Event-Triggered Nonlinear Model Predictive Control for Optimal Ascent Guidance

Recent decades have seen a surge in research on trajectory optimization (or optimal control) as a means of achieving computational guidance, which can be regarded as a nonlinear model predictive guidance (NMPG) framework. Most existing NMPG methods are time-triggered and can be classified as the fixed guidance cycle NMPG (FC-NMPG) or the uninterrupted online optimization NMPG (UI-NMPG). The FC-NMPG method exhibits inadequate disturbance rejection, while the UI-NMPG method places exceedingly high demands on computational resources and is not conducive to control system design. This paper develops an event-triggered nonlinear model predictive guidance (ET-NMPG) method and applies it to ascent guidance. In the ET-NMPG method, online trajectory optimization is only executed when the deviation between the actual state and the reference trajectory exceeds a set threshold, thus significantly reducing the computational load on the guidance system. Mesh reduction and mesh truncation algorithms are designed to generate the appropriate discrete mesh, which enhances the stability and efficiency of trajectory optimization. In addition, an initial state prediction step is additionally formulated to prevent Zeno behavior during ET-NMPG application, which concurrently improves the alignment between the optimal command and flight state. Monte Carlo simulation results underscore that the proposed method outperforms alternative methods in terms of both accuracy and reduced computational expenditure.