摘要
关于涡扇发动机最优加速控问题,由于状态系统存在较强的非线性,控制性能差,改善发动机加速性,传统非线性规划算法求解过程中因采用罚函数处理约束条件而无法充分搜索控制参数的可行域。为提高系统性能,并充分挖掘发动机的加速特性,采用Sigma方法的多目标粒子群算法求解。可以在带限制因子的粒子群算法的基础上,利用粒子群算法的快速寻优能力和Sigma方法沿约束边界的充分搜索方法,求解发动机加速过程中控制参数,并进行仿真。结果证明,采用多目标粒子群算法优化后,加速时间缩短了约2.01s,结果表明改进方法是可行的,能在确保发动机安全工作的前提下,进一步提升了发动机的加速性能。
Due the strong nonlinearity of state system of optimal acceleration control of turbofan engine,the control performance is poor.In order to promote the acceleration performance,the traditional nonlinear programming method is usually used,whose problem is that the feasible domain of control parameters is searched insufficiently due to the application of penalty function to process constraints.To enhance the system performance and promote the acceleration performance sufficiently,Sigma-based multi-objective particle swarm optimization（MOPSO） was applied to solve this problem,which synthesized fast optimization of constricted factor PSO and boundary searching of Sigma method to solve the acceleration control parameters.The simulation results show that the acceleration time is reduced by 2.01s through the application of MOPSO,and the acceleration performance is further promoted at the premise of safety,which validates the Sigma-based MOPSO method.
出处
《计算机仿真》
CSCD
北大核心
2012年第3期162-166,共5页
Computer Simulation
关键词
涡扇发动机
多目标最优化
加速控制
粒子群优化
Turbofan engine
Multi-objective optimization
Acceleration control
Particle swarm optimization
