:894-899.
[17]MIAN A A,DAOBO W.Modeling and BacksteppingBased Nonlinear Control Strategy for a 6 DOF Quadrotor Helicopter[J].Chinese Journal of Aeronautics,2008,21(3):261-268.
[18]MINH L D,HA C.Modeling and Control of Quadrotor MAV Using Vision-Based Measurement[C]//Str ategic Technology(IFOST),2010 International Forum on IEEE,2010:70-75.
[19]TAYEBI A,MCGILVRAY S.Attitude Stabilization of a Four-Rotor Aerial Robot[C]//IEEE Conference on Decision and Control,2004,(12):14-17.
[2]CHEN W,YANG J,GUO L,et al.DisturbanceObserver-Based Control and Related Methods-An Ove rview[J].Industrial Electronics,IEEE Transactions on IEEEJournals&Magazines,2016,63(2):1083-1095.
[3]韩京清.自抗扰控制技术:估计补偿不确定因素的控制技术[M].北京:国防工业出版社,2008.
[4]高志强.浅谈工程控制的信息问题[J].系统科学与数学,2016,36(7):908-923.
[5]韩京清,王伟.非线性跟踪─微分器[J].系统科学与数学,1994,(2):177-183.
[6]韩京清,袁露林.跟踪-微分器的离散形式[J].系统科学与数学,1999,(3):268-273.
[7]韩京清.一类不确定对象的扩张状态观测器[J].控制与决策,1995,(1):85-88.
[8]韩京清.非线性系统的状态观测器[J].控制与决策,1990,(3):57-60.
[9]韩京清.非线性状态误差反馈控制律─NLSEF[J].控制与决策,1995,(3):221-225,231.
[10]韩京清,许可康.用状态反馈实现稳定的抗干扰性[J].中国科学(A辑数学物理学天文学技术科学),1982,(10):941-950.
[11]朱斌.自抗扰控制入门[M].北京:北京航空航天大学出版社,2017.
[12]秦黎博.四旋翼无人机自抗扰控制系统设计[D].大连:大连理工大学,2017.
[13]蔡敏.六旋翼飞行器轨迹跟踪的控制方法研究[D].兰州:兰州交通大学,2017.
[14]养成顺,扬忠,徐德智,葛乐.新型六旋翼飞行器的轨迹跟踪控制[J].系统工程与电子技术,2012,34(10):2098-2105.
[15]AMIR M Y,ABBASS V.Modeling of Quadrotor Helicopter Dynamics[C]//Smart Manufacturing Applica tion,2008.ICSMA 2008.International Conference on IEEE,2008:100-105.
[16]ERGINER B,ALTUG E.Modeling and PD Con trol of a Quadrotor VTOL Vehicle[C]//Intelligent Vehicles Symposium,2007 IEEE.IEEE,2007:894-899.
[17]MIAN A A,DAOBO W.Modeling and BacksteppingBased Nonlinear Control Strategy for a 6 DOF Quadrotor Helicopter[J].Chinese Journal of Aeronautics,2008,21(3):261-268.
[18]MINH L D,HA C.Modeling and Control of Quadrotor MAV Using Vision-Based Measurement[C]//Str ategic Technology(IFOST),2010 International Forum on IEEE,2010:70-75.
[19]TAYEBI A,MCGILVRAY S.Attitude Stabilization of a Four-Rotor Aerial Robot[C]//IEEE Conference on Decision and Control,2004,(12):14-17.