融合蜂群优化航空发动机自适应PID控制
|
摘要
针对传统人工蜂群算法收敛速度慢、容易陷入局部最优等缺点,提出了一种改进的融合蜂群(HABC)算法,从选择机制、邻域搜索机制和解向量的多样性3个方面改进了蜂群算法的寻优性能。针对航空发动机控制系统,基于HABC算法设计了一种在线自适应PID控制器,在控制过程中不断优化PID参数,使控制器能够根据发动机系统的当前工作状态自适应地得到时变最优参数。仿真结果表明,所设计的在线自适应PID控制器实现了参数时变最优,使航空发动机闭环系统具有满意的动态性能和鲁棒稳定性。
In view of the fact that the traditional artificial bee colony(ABC) algorithm has a slow convergence speed and traps into local optima easily, a new hybrid artificial bee colony(HABC) algorithm is proposed,which improves the optimization performance in three aspects: selection mechanism, neighborhood search mechanism and diversity of solution vectors. Aimed at the aeroengine control system, an online adaptive PID controller of optimum is designed based on this algorithm. In the control process, the parameters of PID controller are optimized constantly by HABC algorithm so that the aeroengine controller can adaptively obtain the time-varying optimal parameters according to the current system working status. Simulation results show that the aeroengine online adaptive PID controller implements the time-varying optimum of PID controller parameters, which insures that the closed loop system has good dynamic performance as well as strong robustness.
In view of the fact that the traditional artificial bee colony(ABC) algorithm has a slow convergence speed and traps into local optima easily, a new hybrid artificial bee colony(HABC) algorithm is proposed,which improves the optimization performance in three aspects: selection mechanism, neighborhood search mechanism and diversity of solution vectors. Aimed at the aeroengine control system, an online adaptive PID controller of optimum is designed based on this algorithm. In the control process, the parameters of PID controller are optimized constantly by HABC algorithm so that the aeroengine controller can adaptively obtain the time-varying optimal parameters according to the current system working status. Simulation results show that the aeroengine online adaptive PID controller implements the time-varying optimum of PID controller parameters, which insures that the closed loop system has good dynamic performance as well as strong robustness.
引文
[1]孙健国.面向21世纪航空动力控制展望[J].航空动力学报,2001,16(2):97-102.Sun J G.Outlook for aeronautical power control in twenty-first Century[J].Journal of Aerospace Power,2001,16(2):97-102.
[2]Tomizuka M,Horowitz R.Model reference adaptive control of mechanical manipulators[C]//Proc.IFAC Symp.Adaptive Systems in Control and Signal Proc.2014:27-32.
[3]肖玲斐,黄向华.涡轴发动机自适应非线性预测控制[J].航空动力学报,2012,27(5):1194-1200.Xiao L F,Huang X H.Adaptive Nonlinear Predictive Control for Turboshaft Engines[J].Journal of Aerospace Power,2012,27(5):1194-1200.
[4]苗单广,谢寿生,张波,等.航空发动机自适应全局快速非奇异Terminal滑模控制[J].航空动力学报,2013,28(11):2634-2640.Miao D G,Xie S S,Zhang B,et al.Aeroengine adaptive global fast non-singular Terminal sliding mode control[J].Journal of Aerospace Power,2013,28(11):2634-2640.
[5]李玥,孙健国.基于遗传算法的航空发动机多目标优化PID控制[J].航空动力学报,2008,23(1):174-178.Li Y,Sun J G.Multi-objective optimal PID control of Aeroengine Based on genetic algorithm[J].Journal of Aerospace Power,2008,23(1):174-178.
[6]李秋红,孙健国,周继超.航空发动机PID控制参数优化的改进遗传算法[J].南京航空航天大学学报,2006,38(2):162-165.Li Q H,Sun J G,Zhou J C.Improved genetic algorithm for optimization of aeroengine PID control parameters[J].Journal of Nanjing University of Aeronautics and Astronautics,2006,38(2):162-165.
[7]Li S,Sun J.Application of genetic algorithm to solving nonlinear model of aeroengines[J].Chinese Journal of Aeronautics,2003,16(2):69-72.
[8]曹志松,朴英.基于混合遗传算法的航空发动机PID控制参数寻优[J].航空动力学报,2007,22(9):1588-1592.Cao Z S,Pu Y.Aeroengine PID control parameter optimization based on hybrid genetic algorithm[J].Journal of Aerospace Power,2007,22(9):1588-1592.
[9]Karaboga D,Gorkemli B,Ozturk C,et al.A comprehensive survey:artificial bee colony(ABC)algorithm and applications[J].Artificial Intelligence Review,2014,42(1):1-37.
[10]Karaboga D,Kaya E.Training ANFIS using artificial bee colony algorithm[C]//Innovations in Intelligent Systems and Applications(INISTA),2013 IEEE International Symposium on.IEEE,2013:1-5.
[11]Zhong S,Dong Y F,Sarosh A.Artificial Bee Colony Algorithm for Parametric Optimization of Spacecraft Attitude Tracking Controller[M]//Foundations and Practical Applications of Cognitive Systems and Information Processing.Springer Berlin Heidelberg,2014:501-510.
[12]胡中华,赵敏.基于人工蜂群算法的无人机航迹规划研究[J].传感器与微系统,2010,29(3):35-38.Hu Z H,Zhao M.Research on UAV trajectory planning based on artificial bee colony algorithm[J].Sensors and Microsystems,2010,29(3):35-38.
[13]张禹,白晓兰,武芃睿.基于ABC算法的航空发动机管路自动优化布局[J].东北大学学报(自然科学版),2013,34(10):1461-1464.Zhang Y,Bai X L,Wu Q R.Automatic optimization of aeroengine pipeline layout based on ABC algorithm[J].Journal of Northeast University(Natural Science Edition),2013,34(10):1461-1464.
[14]王海泉,胡瀛月,廖伍代,等.基于改进人工蜂群算法的机器人路径规划[J].控制工程,2016,23(9):1407-1411.Wang H Q,Hu Y Y,Liao W D,et al.Robot path planning based on improved artificial bee colony algorithm[J].Control Engineering of China,2016,23(9):1407-1411.
[15]罗钧,樊鹏程.基于遗传交叉因子的改进蜂群优化算法倡[J].计算机应用研究,2009,26(10):3716-3717.Luo J,Fan P C.An improved bee colony optimization algorithm based on genetic crossover factor is advocated[J].Computer Applied Research,2009,26(10):3716-3717.
[16]袁艳花.遗传蜂群算法及其应用[D].南京理工大学,2013.Yuan Y H.Genetic bee colony algorithm and its application[D].Nanjing University of Science and Technology,2013.
[2]Tomizuka M,Horowitz R.Model reference adaptive control of mechanical manipulators[C]//Proc.IFAC Symp.Adaptive Systems in Control and Signal Proc.2014:27-32.
[3]肖玲斐,黄向华.涡轴发动机自适应非线性预测控制[J].航空动力学报,2012,27(5):1194-1200.Xiao L F,Huang X H.Adaptive Nonlinear Predictive Control for Turboshaft Engines[J].Journal of Aerospace Power,2012,27(5):1194-1200.
[4]苗单广,谢寿生,张波,等.航空发动机自适应全局快速非奇异Terminal滑模控制[J].航空动力学报,2013,28(11):2634-2640.Miao D G,Xie S S,Zhang B,et al.Aeroengine adaptive global fast non-singular Terminal sliding mode control[J].Journal of Aerospace Power,2013,28(11):2634-2640.
[5]李玥,孙健国.基于遗传算法的航空发动机多目标优化PID控制[J].航空动力学报,2008,23(1):174-178.Li Y,Sun J G.Multi-objective optimal PID control of Aeroengine Based on genetic algorithm[J].Journal of Aerospace Power,2008,23(1):174-178.
[6]李秋红,孙健国,周继超.航空发动机PID控制参数优化的改进遗传算法[J].南京航空航天大学学报,2006,38(2):162-165.Li Q H,Sun J G,Zhou J C.Improved genetic algorithm for optimization of aeroengine PID control parameters[J].Journal of Nanjing University of Aeronautics and Astronautics,2006,38(2):162-165.
[7]Li S,Sun J.Application of genetic algorithm to solving nonlinear model of aeroengines[J].Chinese Journal of Aeronautics,2003,16(2):69-72.
[8]曹志松,朴英.基于混合遗传算法的航空发动机PID控制参数寻优[J].航空动力学报,2007,22(9):1588-1592.Cao Z S,Pu Y.Aeroengine PID control parameter optimization based on hybrid genetic algorithm[J].Journal of Aerospace Power,2007,22(9):1588-1592.
[9]Karaboga D,Gorkemli B,Ozturk C,et al.A comprehensive survey:artificial bee colony(ABC)algorithm and applications[J].Artificial Intelligence Review,2014,42(1):1-37.
[10]Karaboga D,Kaya E.Training ANFIS using artificial bee colony algorithm[C]//Innovations in Intelligent Systems and Applications(INISTA),2013 IEEE International Symposium on.IEEE,2013:1-5.
[11]Zhong S,Dong Y F,Sarosh A.Artificial Bee Colony Algorithm for Parametric Optimization of Spacecraft Attitude Tracking Controller[M]//Foundations and Practical Applications of Cognitive Systems and Information Processing.Springer Berlin Heidelberg,2014:501-510.
[12]胡中华,赵敏.基于人工蜂群算法的无人机航迹规划研究[J].传感器与微系统,2010,29(3):35-38.Hu Z H,Zhao M.Research on UAV trajectory planning based on artificial bee colony algorithm[J].Sensors and Microsystems,2010,29(3):35-38.
[13]张禹,白晓兰,武芃睿.基于ABC算法的航空发动机管路自动优化布局[J].东北大学学报(自然科学版),2013,34(10):1461-1464.Zhang Y,Bai X L,Wu Q R.Automatic optimization of aeroengine pipeline layout based on ABC algorithm[J].Journal of Northeast University(Natural Science Edition),2013,34(10):1461-1464.
[14]王海泉,胡瀛月,廖伍代,等.基于改进人工蜂群算法的机器人路径规划[J].控制工程,2016,23(9):1407-1411.Wang H Q,Hu Y Y,Liao W D,et al.Robot path planning based on improved artificial bee colony algorithm[J].Control Engineering of China,2016,23(9):1407-1411.
[15]罗钧,樊鹏程.基于遗传交叉因子的改进蜂群优化算法倡[J].计算机应用研究,2009,26(10):3716-3717.Luo J,Fan P C.An improved bee colony optimization algorithm based on genetic crossover factor is advocated[J].Computer Applied Research,2009,26(10):3716-3717.
[16]袁艳花.遗传蜂群算法及其应用[D].南京理工大学,2013.Yuan Y H.Genetic bee colony algorithm and its application[D].Nanjing University of Science and Technology,2013.