四旋翼无人机自适应反步滑模姿态控制器设计
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摘要
针对四旋翼无人机姿态控制问题,提出一种基于反步滑模的自适应控制算法。首先,基于牛顿动力学方程建立系统的非线性模型;其次,根据Lyapunov稳定性判据推出无人机反步滑模控制律;然后,设计了一种扰动观测器,对不可测扰动进行实时估计,提高姿态控制系统的抗扰能力;最后,引入一种积分自适应控制律,克服模型参数摄动和外部扰动等因素的影响,增强控制系统的鲁棒性。仿真结果表明,引入了积分自适应律和扰动观测器的反步滑模姿态控制方法比传统的PID控制以及反步滑模法具有更好的鲁棒性。
This paper aims to design an adaptive control algorithm based on backstepping sliding mode algorithm to solve the attitude control problem of a quadrotor UAV. Firstly, a nonlinear model of the system is established based on Newton Euler formalism. Secondly, a backstepping sliding mode control law of the UAV is designed based on the Lyapunov stability criterion, then a disturbance observer is designed to estimate the unmeasured disturbance in real-time. Finally, to overcome the influence of model parameter perturbation and external disturbance, an integral adaptive control law is introduced to enhance the robustness of the control system. Simulation results show that the attitude control method with integral adaptive law and disturbance observer is more robust than the traditional PID control and backstepping sliding mode method.
This paper aims to design an adaptive control algorithm based on backstepping sliding mode algorithm to solve the attitude control problem of a quadrotor UAV. Firstly, a nonlinear model of the system is established based on Newton Euler formalism. Secondly, a backstepping sliding mode control law of the UAV is designed based on the Lyapunov stability criterion, then a disturbance observer is designed to estimate the unmeasured disturbance in real-time. Finally, to overcome the influence of model parameter perturbation and external disturbance, an integral adaptive control law is introduced to enhance the robustness of the control system. Simulation results show that the attitude control method with integral adaptive law and disturbance observer is more robust than the traditional PID control and backstepping sliding mode method.
引文
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[4] BOUABALLAH S, SIEGWART R. Backstepping and sliding-mode techniques applied to an indoor micro quadrotor[C]//Robotics and Automation. Proceedings of the 2005 IEEE International Conference on. IEEE, 2006: 2247-2252.
[5] CHINGOZHA T, NYANDORO O. Adaptive sliding backstepping control of quadrotor UAV attitude[J]. IFAC Proceedings Volumes, 2014, 47(3): 11043-11048.
[6] TIAN B L, YE Z Y, ZHANG Z Q. Free formation flight of UAV using compensatory fuzzy neural network control[J]. Journal of Civil Aviation University of China, 2005, 6: 10-13.
[7] ZHAO H, WENG X W, HUANG C Q, et al. Application of fuzzy neural network in the attitude control of the UAV[J]. Journal of System Simulation, 2008:431-436.
[8] WU J, PENG H, CHEN Q, et al. Modeling and control approach to a distinctive quadrotor helicopter[J]. ISA Transactions, 2014, 53(1): 173-185.
[9] BANGURA M, MAHONY R. Real-time model predictive control for quadrotors[J]. IFAC Proceedings Volumes, 2014, 47(3): 11773-11780.
[10] KAWAMURA A, ITOH H, SAKAMOTO K. Chattering reduction of disturbance observer based sliding mode control[J]. IEEE Transactions on Industry Applications, 1994, 30(2):456-461
[11] KOSHKOUEI A J, MILLS R E, ZINOBER A S I. Adaptive backstepping control[J]. Lecture Notes in Control & Information Sciences, 2002, 274:129-153.
[12] 刘金琨.滑模变结构控制MATLAB仿真[M]. 北京:清华大学出版社, 2015: 236-250.
[2] RAJESH R J, ANANDA C M. PSO tuned PID controller for controlling camera position in UAV using 2-axis gimbal[C]//Power and Advanced Control Engineering, 2015 International Conference on. IEEE, 2015: 128-133.
[3] FERREIRA H C, BAPTISTA R S, ISHIHARA J Y, et al. Disturbance rejection in a fixed wing UAV using nonlinear H∞ state feedback[C]// IEEE International Conference on Control and Automation. IEEE, 2012:386-391.
[4] BOUABALLAH S, SIEGWART R. Backstepping and sliding-mode techniques applied to an indoor micro quadrotor[C]//Robotics and Automation. Proceedings of the 2005 IEEE International Conference on. IEEE, 2006: 2247-2252.
[5] CHINGOZHA T, NYANDORO O. Adaptive sliding backstepping control of quadrotor UAV attitude[J]. IFAC Proceedings Volumes, 2014, 47(3): 11043-11048.
[6] TIAN B L, YE Z Y, ZHANG Z Q. Free formation flight of UAV using compensatory fuzzy neural network control[J]. Journal of Civil Aviation University of China, 2005, 6: 10-13.
[7] ZHAO H, WENG X W, HUANG C Q, et al. Application of fuzzy neural network in the attitude control of the UAV[J]. Journal of System Simulation, 2008:431-436.
[8] WU J, PENG H, CHEN Q, et al. Modeling and control approach to a distinctive quadrotor helicopter[J]. ISA Transactions, 2014, 53(1): 173-185.
[9] BANGURA M, MAHONY R. Real-time model predictive control for quadrotors[J]. IFAC Proceedings Volumes, 2014, 47(3): 11773-11780.
[10] KAWAMURA A, ITOH H, SAKAMOTO K. Chattering reduction of disturbance observer based sliding mode control[J]. IEEE Transactions on Industry Applications, 1994, 30(2):456-461
[11] KOSHKOUEI A J, MILLS R E, ZINOBER A S I. Adaptive backstepping control[J]. Lecture Notes in Control & Information Sciences, 2002, 274:129-153.
[12] 刘金琨.滑模变结构控制MATLAB仿真[M]. 北京:清华大学出版社, 2015: 236-250.