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基于NSGA-Ⅱ算法的ATR发动机PI控制器多目标优化方法研究
焦昱翔1,2,赵庆军2,3,4,任三群2,蔡伟东2,3,许诚1,赵巍2,3
1.华北电力大学 能源动力与机械工程学院,北京 102206;2.中国科学院工程热物理研究所,北京 100190;3.中国科学院大学 航空宇航学院,北京 100190;4.中国科学院分布式冷热电联供系统北京市重点实验室,北京 100190
摘要:
为了使空气涡轮火箭发动机(ATR)从慢车快速、稳定和准确地加速到最大状态,以ATR发动机燃气发生器流量和尾喷管喉部面积为控制变量,采用快速非支配排序遗传算法(NSGA-Ⅱ算法)建立了发动机控制器参数多目标优化方法。将超调量、稳态误差、上升时间及误差积分值四个指标以加权的形式作为目标函数,引入执行机构超调惩罚机制,建立了PI控制器参数Pareto最优解集,完成了ATR发动机从慢车加速到最大状态的动态过程仿真。结果表明,将双回路多个控制性能指标以加权的形式组合作为目标函数,可以获得均匀分布的Pareto前沿;联合应用多目标优化方法和基于熵权法的优劣解距离法(TOPSIS),能够在双回路耦合下获得满足设计要求的ATR发动机动态特性,极大地缩短了人工整定控制器参数的时间;在加速过程中,多目标优化方法将涡轮膨胀比上升时间作为目标函数之一,与尾喷管面积开环控制动态过程相比,可以使涡轮膨胀比更早到达目标值,共同工作线远离喘振边界。
关键词:  空气涡轮火箭发动机  动态过程  PI控制器  参数优化  遗传算法
DOI:10.13675/j.cnki.tjjs.2302037
分类号:V438
基金项目:国家科技重大专项(J2019-III-0001-0044;J2019-II-0016-0037)。
Multi-objective optimization method of ATR engine PI controller based on NSGA-Ⅱ algorithm
JIAO Yuxiang1,2, ZHAO Qingjun2,3,4, REN Sanqun2, CAI Weidong2,3, XU Cheng1, ZHAO Wei2,3
1.School of Energy,Power and Mechanical Engineering,North China Electric Power University,Beijing 102206,China;2.Institute of Engineering Thermophysics,Chinese Academy of Sciences,Beijing 100190,China;3.School of Aeronautics and Astronautics,University of Chinese Academy of Sciences,Beijing 100190,China;4.Beijing Key Laboratory of Distributed Combined Cooling Heating and Power System, Chinese Academy of Sciences,Beijing 100190,China
Abstract:
In order to accelerate the Air Turbo-Rocket engine (ATR) from the idle to the maximum state rapidly, stably and accurately, a multi-objective optimization method for engine controller parameters was established by using Non-Dominated Sorted Genetic Algorithm-II (NSGA-Ⅱ) with the gas generator flow and nozzle throat area as controlled variables. Taking the overshoot, steady-state errors, rise time and integral of absolute error as objective function in a weighted form, the overshoot penalty mechanism of actuator was introduced, and the Pareto optimal solution set of PI controller parameters was established. The simulation of the dynamic process of ATR engine acceleration from the idle to the maximum state was completed. The results show that the evenly distributed Pareto frontier can be obtained by combining multiple control performance indexes in the form of weighting as the objective function. The combined application of multi-objective optimization method and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method based on entropy weight method can obtain the dynamic characteristics of ATR engine that meet the design requirements under the double-loop coupling, which greatly shorten the time of manual setting controller parameters. In the acceleration process, the multi-objective optimization method takes the rise time of turbine expansion ratio as one of the objective functions. Compared with the dynamic process of nozzle area open-loop control, the turbine expansion ratio can reach the target value earlier, the common work line is far away from the surge boundary.
Key words:  Air turbine rocket engine  Dynamic process  PI controller  Parameter optimization  Genetic algorithm