Research on Event-Triggered Model Predictive Control for Hypersonic Vehicles Based on Gap Metric

Hypersonic vehicles are characterized by high nonlinearity, strong coupling, and rapidly time-varying dynamics. Directly solving the associated nonlinear optimization problems online imposes a prohibitive computational burden, hindering real-time control implementation. To address these critical challenges, this paper introduces a multi-model control strategy based on gap metric theory. Specifically, the nonlinear model is linearized at multiple equilibrium points to construct a multi-model set constrained by a predefined gap threshold, where an independent controller is synthesized for each sub-model. This approach guarantees control accuracy across the full flight envelope. Furthermore, an event-triggered Model Predictive Control (MPC) algorithm is proposed, utilizing the gradient of the deviation between the actual state and the predicted state. This algorithm effectively mitigates the conservatism in trigger threshold design, significantly reducing computational load while enhancing dynamic response. Simulation results validate the effectiveness of the proposed strategy, demonstrating that it ensures system stability while substantially economizing computational resources.