The invention belongs to the technical field of helicopter control, and provides a
turboshaft engine identification and prediction control method based on MRR-KELM in order to solve the problem that a prediction model constructed for a
turboshaft engine is poor in generalization, an MRR-KELM
algorithm is combined with a Huber
loss function and a self-adaptive regularization strategy,
noise interference and abnormal value influence are restrained, and the prediction model is optimized. The prediction error of the constructed
turboshaft engine prediction model is greatly reduced, and adaptive adjustment can be performed through
Gaussian kernel
dynamic mapping and working condition parameters; according to the
algorithm, a regularization item and a robust
loss function are introduced, the kernel
function mapping capability is combined, the generalization performance and the calculation efficiency of the model under the complex working conditions of small samples,
noise interference and the like are remarkably improved, further, the turboshaft engine prediction model is embedded into a
nonlinear model prediction control framework, and the prediction accuracy of the turboshaft engine is improved. And high-precision stable control over the rotating speed of the
power turbine is achieved through rolling optimization and feedback correction.