Practical Fixed-Time Active Surge Control of Aero-Engines
针对航空发动机模型不确定性和喘振故障时间长的问题，提出一种基于RBF神经网络和自适应律的固定时间主动喘振控制方案，通过调节进气流量稳定压气机动态，延长发动机寿命。
The active surge control has superiorities in expanding the stable working range and reducing the performance loss of aero-engines. Despite these benefits, ensuring fixed-time stability of the closed-loop system with model uncertainty remains a significant challenge. Conventional techniques to active surge control of aero-engines often struggle with model uncertainty and suffer from long-time surge fault. To deal with these issues, this article proposes a novel fixed-time active surge control scheme for enhancing the adaptability to the model change and extending the service life of aero-engines. First, considering the model uncertainty in aero-engines, a radial basis function (RBF) neural network is established for the approximation of complex system dynamic. Second, the adaptive law is proposed to optimize the weight vectors of the neural network. Third, a fixed-time controller is designed to ensure responsiveness and stabilize the compressor dynamics by tuning the intake air flow, where the fixed-time stability property guarantees less operation time under the surge fault. Finally, applications in the turbofan aero-engine validate the superiorities of the proposed method.