Dynamic control strategy of extended-range APU based on fuzzy PID optimized by CAHPSO
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摘要: 针对增程式辅助动力单元(APU)工作点切换过程的控制,提出了一种混沌退火混合粒子群(CAHPSO)算法优化模糊比例积分微分(PID)控制的增程式APU动态控制策略。该算法将标准粒子群(PSO)算法与混沌搜索和退火机制融合,强化全局寻优能力,并采用该算法离线优化模糊PID控制参数。为验证该控制策略的有效性,本文建立了APU仿真模型。仿真结果表明:该控制策略可使APU在工作点从热机点逐步切换至高负荷点的过程中稳定时间短,在三个工作点切换控制过程中稳定时间分别为2.92 s,2.88 s,2.79 s;可使APU转速超调率小,在小负荷点向中负荷点切换时超调约0.95%,在其余过程未出现超调;可使APU转矩变化平缓,在中负荷点向高负荷点切换时转矩仅超调0.16 N·m,具有良好的动态控制效果。Abstract: For the control of the working point switching process of the extended-range auxiliary power unit (APU), a dynamic control strategy for the extended-range APU was proposed by using the chaotic annealing hybrid particle swarm optimization (CAHPSO) algorithm to optimize the fuzzy proportional-integral-derivative (PID) control. This algorithm was used to combine chaos search and annealing mechanisms based on the standard particle swarm optimization (PSO) to enhance the global optimization ability, and optimize the fuzzy PID control parameters offline. In order to verify the effectiveness of the new control strategy, the APU system simulation model was established. The simulation results showed that during the processes of gradually switching from the warming-up point to the high load point, the new control strategy can make the APU shorten the stabilization time, the stabilization time for the three switching control processes of the working points was 2.92 s, 2.88 s, 2.79 s, respectively; the new control strategy can make the APU reduce the speed overshoot rate, only when in the switching from the small load point to the middle load point, the speed overshoot rate was about 0.95% and there was no overshoot in other switching processes; the new control strategy can make the APU torque change smoothly, and the torque overshoot was only 0.16 N·m when switching from the middle load point to the high load point, which achieved a good dynamic control effect.
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Key words:
- extended-range APU /
- dynamic control /
- fuzzy PID /
- CAHPSO /
- working point switching
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[1] LI J,ZHOU L,ZHOU W.A control strategy to reduce fuel consumption of APU for range-extended electric vehicle[C]∥Proceedings of 2nd International Conference on Electronic and Mechanical Engineering and Information Technology.Paris:Atlantis Press,2012:2132-2136. [2] 谢秀磊.增程式电动车辅助动力单元动态建模及协调控制研究[D].长春:吉林大学,2016. XIE Xiulei.Study on dynamic modeling and coordination control for the auxiliary power unit of extended-range electric vehicle[D].Changchun:Jilin University,2016.(in Chinese) [3] 刘汉武.增程式电动汽车增程器的协调控制与性能优化[D].长春:吉林大学,2017. LIU Hanwu.A study on the control strategy and performance optimization for APU of extended-range electric vehicles[D].Changchun:Jilin University,2017.(in Chinese) [4] WANG H,WANG L,LIAO Y,et al.Research on engine speed control system based on fuzzy adaptive PID controller[J].Manufacturing Technology,2019,19(6):1080-1087. [5] MANSOURABAD A M,BEHESHTI M T H,SIMAB M.A hybrid PSO_fuzzy_PID controller for gas turbine speed control[J].International Journal of Control and Automation,2013,6(1):13-24. [6] BOUKHALFA G,BELKACEM S,CHIKHI A,et al.Genetic algorithm and particle swarm optimization tuned fuzzy PID controller on direct torque control of dual star induction motor[J].Journal of Central South University,2019,26(7):1886-1896. [7] 刘爱军,杨育,李斐,等.混沌模拟退火粒子群优化算法研究及应用[J].浙江大学学报(工学版),2013,47(10):1722-1730. LIU Aijun,YANG Yu,LI Fei,et al.Chaotic simulated annealing particle swarm optimization algorithm research and its application[J].Journal of Zhejiang University (Engineering Science),2013,47(10):1722-1730.(in Chinese) [8] WAHONO B,OGAI H.Construction of response surface model for diesel engine using stepwise method[C]∥Proceedings of the 6th International Conference on Soft Computing and Intelligent Systems,and the 13th International Symposium on Advanced Intelligence Systems.Kobe,Japan:IEEE,2012:989-994. [9] 邵金菊.汽油发动机转速变增益鲁棒控制的研究[D].南京:南京航空航天大学,2009. SHAO Jinju.Research on gain scheduling robust control for gasoline engine speed regulation[D].Nanjing:Nanjing University of Aeronautics and Astronautics,2009.(in Chinese) [10] 郭卫,郏高祥,张武,等.基于发动机转速调节特性CVT速比模糊控制仿真研究[J].机械强度,2019,41(1):110-116. GUO Wei,JIA Gaoxiang,ZHANG Wu,et al.Research on fuzzy control simulation of CVT ratio based on engine speed regulation characteristics[J].Journal of Mechanical Strength,2019,41(1):110-116.(in Chinese) [11] 王小青,黄一敏,杨一栋,等.小型无人直升机发动机控制系统设计[J].航空动力学报,2007,22(12):2139-2142. WANG Xiaoqing,HUANG Yimin,YANG Yidong,et al.Control system design for engine of a small-size unmanned helicopter[J].Journal of Aerospace Power,2007,22(12):2139-2142.(in Chinese) [12] ELTAG K,ASLAMX M S,ULLAH R,et al.Dynamic stability enhancement using fuzzy PID control technology for power system[J].International Journal of Control Automation and Systems,2019,17(1):234-242. [13] GONG C,HU Y,GAO J,et al.An improved delay-suppressed sliding-mode observer for sensorless vector-controlled PMSM[J].IEEE Transactions on Industrial Electronics,2020,67(7):5913-5923. [14] 徐利锋,黄祖胜,杨中柱,等.引入多级扰动的混合型粒子群优化算法[J].软件学报,2019,30(6):1835-1852. XU Lifeng,HUANG Zusheng,YANG Zhongzhu,et al.Mixed particle swarm optimization algorithm with multistage disturbances[J].Journal of Software,2019,30(6):1835-1852.(in Chinese) [15] 艾欣,李一铮,王坤宇,等.基于混沌模拟退火粒子群优化算法的电动汽车充电站选址与定容[J].电力自动化设备,2018,38(9):9-14. AI Xin,LI Yizheng,WANG Kunyu,et al.Locating and sizing of electric vehicle charging station based on chaotic simulated annealing particle swarm optimization algorithm[J].Electric Power Automation Equipment,2018,38(9):9-14.(in Chinese) [16] 李勇,王建君,曹丽华.火电厂负荷优化分配的模拟退火粒子群算法[J].电力系统及其自动化学报,2011,23(3):40-44. LI Yong,WANG Jianjun,CAO Lihua.Simulated annealing particle swarm optimization algorithm of optimal load dispatch in power plant[J].Proceedings of the CSU-EPSA,2011,23(3):40-44.(in Chinese) [17] WANG Z,WANG Q,HE D,et al.Animproved particle swarm optimization algorithm based on fuzzy PID control[C]∥Proceedings of 4th International Conference on Information Science and Control Engineering (ICISCE).Changsha:IEEE,2017:835-839. [18] 白洋,段黎明,柳林,等.基于改进的混合粒子群算法的变循环发动机模型求解[J].推进技术,2014,35(12):1694-1700. BAI Yang,DUAN Liming,LIU Lin,et al.Solving variable cycle engine model based on improved hybrid particle swarm optimization[J].Journal of Propulsion Technology,2014,35(12):1694-1700.(in Chinese) [19] DOS SANTOS C L,HERRERA B M.Fuzzy identification based on a chaotic particle swarm optimization approach applied to a nonlinear yo-yo motion system[J].IEEE Transactions on Industrial Electronics,2007,54(6):3234-3245. [20] DOS SANTOS C L,MARIANI V C.A novel chaotic particle swarm optimization approach using Hénon map and implicit filtering local search for economic load dispatch[J].Chaos,Solitons and Fractals,2009,39(2):510-518. [21] REN G,MA G.A novel scheme design of power unit for extended range electric vehicles[J].International Journal of Electric and Hybrid Vehicles,2012,4(4):314-326. [22] 刘晓黎.基于永磁同步电机数学模型的矢量控制理论、仿真、实验及应用研究[D].合肥:合肥工业大学,2017.
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