A neural network learning-based global optimization approach for aero-engine transient control schedule

Abstract Transient performance of aero-engine determines the maneuverability of aircraft. The optimal control schedule can tap the potential transient performance with subject to each constraint. Thus the transient time can be minimized with the optimal transient control schedule. This transient schedule is described by a strong constrained and nonlinear problem. It is therefore challenging to present an optimal method to achieve the best transient schedule. Motivated by solving this problem, a surrogate-assisted optimization framework is presented by using the learning capability of neural networks. It is achieved by presenting a sequential ensemble radial basis function (RBF) neural network-based optimization (SERO) algorithm. The advantage of the RBF neural network such as excellent prediction accuracy is taken and integrated into the surrogate-assisted optimization algorithm to improve the algorithm performance. With the application of the proposed scheme, the global optimization schedule is guaranteed for aero-engine transient control. Numerical validation is finally carried out by applying the presented optimization framework to a mixed-flow aero-engine transient control schedule. It is demonstrated that the SERO approach can minimize the transient process time despite any constraint at any time.