四旋翼无人机姿态角控制系统设计
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摘要
提出了一种基于遗传优化的自抗扰非奇异快速终端滑模控制算法,解决四旋翼的姿态角控制系统问题。运用滑模控制的思想构造滑模面,引入终端滑模策略,将滑模面改造成非线性结构,利用指数函数的特点使得终端滑模非奇异,并且更快收敛。通过运用自抗扰控制思想加强系统对于内外扰动的抗干扰稳定性。利用遗传算法,进化控制器的参数至最佳控制指标组合的值。仿真和实验结果证明提出的控制系统正确可行,既具备滑模控制的鲁棒性又吸取了自抗扰控制对于内外部扰动稳定的优点,同时能够对控制器的参数进行迭代进化。
This article designs the active-disturbance-rejection nonsingular fast terminal slidingmode controller based on genetic algorithm,which has the advantages of both sliding-mode controller's robustness and the ADRC's stability for internal and external disturbance. According to the quadrotor's characteristic of strong robustness,the sliding-mode surface is constructed by considering the idea of sliding-mode control,and is introduced into the nonlinear structure,which has faster convergence.Through using ADRC idea the stability for internal and external disturbance is strengthened. The controller's parameter can be optimized iteratively by the genetic algorithm. The quadrotor's control characteristics of attitude angle and height are simulated and the dynamic and static performance proves the feasibility accordingly,which verifies the effectiveness of the designed controller.
This article designs the active-disturbance-rejection nonsingular fast terminal slidingmode controller based on genetic algorithm,which has the advantages of both sliding-mode controller's robustness and the ADRC's stability for internal and external disturbance. According to the quadrotor's characteristic of strong robustness,the sliding-mode surface is constructed by considering the idea of sliding-mode control,and is introduced into the nonlinear structure,which has faster convergence.Through using ADRC idea the stability for internal and external disturbance is strengthened. The controller's parameter can be optimized iteratively by the genetic algorithm. The quadrotor's control characteristics of attitude angle and height are simulated and the dynamic and static performance proves the feasibility accordingly,which verifies the effectiveness of the designed controller.
引文
[1]Ortiz J P,Minchala L I,Reinoso M J.Nonlinear robust H-Infinity PID controller for the multivariable system quadrotor[J].IEEE Latin America Transactions,2016,14(3):1176-1183.
[2]Das A,Lewis F,Subbarao K.Backstepping approach for controlling a quadrotor using lagrange form dynamics[J].Journal of Intelligent&Robotic Systems,2009,56(1-2):127-151.
[3]Yacef F,Bouhali O,Hamerlain M,et al.Observer-based adaptive fuzzy backstepping tracking control of quadrotor unmanned aerial vehicle powered by li-ion battery[J].Journal of Intelligent&Robotic Systems,2016,84(1-4):179-197.
[4]Chiou J S,Tran H K,Shieh M Y,et al.Particle swarm optimization algorithm reinforced fuzzy proportional-integral-derivative for a quadrotor attitude control[J].Advances in Mechanical Engineering,2016,8(9):113-119.
[5]Zhang C,Hu H,Wang J.An adaptive neural network approach to the tracking control of micro aerial vehicles in constrained space[J].International Journal of Systems Science,2016,48(1):84-94.
[6]朱培.四旋翼空中机器人自适应滑模控制研究[D].北京:北方工业大学,2015.
[7]Ansari U,Bajodah A H.Quadrotor control using generalized dynamic inversion and terminal sliding mode[J].2015 3rd International Conference on Control,Engineering&Information Technology(CEIT),2015,5(4):6-11.
[8]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.
[9]和红磊,王玉龙.基于滑模自抗扰的半潜式海洋平台动力定位控制方法研究[J].船舶工程,2016,6(11):72-77.
[10]赵志良.非线性自抗扰控制的收敛性[D].合肥:中国科学技术大学,2012.
[11]薛鹏,任鹏飞,曹学儒.四旋翼飞行器滑模控制的稳定性分析[J].河南工程学院学报(自然科学版),2015,3(01):33-43.
[12]林旭梅,王婵.四旋翼飞行器的自适应鲁棒滑模控制器设计[J].仪器仪表学报,2015,8(7):1522-1528.
[2]Das A,Lewis F,Subbarao K.Backstepping approach for controlling a quadrotor using lagrange form dynamics[J].Journal of Intelligent&Robotic Systems,2009,56(1-2):127-151.
[3]Yacef F,Bouhali O,Hamerlain M,et al.Observer-based adaptive fuzzy backstepping tracking control of quadrotor unmanned aerial vehicle powered by li-ion battery[J].Journal of Intelligent&Robotic Systems,2016,84(1-4):179-197.
[4]Chiou J S,Tran H K,Shieh M Y,et al.Particle swarm optimization algorithm reinforced fuzzy proportional-integral-derivative for a quadrotor attitude control[J].Advances in Mechanical Engineering,2016,8(9):113-119.
[5]Zhang C,Hu H,Wang J.An adaptive neural network approach to the tracking control of micro aerial vehicles in constrained space[J].International Journal of Systems Science,2016,48(1):84-94.
[6]朱培.四旋翼空中机器人自适应滑模控制研究[D].北京:北方工业大学,2015.
[7]Ansari U,Bajodah A H.Quadrotor control using generalized dynamic inversion and terminal sliding mode[J].2015 3rd International Conference on Control,Engineering&Information Technology(CEIT),2015,5(4):6-11.
[8]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.
[9]和红磊,王玉龙.基于滑模自抗扰的半潜式海洋平台动力定位控制方法研究[J].船舶工程,2016,6(11):72-77.
[10]赵志良.非线性自抗扰控制的收敛性[D].合肥:中国科学技术大学,2012.
[11]薛鹏,任鹏飞,曹学儒.四旋翼飞行器滑模控制的稳定性分析[J].河南工程学院学报(自然科学版),2015,3(01):33-43.
[12]林旭梅,王婵.四旋翼飞行器的自适应鲁棒滑模控制器设计[J].仪器仪表学报,2015,8(7):1522-1528.