Predictive Guidance Algorithms for Hypersonic Vehicles: A Comparative Simulation Study

Abstract

Hypersonic vehicles operating at speeds exceeding Mach 5 face extreme guidance challenges including rapid state evolution, severe aerodynamic heating, control authority limitations, and narrow entry corridors that demand predictive guidance algorithms with exceptional accuracy and computational efficiency. This paper presents a comprehensive comparative simulation study of predictive guidance methods for hypersonic vehicle trajectory control during atmospheric entry and terminal approach phases. We systematically examine four major guidance paradigms: predictor-corrector methods extending classical Apollo-era techniques, model predictive control (MPC) formulations with receding horizon optimization, adaptive guidance using neural networks trained on optimal trajectories, and hybrid approaches combining analytical predictions with numerical corrections. The study develops a high-fidelity simulation environment incorporating six-degree-of-freedom vehicle dynamics, altitude-dependent atmospheric models, aerodynamic coefficient variations with Mach number and angle of attack, and thermal protection system constraints limiting heat flux and total heat load. For each guidance algorithm, we analyze prediction accuracy, computational latency, robustness to model uncertainties, and ability to satisfy path constraints including dynamic pressure limits, heating rate bounds, and terminal accuracy requirements. Comparative evaluation encompasses diverse mission scenarios: ballistic entry with maximum cross-range maneuvering, skip entry trajectories for extended range, and precision landing within 100-meter target circles. Simulation results quantify the trade-offs between guidance optimality and computational burden, revealing that convex optimization-based MPC achieves near-optimal performance with 10-50 Hz update rates feasible on modern flight computers, while neural network guidance enables kilohertz-rate updates with 5-10% optimality loss. We investigate robustness through Monte Carlo analysis with dispersions in atmospheric density (±30%), aerodynamic coefficients (±20%), and initial state errors, identifying that adaptive guidance methods demonstrate superior performance under large uncertainties. The study examines advanced techniques including successive convexification for non-convex constraint handling, Gaussian process regression for online aerodynamic model refinement, and reinforcement learning for guidance policy optimization. Particular attention is devoted to computational implementation aspects including algorithm parallelization, reduced-order atmospheric models, and onboard trajectory database interpolation. The paper concludes with recommendations for guidance algorithm selection based on mission requirements and identifies research gaps in formal verification of predictive guidance safety and handling of actuator failures during hypersonic flight.