基于改进粒子群算法的四旋翼自抗扰控制器优化设计
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摘要
针对四旋翼飞行器自抗扰控制器参数较多、人工整定困难且难以得到最优控制效果的问题,提出一种基于改进粒子群算法的四旋翼自抗扰控制器优化方法。在设计了四旋翼飞行器的自抗扰控制器之后,将自抗扰控制器的参数作为粒子群中的粒子进行迭代寻优,同时在传统的粒子群算法基础上参考遗传算法,对适应值不好的粒子进行交叉保优,以提高粒子的多样性,加快寻优速度。仿真结果表明,对比人工整定参数的控制器,优化后的控制器超调更小、调节时间更快。该方法能够解决四旋翼飞行器自抗扰控制器人工参数整定困难的问题,且优化后的控制器具有更好的控制效果。
In order to solve the problem that the parameters of quad-rotor aircraft ADRC controller are difficult to set,and it is hard to get the optimal control effect due to manual parameter tuning,this paper proposed a design method of adaptive disturbance rejection controller based on improved particle swarm optimization for quad-rotor. After designed the ADRC controller of quad-rotor,this method iteratively optimized the parameters of the controller as particles in the particle swarm. At the same time,based on the traditional particle swarm optimization and referring to genetic algorithm,it cross-guaranteed the particles with poor fitness value to improve the diversity of particles,and to speed up the search speed. The simulation results show that compare with the controller with manual tuning parameters,the optimized controller has less overshoot and faster adjusting time. This method can solve the difficulty of manual parameter setting of quad-rotor aircraft ADRC controller,and the optimized controller has better control performance.
In order to solve the problem that the parameters of quad-rotor aircraft ADRC controller are difficult to set,and it is hard to get the optimal control effect due to manual parameter tuning,this paper proposed a design method of adaptive disturbance rejection controller based on improved particle swarm optimization for quad-rotor. After designed the ADRC controller of quad-rotor,this method iteratively optimized the parameters of the controller as particles in the particle swarm. At the same time,based on the traditional particle swarm optimization and referring to genetic algorithm,it cross-guaranteed the particles with poor fitness value to improve the diversity of particles,and to speed up the search speed. The simulation results show that compare with the controller with manual tuning parameters,the optimized controller has less overshoot and faster adjusting time. This method can solve the difficulty of manual parameter setting of quad-rotor aircraft ADRC controller,and the optimized controller has better control performance.
引文
[1] Gonzalez-Vazquez S,Moreno-Valenzuela J. A new nonlinear PI/PID controller for quadrotor posture regulation[C]//Proc of IEEE Electronics,Robotics and Automotive Mechanics Conference. Piscataway,NJ:IEEE Press,2010:642-647.
[2] Alothman Y,Jasim W,Gu Dongbing. Quad-rotor lifting-transporting cable-suspended payloads control[C]//Proc of the 21st International Conference on Automation and Computing. Piscataway,NJ:IEEE Press,2015:1-6.
[3] Yang Ning,Wu Liaoni,Lin Qi. Adaptive flight control law based on neural networks and dynamic inversion for unmanned aerial vehicle[C]//Proc of International Conference on Automatic Control and Artificial Intelligence. Piscataway,NJ:IEEE Press,2012:355-358.
[4] Raffo G V,Ortega M G,Rubio F R. Robust nonlinear control for path tracking of a quad-rotor helicopter[J]. Asian Journal of Control,2015,17(1):142-156.
[5] Tan Lining,Lu Libin,Jin Guodong. Attitude stabilization control of a quadrotor helicopter using integral backstepping[C]//Proc of International Conference on Automatic Control and Artificial Intelligence.Piscataway,NJ:IEEE Press,2012:573-577.
[6] De Monte P,Lohmann B. Trajectory tracking control for a quadrotor helicopter based on backstepping using a decoupling quaternion parametrization[C]//Proc of the 21st Mediterranean Conference on Control and Automation. Piscataway,NJ:IEEE Press,2013:507-512.
[7]刘一莎,杨晟萱,王伟.四旋翼飞行器的自抗扰飞行控制方法[J].控制理论与应用,2015,32(10):1351-1360.(Liu Yisha,Yang Shengxuan,Wang Wei. An active disturbance-rejection flight control method for quad-rotor unmanned aerial vehicles[J]. Control Theory&Applications,2015,32(10):1351-1360.)
[8]杨立本,章卫国,黄得刚.基于ADRC姿态解耦的四旋翼飞行器鲁棒轨迹跟踪[J].北京航空航天大学学报,2015,41(6):1026-1033.(Yang Liben,Zhang Weiguo,Huang Degang. Robust trajectory tracking for quadrotor aircraft based on ADRC attitude decoupling control[J]. Journal of Beijing University of Aeronautics and Astronautics,2015,41(6):1026-1033.)
[9]叶孝璐,俞立,张文安,等.基于串级ADRC的四旋翼飞行器悬停控制[J].中南大学学报:自然科学版,2017,48(8):2079-2087.(Ye Xiaolu,Yu Li,Zhang Wen’an,et al. Cascade ADRC-based hover control for quadrotor air vehicles[J]. Journal of Central South University:Science and Technology,2017,48(8):2079-2087.)
[10]韩京清.从PID技术到“自抗扰控制”技术[J].控制工程,2002,9(3):13-18.(Han Jingqing. From PID technique to active disturbances rejection control technique[J]. Control Engineering of China,2002,9(3):13-18.)
[11]张立明.自抗扰控制技术在AUV航向控制中的应用[D].哈尔滨:哈尔滨工程大学,2009.(Zhang Liming. Research on application of active disturbance rejection technology to AUV heading control[D]. Harbin:Harbin Engineering University,2009.)
[12]Kennedy J,Eberhart R. Particle swarm optimization[C]//Proc of IEEE International Conference on Neural Networks. 1995:1942-1948.
[13]Eberhart R,Kennedy J. A new optimizer using particle swarm theory[C]//Proc of the 6th International Symposium on Micro Machine and Human Science. Piscataway,NJ:IEEE Press,1995:39-43.
[14]张万绪,张向兰,李莹.基于改进粒子群算法的智能机器人路径规划[J].计算机应用,2014,34(2):510-513.(Zhang Wanxu,Zhang Xianglan,Li Ying. Path planning for intelligent robots based on improved particle swarm optimization algorithm[J]. Journal of Computer Applications,2014,34(2):510-513.)
[15]田佳,胡威,李琳,等.基于粒子群优化算法的多核处理器任务调度研究[J].计算机应用研究,2017,34(12):3698-3700.(Tian Jia,Hu Wei,Li Lin,et al. Research on multi-core processor task scheduling based on particle swarm optimization algorithm[J]. Application Research of Computers,2017,34(12):3698-3700.)
[16] Shi Yuhui,Eberhart R. A modified particle swarm optimizer[C]//Proc of IEEE International Conference on Evolutionary Computation.Piscataway,NJ:IEEE Press,1998:69-73.
[2] Alothman Y,Jasim W,Gu Dongbing. Quad-rotor lifting-transporting cable-suspended payloads control[C]//Proc of the 21st International Conference on Automation and Computing. Piscataway,NJ:IEEE Press,2015:1-6.
[3] Yang Ning,Wu Liaoni,Lin Qi. Adaptive flight control law based on neural networks and dynamic inversion for unmanned aerial vehicle[C]//Proc of International Conference on Automatic Control and Artificial Intelligence. Piscataway,NJ:IEEE Press,2012:355-358.
[4] Raffo G V,Ortega M G,Rubio F R. Robust nonlinear control for path tracking of a quad-rotor helicopter[J]. Asian Journal of Control,2015,17(1):142-156.
[5] Tan Lining,Lu Libin,Jin Guodong. Attitude stabilization control of a quadrotor helicopter using integral backstepping[C]//Proc of International Conference on Automatic Control and Artificial Intelligence.Piscataway,NJ:IEEE Press,2012:573-577.
[6] De Monte P,Lohmann B. Trajectory tracking control for a quadrotor helicopter based on backstepping using a decoupling quaternion parametrization[C]//Proc of the 21st Mediterranean Conference on Control and Automation. Piscataway,NJ:IEEE Press,2013:507-512.
[7]刘一莎,杨晟萱,王伟.四旋翼飞行器的自抗扰飞行控制方法[J].控制理论与应用,2015,32(10):1351-1360.(Liu Yisha,Yang Shengxuan,Wang Wei. An active disturbance-rejection flight control method for quad-rotor unmanned aerial vehicles[J]. Control Theory&Applications,2015,32(10):1351-1360.)
[8]杨立本,章卫国,黄得刚.基于ADRC姿态解耦的四旋翼飞行器鲁棒轨迹跟踪[J].北京航空航天大学学报,2015,41(6):1026-1033.(Yang Liben,Zhang Weiguo,Huang Degang. Robust trajectory tracking for quadrotor aircraft based on ADRC attitude decoupling control[J]. Journal of Beijing University of Aeronautics and Astronautics,2015,41(6):1026-1033.)
[9]叶孝璐,俞立,张文安,等.基于串级ADRC的四旋翼飞行器悬停控制[J].中南大学学报:自然科学版,2017,48(8):2079-2087.(Ye Xiaolu,Yu Li,Zhang Wen’an,et al. Cascade ADRC-based hover control for quadrotor air vehicles[J]. Journal of Central South University:Science and Technology,2017,48(8):2079-2087.)
[10]韩京清.从PID技术到“自抗扰控制”技术[J].控制工程,2002,9(3):13-18.(Han Jingqing. From PID technique to active disturbances rejection control technique[J]. Control Engineering of China,2002,9(3):13-18.)
[11]张立明.自抗扰控制技术在AUV航向控制中的应用[D].哈尔滨:哈尔滨工程大学,2009.(Zhang Liming. Research on application of active disturbance rejection technology to AUV heading control[D]. Harbin:Harbin Engineering University,2009.)
[12]Kennedy J,Eberhart R. Particle swarm optimization[C]//Proc of IEEE International Conference on Neural Networks. 1995:1942-1948.
[13]Eberhart R,Kennedy J. A new optimizer using particle swarm theory[C]//Proc of the 6th International Symposium on Micro Machine and Human Science. Piscataway,NJ:IEEE Press,1995:39-43.
[14]张万绪,张向兰,李莹.基于改进粒子群算法的智能机器人路径规划[J].计算机应用,2014,34(2):510-513.(Zhang Wanxu,Zhang Xianglan,Li Ying. Path planning for intelligent robots based on improved particle swarm optimization algorithm[J]. Journal of Computer Applications,2014,34(2):510-513.)
[15]田佳,胡威,李琳,等.基于粒子群优化算法的多核处理器任务调度研究[J].计算机应用研究,2017,34(12):3698-3700.(Tian Jia,Hu Wei,Li Lin,et al. Research on multi-core processor task scheduling based on particle swarm optimization algorithm[J]. Application Research of Computers,2017,34(12):3698-3700.)
[16] Shi Yuhui,Eberhart R. A modified particle swarm optimizer[C]//Proc of IEEE International Conference on Evolutionary Computation.Piscataway,NJ:IEEE Press,1998:69-73.