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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。
As to the optimal acceleration control of turbofan engine, due to the existence of strong nonlinear state system, poor control performance and improvement of engine acceleration, the control parameters can not be fully searched for using the penalty function in solving the traditional nonlinear programming algorithm Feasible domain. In order to improve system performance and fully exploit the acceleration characteristics of the engine, a multi-objective particle swarm optimization algorithm based on the Sigma method was used to solve the problem. Based on the particle swarm optimization algorithm with limited factor, the particle swarm optimization algorithm can be used to solve the control parameters in acceleration process based on the rapid optimization ability of particle swarm optimization algorithm and the full search method along the constraint boundary of Sigma method. The results show that the acceleration time is shortened by about 2.01s using the multi-objective particle swarm optimization algorithm. The results show that the improved method is feasible and can further improve the engine’s acceleration performance under the premise of ensuring the safety of the engine.