基于遗传算法-序列二次规划的涡扇发动机最低油耗性能寻优控制

Performance Seeking Control of Turbofan Engine Minimum Fuel Consumption Model Based on GA-SQP

关键词:  涡扇发动机  非线性数学模型  性能寻优控制  GA-SQP混合算法  最低油耗模式  

Key words:  Turbofan Engine  Nonlinear Mathematical Model  Performance Seeking Control  GA-SQP Hybrid Algorithms  Minimum Fuel Consumption Model

摘要:

航空发动机性能寻优控制可充分挖掘发动机潜力，大幅提升发动机性能。燃油消耗率是发动机的一项重要技术指标，对燃油消耗率进行优化，其经济意义及作战效能十分明显。本文针对飞机巡航状态下发动机节油特性进行研究，在保证航空发动机推力不变及安全工作（如保证发动机不超温、不超转、不喘振等）的前提下，使燃油消耗率最小。本文以所建立的某双转子混合排气加力式涡扇发动机非线性数学模型为研究对象，提出了一种基于遗传算法-序列二次规划（GA-SQP）混合优化算法，该优化算法充分发挥了遗传算法和序列二次规划算法的优势，同时在一定程度上克服了两者的缺点，利用Matlab对该优化算法进行了仿真分析。在随机选取的10个飞行状态点对航空发动机最低油耗模式性能寻优控制进行研究后发现：基于GA-SQP混合算法的优化控制可平均降低油耗3.61%（采用基于遗传算法的优化控制则为3.68%），基于GA-SQP混合算法的优化控制的平均耗时为基于遗传算法的优化控制的23.4%。仿真结果表明，基于GA-SQP混合算法的优化控制无需人为设置初始解，不仅能达到与基于遗传算法的优化控制基本相同的优化控制效果，同时还可大幅减少计算量，提高了计算效率。

Abstract:

Aero-engine performance optimization control can fully tap the engine potential and greatly improve the engine performance. The fuel consumption rate is an important technical index of the engine. To optimize the fuel consumption rate, its economic significance and combat effectiveness are very obvious. In this paper, the fuel saving characteristics of the engine under the condition of aircraft cruising are studied, and the fuel consumption rate is minimized on the premise of ensuring the constant thrust and safe work of the aero-engine (such as ensuring that the engine does not overtemperature, does not overturn, does not surge, etc).In this paper, based on the nonlinear mathematical model of a double-rotor hybrid exhaust turbofan engine, a hybrid optimization algorithm based on genetic algorithm-sequential quadratic programming (GA-SQP) is proposed. which gives full play to the advantages of genetic algorithm and SQP algorithm. At the same time, the shortcomings of the two algorithms are overcome to a certain extent, and the optimization algorithm is simulated and analyzed by Matlab. After the performance optimization control of aero-engine minimum fuel consumption mode is studied at 10 randomly selected flight state points, it is found that the optimal control method based on GA-SQP hybrid algorithm can reduce fuel consumption by 3.61% on average（ 3.68% in the optimal control method of genetic algorithm）.The average time of the optimal control method based on GA-SQP hybrid algorithm is 23.4% of that of the optimal control method based on genetic algorithm. The simulation results show that the optimal control based on GA-SQP hybrid algorithm does not need to set the initial solution artificially, which can not only achieve the same optimal control effect as the optimal control based on genetic algorithm, but also greatly reduce the amount of computation. The calculation efficiency is improved.