Adaptive fault-tolerant control of distributed electric propulsion aircraft based on multivariable model predictive control

Abstract

Distributed Electric Propulsion (DEP) aircraft presents a highly promising technology aligned with the demands of green aviation due to its superior flight efficiency, reduced energy loss, and diminished noise. This paper firstly designs and establishes a flight dynamics model for DEP aircraft, followed by an analysis of flying qualities, thus laying the foundation for control method design. Secondly, addressing the challenge of detecting subtle control surface faults, a deep learning fault diagnosis method based on Data Dimensionality Reduction (DDR) structure is devised, achieving an accuracy rate of 99.8 %. Finally, an adaptive fault-tolerant control method for the lateral motion mode of DEP aircraft is introduced, utilizing Constrained Multivariable-multiobjective Model Predictive Control (CMMPC). Notably, for the rudder complete fault, adaptive fault-tolerant control is achieved by switching controllers and utilizing differential thrust from distributed redundant propulsion units to mitigate yaw channel anomalies, thus showcasing the advantages of DEP aircraft. Simulation results validate the theoretical soundness of the proposed method, providing a theoretical basis for the future engineering application and advancement of control systems for DEP aircraft.