关键词: 航空发动机;全程滑模控制;RBF神经网络; 粒子群优化算法

Abstract: A method of global sliding mode control was put forward for aeroengine with uncertainty and strong nonlinearity based on self-adaptive PSO neural network. A kind of global sliding mode surface nonlinear function was designed, which included a variational exponent function, and the function parameters were adjusted by a adaptive particle swarm optimization(PSO) study arithmetic combined RBF neural network. The global sliding mode control assured control system global robustness. At the same time, the optimization formula about parameters were established based on static state error convergent speed and sliding mode chattering value, and the adaptive PSO neural network was searching current global optimization point. Simulation results show that the devised controller has good effect and weakens chattering. 

Key words: Aeroengine; Global sliding mode control(GSMC); RBF neural network; Particle swarm optimization(PSO)