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

Adaptive Fuzzy Immune PID Control for GasGenerator Pressure Based on Artificial BeeColony Algorithm Optimization

关键词:  燃气发生器  燃气流量控制  参变系统  自适应PID  模糊免疫算法  人工蜂群算法

Key words:  Gas generator  Gas flow control  Parameter-varying system  Adaptive PID  Fuzzy immune algorithm  Artificial bee colony algorithm

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。

Abstract:

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.