This application provides a data-driven multivariable adaptive predictive control method for an adaptive cyclic engine, belonging to the field of aero-engine technology. The method includes: constructing an equivalent
data model of the adaptive cyclic engine based on full-format dynamic
linearization of the engine's input and output data to obtain multi-step forward output prediction equations; performing real-
time estimation of the pseudo-gradient matrix in the prediction equations using autoregressive and
projection algorithms to form a predictive control rolling optimization framework; embedding a model-free
adaptive control algorithm based on single-step optimization into this framework to form a composite control strategy; designing control input performance indicators with the goal of minimizing
tracking error to obtain
optimal control parameters; and generating real-
time optimal control parameters using the engine's real-time input and output data based on the composite control strategy to achieve precise multivariable coordinated control. This application improves the long-term prediction, constraint handling, and
global optimization capabilities of the
control system.