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针对现代航空发动机是一个具有不确定性的强非线性系统,提出了一种基于自适应PSO网络整定的航空发动机全程滑模控制方法。设计了一类全程滑模面非线性函数,函数中含有变参数指数函数,其参数由一种新的自适应粒子群学习算法(PSO)结合RBF神经网络来整定。全程滑模控制保证了控制系统的全程鲁棒性,同时,由稳态误差收敛速度和滑模抖振幅度建立参数优化指标,用自适应PSO神经网络快速搜索当前的全局最优点。仿真结果表明,所设计的控制器取得了良好的效果,削弱了抖振。
Aiming at the strong nonlinear system with uncertainties, the modern aero-engine is proposed. A sliding mode control method for aeroengines based on adaptive PSO network tuning is proposed. A class of nonlinear sliding mode sliding surface function is designed. The function contains the variable parameter exponential function. Its parameters are tuned by a new adaptive particle swarm optimization algorithm (PSO) combined with RBF neural network. The whole sliding mode control ensures the robustness of the whole control system. At the same time, the parameter optimization index is established by steady-state error convergence rate and sliding mode chattering amplitude, and the current global optimal point is searched quickly by adaptive PSO neural network. The simulation results show that the designed controller achieves good results and weakens the chattering.