基于人工蜂群算法优化的燃气发生器压强自适应模糊免疫PID控制
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摘要
固冲发动机燃气流量控制系统因具有较强的非线性和时变性,导致其控制问题较难解决。为了实现对燃气压强的精确闭环控制,设计了基于人工蜂群算法优化的自适应模糊免疫PID (ABC-AFI-PID)控制器。控制器的比例系数由模糊免疫控制器在线修正,积分和微分系数由自适应模糊控制器实时调整,并应用人工蜂群算法对控制器的设计参数进行鲁棒优化。采用ABC-AFI-PID控制器、自适应模糊PID (AF-PID)控制器和传统PID控制器分别对某滑盘阀式流量控制系统的线性和非线性模型进行仿真,来验证控制器在设计工作点(7.24MPa)附近以及全压强调节范围内的动态性能和稳态性能。结果表明:在不同的工况下,ABC-AFI-PID控制器体现出良好的品质。相比于AF-PID控制器可将压强响应速度提高约1.8～2.5倍,相比于传统的PID控制器可将压强响应速度提高约4.6～5.1倍,并且其超调量也被控制在7.14%以内。该控制器在快速性、稳定性和鲁棒性上均展现出了巨大优势,显著地提高了燃气流量控制系统的性能。
Gas flow control system for solid rocket ramjet is difficult to be solved due to its strong nonlinear and time-varying characteristics. Adaptive fuzzy immune PID controller based on artificial bee colony algorithm(ABC-AFI-PID)optimization was designed in order to achieve accurate pressure closed-loop control. The proportionality of controller was duly corrected by fuzzy immune controller,the integral and derivative coefficients were adjusted by adaptive fuzzy controller in real time,and the artificial bee colony algorithm was used to robustly optimize the design parameters. The linear and nonlinear models of a sliding disc valve flow control system were simulated by using ABC-AFI-PID controller,AF-PID controller and traditional PID controller to verify the dynamic and steady-state performance of the new controller nearby design working point(7.24MPa)and full pressure regulation range. Simulation results show that ABC-AFI-PID controller is in possession of good quality at different working conditions. Compared with AF-PID controller,the response speed of pressure can be increased by about 1.8～2.5 times; compared with traditional PID controller,the response speed of pressure can be increased by about 4.6～5.1 times. And its overshoot is also controlled within 7.14%. This controller has shown great advantages in rapidity,stability and robustness characteristics. Hence,the performance of gas flow control system is remarkably improved.
Gas flow control system for solid rocket ramjet is difficult to be solved due to its strong nonlinear and time-varying characteristics. Adaptive fuzzy immune PID controller based on artificial bee colony algorithm(ABC-AFI-PID)optimization was designed in order to achieve accurate pressure closed-loop control. The proportionality of controller was duly corrected by fuzzy immune controller,the integral and derivative coefficients were adjusted by adaptive fuzzy controller in real time,and the artificial bee colony algorithm was used to robustly optimize the design parameters. The linear and nonlinear models of a sliding disc valve flow control system were simulated by using ABC-AFI-PID controller,AF-PID controller and traditional PID controller to verify the dynamic and steady-state performance of the new controller nearby design working point(7.24MPa)and full pressure regulation range. Simulation results show that ABC-AFI-PID controller is in possession of good quality at different working conditions. Compared with AF-PID controller,the response speed of pressure can be increased by about 1.8～2.5 times; compared with traditional PID controller,the response speed of pressure can be increased by about 4.6～5.1 times. And its overshoot is also controlled within 7.14%. This controller has shown great advantages in rapidity,stability and robustness characteristics. Hence,the performance of gas flow control system is remarkably improved.
引文
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[9]何坤,陈雄,郑健,等.模糊PI控制算法在燃气发生器上的应用[J].推进技术,2017,38(8):1878-1884.(HE Kun,CHEN Xiong,ZHENG Jian,et al. Fuzzy PI Control Algorithm for Gas Generator[J].Journal of Propulsion Technology,2017,38(8):1878-1884.)
[10]刘源翔,聂聆聪,张皎,等.基于线性自抗扰控制器的燃气流量可调发生器压强控制算法研究[J].推进技术,2015,36(12):1768-1773.(LIU Yuanxiang,NIE Ling-cong,ZHANG Jiao,et al. Research on Pressure Control Algorithm of a Flow Adjustable Gas Generator Based on Linear Active Disturbance Rejection Control[J]. Journal of Propulsion Technology,2015,36(12):1768-1773.)
[11]刘金琨.先进PID控制MATLAB仿真[M].北京:电子工业出版社,2011.
[12]胡包钢,应浩.模糊PID控制技术研究发展回顾及其面临的若干重要问题[J].自动化学报,2001,27(4):567-584.
[13]王东风,韩璞.基于免疫遗传算法优化的汽温系统变参数PID控制[J].中国电机工程学报,2003,23(9):212-217.
[14]杨石林,高波,董新刚.滑盘式流量调节燃气发生器动态特性分析[J].固体火箭技术,2009,32(5):506-510.
[15]何勇攀,陈玉春,于守志,等.固体火箭冲压发动机燃气发生器动态特性影响分析[J].航空动力学报,2017,32(1):227-232.
[16]孙涛.基于模糊免疫自适应PID的智能控制算法的研究[D].大连:大连海事大学,2009.
[17]何景峰,沈刚,丛大成,等.模糊免疫PID控制在淀粉浓度控制中的应用[J].农业机械学报,2009,40(1):137-142.
[18]董全成,冯显英.基于自适应模糊免疫PID的轧花自动控制系统[J].农业工程学报,2013,29(23):30-37.
[19]高宪文,赵亚平.焦炉模糊免疫自适应PID控制的应用研究[J].控制与决策,2005,20(12):1346-1349.
[20]石辛民,郝整清.模糊控制及其MATLAB仿真[M].北京:清华大学出版社,2008.
[21]卢彬彬,肖玲斐,龚仁吉,等.基于人工蜂群算法的航空发动机参数自整定PID控制[J].推进技术,2015,36(1):130-135.(LU Bin-bin,XIAO Ling-fei,GONG Ren-ji,et al. Self-Tuning PID Control for Aeroengine Based on Artificial Bee Colony Algorithm[J].Journal of Propulsion Technology,2015,36(1):130-135.)
[22]江铭炎,袁东风.人工蜂群算法及其应用[M].北京:科学出版社,2014.
[23] ZHANG Dong-li,TANG Ying-gan,GUAN Xin-ping.Optimum Design of Fractional Order PID Controller for an AVR System Using an Improved Artificial Bee Colony Algorithm[J]. Acta Automatica Sinica,2014,40(5):973-980.
[2] Nakayama H,Ikegami Y,Yoshida A. Full-Scale Firing Tests of Variable Flow Ducted Rocket Engines Employing GAP Solid Fuel Gas Generator[R]. AIAA 2009-5121.
[3]刘源翔.变流量整体式固冲发动机控制系统研究[D].北京:北京理工大学,2015.
[4] BAO W,LI B,CHANG J. Switching Control of Thrust Regulation and Inlet Buzz Protection for Ducted Rocket[J]. Acta Astronautica,2010,67(7–8):764-773.
[5]霍东兴,闫大庆,高波.可变流量固体冲压发动机技术研究进展与展望[J].固体火箭技术,2017,40(1):7-15.
[6]杨石林,张岗.可变燃气流量固冲发动机控制系统研究进展[C].西安:中国航天第三专业信息网第三十七届技术交流会暨第一届空天动力联合会议,2016.
[7]龚晰,何保成,刘志明.固体火箭冲压发动机发展与流量调节现状[J].飞航导弹,2015,(2):74-78.
[8]聂聆聪,刘志明,刘源祥.流量可调燃气发生器压力闭环模糊控制算法[J].推进技术,2013,34(4):551-556.(NIE Ling-cong,LIU Zhi-ming,LIU Yuanxiang. Pressure Close Loop Fuzzy Control Method of a Flow Adjustable Gas Generator[J]. Journal of Propulsion Technology,2013,34(4):551-556.)
[9]何坤,陈雄,郑健,等.模糊PI控制算法在燃气发生器上的应用[J].推进技术,2017,38(8):1878-1884.(HE Kun,CHEN Xiong,ZHENG Jian,et al. Fuzzy PI Control Algorithm for Gas Generator[J].Journal of Propulsion Technology,2017,38(8):1878-1884.)
[10]刘源翔,聂聆聪,张皎,等.基于线性自抗扰控制器的燃气流量可调发生器压强控制算法研究[J].推进技术,2015,36(12):1768-1773.(LIU Yuanxiang,NIE Ling-cong,ZHANG Jiao,et al. Research on Pressure Control Algorithm of a Flow Adjustable Gas Generator Based on Linear Active Disturbance Rejection Control[J]. Journal of Propulsion Technology,2015,36(12):1768-1773.)
[11]刘金琨.先进PID控制MATLAB仿真[M].北京:电子工业出版社,2011.
[12]胡包钢,应浩.模糊PID控制技术研究发展回顾及其面临的若干重要问题[J].自动化学报,2001,27(4):567-584.
[13]王东风,韩璞.基于免疫遗传算法优化的汽温系统变参数PID控制[J].中国电机工程学报,2003,23(9):212-217.
[14]杨石林,高波,董新刚.滑盘式流量调节燃气发生器动态特性分析[J].固体火箭技术,2009,32(5):506-510.
[15]何勇攀,陈玉春,于守志,等.固体火箭冲压发动机燃气发生器动态特性影响分析[J].航空动力学报,2017,32(1):227-232.
[16]孙涛.基于模糊免疫自适应PID的智能控制算法的研究[D].大连:大连海事大学,2009.
[17]何景峰,沈刚,丛大成,等.模糊免疫PID控制在淀粉浓度控制中的应用[J].农业机械学报,2009,40(1):137-142.
[18]董全成,冯显英.基于自适应模糊免疫PID的轧花自动控制系统[J].农业工程学报,2013,29(23):30-37.
[19]高宪文,赵亚平.焦炉模糊免疫自适应PID控制的应用研究[J].控制与决策,2005,20(12):1346-1349.
[20]石辛民,郝整清.模糊控制及其MATLAB仿真[M].北京:清华大学出版社,2008.
[21]卢彬彬,肖玲斐,龚仁吉,等.基于人工蜂群算法的航空发动机参数自整定PID控制[J].推进技术,2015,36(1):130-135.(LU Bin-bin,XIAO Ling-fei,GONG Ren-ji,et al. Self-Tuning PID Control for Aeroengine Based on Artificial Bee Colony Algorithm[J].Journal of Propulsion Technology,2015,36(1):130-135.)
[22]江铭炎,袁东风.人工蜂群算法及其应用[M].北京:科学出版社,2014.
[23] ZHANG Dong-li,TANG Ying-gan,GUAN Xin-ping.Optimum Design of Fractional Order PID Controller for an AVR System Using an Improved Artificial Bee Colony Algorithm[J]. Acta Automatica Sinica,2014,40(5):973-980.
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