Hypersonic Glide Research Articles

Hypersonic glide vehicle trajectory prediction based on frequency enhanced channel attention and light sampling-oriented MLP network

Hypersonic Glide Vehicles (HGVs) are advanced aircraft that can achieve extremely high speeds (generally over 5 Mach) and maneuverability within the Earth's atmosphere. HGV trajectory prediction is crucial for effective defense planning and interception strategies. In recent years, HGV trajectory prediction methods based on deep learning have the great potential to significantly enhance prediction accuracy and efficiency. However, it's still challenging to strike a balance between improving prediction performance and reducing computation costs of the deep learning trajectory prediction models. To solve this problem, we propose a new deep learning framework (FECA-LSMN) for efficient HGV trajectory prediction. The model first uses a Frequency Enhanced Channel Attention (FECA) module to facilitate the fusion of different HGV trajectory features, and then subsequently employs a Light Sampling-oriented Multi-Layer Perceptron Network (LSMN) based on simple MLP-based structures to extract long/short-term HGV trajectory features for accurate trajectory prediction. Also, we employ a new data normalization method called reversible instance normalization (RevIN) to enhance the prediction accuracy and training stability of the network. Compared to other popular trajectory prediction models based on LSTM, GRU and Transformer, our FECA-LSMN model achieves leading or comparable performance in terms of RMSE, MAE and MAPE metrics while demonstrating notably faster computation time. The ablation experiments show that the incorporation of the FECA module significantly improves the prediction performance of the network. The RevIN data normalization technique outperforms traditional min-max normalization as well.

Multitask-constrained reentry trajectory planning for hypersonic gliding vehicle

This paper studies the reentry trajectory planning problems for hypersonic gliding vehicle under multiple tasks. Different from the former achievements, this paper takes into account the practical tasks involved in the reentry phase, including penetration of interceptors, evasion of the no-fly zones, and the arrival of the waypoints. Firstly, the constraints during the reentry phase are analyzed in detail, and the original trajectory planning problem is formulated. Secondly, the hp-adaptive pseudospectral discretization method is proposed to effectively reduce the discretization error. Relevant variables are introduced to relax and transform severe intractable constraints of multiple nonconvex forms, thus enhancing the robustness of the planning process. Thirdly, the improved sequential convex programming with decision variables algorithm is proposed to ensure the converged trajectory satisfies complicated tasks. The theoretical analysis is also presented to demonstrate that the converged trajectory is the approximate stationary solution of the discrete form of the original problem. Finally, the effectiveness of the proposed algorithms is validated through numerical simulation.

Collision avoidance and formation control of hypersonic glide vehicles within predefined time

Collision avoidance and formation control of hypersonic glide vehicles within predefined time

Analytical Solutions for Hypersonic Glide Trajectory Based on Altitude–Velocity Profile

Analytical Solutions for Hypersonic Glide Trajectory Based on Altitude–Velocity Profile

Intelligent Trajectory Prediction Algorithm for Hypersonic Vehicle Based on Sparse Associative Structure Model

The Hypersonic Glide Vehicle (HGV) has become a focal point in military competitions among nations. Predicting the real-time trajectory of an HGV is of significant importance for aerospace defense interception and assessing enemy combat intentions. Existing prediction methods are limited by the need for large data samples and poor general applicability. To address these challenges, this paper presents a novel trajectory forecasting approach based on the Sparse Association Structure Model (SASM). The SASM can uncover the relationship among known data, transfer associative relationships to unknown data, and improve the generalization of the model. Firstly, a trajectory database is established for different maneuvering modes based on the six-degree-of-freedom motion equations and models of attack and bank angles of the HGV. Subsequently, three trajectory parameters are selected as prediction variables according to the maneuvering characteristics of the HGV. A parameters prediction model based on the SASM is then constructed to predict trajectory parameters. The SASM model demonstrates high accuracy and generalization in forecasting the trajectories of three different HGV types. Experimental results show a 50.35% reduction in prediction error and a 48.7% decrease in average processing time compared to the LSTM model, highlighting the effectiveness of the proposed method for real-time trajectory forecasting.

Parameterized evasion strategy for hypersonic glide vehicles against two missiles based on reinforcement learning

In practical combat scenarios, Hypersonic Glide Vehicles (HGV) face the challenge of evading Successive Pursuers from the Same Direction while satisfying the Homing Constraint (SPSDHC). To address this problem, this paper proposes a parameterized evasion guidance algorithm based on reinforcement learning. The three-player optimal evasion strategy is firstly analyzed and approximated by parametrization. The switching acceleration command of HGV optimal evasion strategy considering the upper limit of missile acceleration command is analyzed based on the optimal control theory. The terminal miss of HGV in the case of evading two missiles is analyzed, which means that the three-player optimal evasion strategy is a linear combination of two one-to-one strategies. Then, a velocity control algorithm is proposed to increase the terminal miss by actively controlling the flight speed of the HGV based on the parametrized evasion strategy. The reinforcement learning method is used to implement the strategy in real time and a reward function is designed by deducing homing strategy for the HGV to approach the target, which ensures that the HGV satisfies the homing constraint. Experimental results demonstrate the feasibility and robustness of the proposed parameterized evasion strategy, which enables the HGV to generate maximum terminal miss and satisfy homing constraint when facing single or double missiles.

A segmented trajectory planning and guidance method for hypersonic glide vehicles considering target detection performance

This paper proposes a closed-loop detection and guidance method for optimizing the detection effectiveness of Radio Frequency (RF) stealth targets by terminal-guided hypersonic glide vehicles (HGV) during the terminal-guidance phase. This method divides the guidance process into two stages based on the detectability of the target. In the search stage, the Twin Delayed Deep Deterministic Policy Gradient (TD3) coupled with Successive Convex Optimization (SCO) guidance method is employed. This approach separates the detection performance from the numerical solution process of the optimization problem, enabling online adjustment of performance indicator functions to refine trajectory configurations. In the tracking phase, the dynamic characteristics of the target's Radar Cross Section (RCS) are convexified and discretized, incorporated into the objective function of the optimization problem as performance indicators. The Higher-Order Soft-Trust-Region Sequential Convex Programming (HSSCP) method is employed to improve the convergence of the algorithm. Additionally, the online trajectory planning problem is integrated into the Model Predictive Control (MPC) framework to achieve closed-loop guidance. Simulation results indicate that this method can improve the detection performance of RF stealthy targets during the terminal guidance phase, the cumulative detection information for the target increased by approximately 20.6%, and the maximum instantaneous detection probability improved by about 14.3%. Moreover, it can achieve convergence of complex multi-constraint problems within a limited number of iterations.

Enhanced Trajectory Forecasting for Hypersonic Glide Vehicle via Physics-Embedded Neural ODE

Forecasting hypersonic glide vehicle (HGV) trajectories accurately is crucial for defense, but traditional methods face challenges due to the scarce real-world data and the intricate dynamics of these vehicles. Data-driven approaches based on deep learning, while having emerged in recent years, often exhibit limitations in predictive accuracy and long-term forecasting. Whereas, physics-informed neural networks (PINNs) offer a solution by incorporating physical laws, but they treat these laws as constraints rather than fully integrating them into the learning process. This paper presents PhysNODE, a novel physics-embedded neural ODE model for the precise forecasting of HGV trajectories, which directly integrates the equations of HGV motion into a neural ODE. PhysNODE leverages a neural network to estimate the hidden aerodynamic parameters within these equations. These parameters are then combined with observable physical quantities to form a derivative function, which is fed into an ODE solver to predict the future trajectory. Comprehensive experiments using simulated datasets of HGV trajectories demonstrate that PhysNODE outperforms the state-of-the-art data-driven and physics-informed methods, particularly when training data is limited. The results highlight the benefit of embedding the physics of the HGV motion into the neural ODE for improved accuracy and stability in trajectory predicting.

Predictor-corrector reentry guidance for hypersonic glide vehicles based on high-precision analytical solutions

To address the challenge of improving downrange prediction accuracy while reducing unnecessary bank reversals, we propose a predictor-corrector guidance method based on high-precision analytical solutions. First, precise analytical solutions for downrange and crossrange are derived to establish the foundation for the guidance law, and we use a neural network model for accurate flight-path angle prediction. Next, we compensate for Earth's rotation effects in the downrange prediction by employing the Taylor expansion. Aiming to eliminate phugoid oscillations, we add the flight-path angle feedback into the command of the bank angle and angle of attack. Subsequently, a lateral guidance law that combines the heading angle error corridor with predicting bank reversals is introduced. The sign of the bank angle is determined by the corridor during the initial flight phase. Following the vehicle travels a specified downrange distance, we use the crossrange analytical solution to regulate subsequent bank reversals. Simulations under disturbance conditions demonstrate that the proposed guidance method has high accuracy and strong robustness, effectively meeting various constraints.

Adaptive Fuzzy Fault-Tolerant Attitude Control for a Hypersonic Gliding Vehicle: A Policy-Iteration Approach

In this paper, adaptive fuzzy fault-tolerant control (AFFTC) for the attitude control system of a hypersonic gliding vehicle (HGV) experiencing an actuator fault is proposed. Actuator faults of the HGV are considered with respect to its actual structure and actuator characteristics. The HGV’s attitude system is firstly represented by a T–S fuzzy model, and then a normal T–S fuzzy controller is designed. A reinforcement learning (RL)-based policy iterative solution algorithm is proposed for the solving of the T-S fuzzy controller. Then, based on the normal T–S controller, a fuzzy FTC controller is proposed in which the control matrices can improve themselves according to the special fault. An integral reinforcement learning (IRL)-based solving algorithm is proposed to reduce the dependence of the design methods on the HGV model. Simulations on three different kinds of actuator faults show that the designed IRL-based FTC can ensure a reliable flight by the HGV.

Not More, But More Assured: Optimising US Nuclear Posture

The Congressional Commission on the Strategic Posture of the United States has urged Washington to adopt new measures to maintain a credible nuclear deterrent against Russia and China in a deteriorating security environment. Any effort by the United States to match its two nuclear peers warhead for warhead, delivery system for delivery system, would be costly folly, potentially reprising the Cold War arms race. A qualitative rather than quantitative response may well be more effective. Developing nuclear-capable hypersonic glide vehicles and cruise missiles, though challenging, would improve Washington’s ability to shape an opponent’s decision calculus and reduce its capacity to pursue deterrence by denial. These systems would also enhance the United States’ ability to hold at risk multiple high-value, well-defended targets and reinforce planners’ confidence in retaliatory strikes. Finally, a qualitative focus would enable the US to argue more credibly for quantitative limitations at levels similar to or lower than those existing today.

An Analytical Reentry Solution Based Online Time-Coordinated A* Path Planning Method for Hypersonic Gliding Vehicles Considering No-Fly-Zone Constraint

To meet the time-coordinated requirement of hypersonic gliding vehicles to reach a single target simultaneously in the presence of no-fly-zone constraints, this paper proposes a time-coordinated A* path planning method considering multiple constraints. The path planning method is designed based on an analytical steady gliding path model and the framework of the A* algorithm. Firstly, an analytical steady gliding path model is designed based on a quadratic function-type altitude-velocity profile. It can derive the control commands explicitly according to the desired terminal altitude and velocity, thus establishing a mapping between the terminal states and the control commands. Secondly, the node extension method of the A* algorithm is improved based on the mapping. Taking the terminal states as new design variables, a feasible path-node set is produced by a one-step integration using the control commands derived according to different terminal states. This node extension method ensures the feasibility of the path nodes while satisfying terminal constraints. Next, the path evaluation function of the A* algorithm is modified by introducing a heuristic switching term to select the most proper node as a waypoint, aiming to minimize the arrival time deviation. Meanwhile, introducing the penalty items into the path evaluation function satisfies the no-fly-zone constraints, process constraints, and control variable constraints. Finally, an online time-coordinated method is proposed to determine a commonly desired arrival time for several hypersonic gliding vehicles. It eliminates the need to specify the arrival time in advance and improves the capability to deal with sudden threats, increasing the path planning method’s online application capability. The proposed method can achieve online time-coordinated multi-constraint path planning for several hypersonic gliding vehicles, whose effectiveness and superiority are verified by simulations.

Aerodynamic Analysis of Hypersonic Gliding Vehicles with Wide-Speed Range Based on the Cuspidal Waverider

Aerodynamic Analysis of Hypersonic Gliding Vehicles with Wide-Speed Range Based on the Cuspidal Waverider

Reentry trajectory optimization of hypersonic glide vehicle based on improved particle swarm algorithm

The reentry process of hypersonic glide vehicle presents a host of intricate constraint issues. A method for optimizing the reentry trajectory of hypersonic glide vehicle is presented to achieve the shortest possible reentry time. The approach utilizes the improved particle swarm optimization (IPSO) algorithm. Firstly, population initialization is performed using the quasi-oppositional differential evolution (QODE) strategy to promote diversity within the population. Then an adaptive adjustment algorithm is designed for the inertia weight coefficient and learning factor to achieve a balance between global and local search capabilities. Finally, utilizing the IPSO algorithm for optimizing the flight parameters of the reentry trajectory, the reentry time is reduced from 338 seconds to 335 seconds. The simulations results indicate that the IPSO algorithm outperforms both the standard PSO algorithm and genetic algorithm (GA) in terms of optimization performance index.

Analysis of the aerodynamic performance of a hypersonic gliding missile with a deflected warhead

In this study, we propose a scheme to control the deflection of the warhead based on the configuration of the hypersonic glide body (HGB) to solve the problems posed by its large control surface load and severe aerodynamic heat under hypersonic flight conditions. We conducted numerical simulations on the configurations of deflection of the warhead of an HGB analog under different flight modes as well as varying angles and directions of deflection. The results showed that once the warhead had been deflected, the overall configuration of the HGB analog still exhibited static longitudinal stability. An increase in the angle of deflection significantly reduced the lift-to-drag ratio of the configuration at large angles of attack. When the warhead was deflected upward, the configuration of the HGB analog exhibited static lateral instability, while it exhibited a high static lateral stability when the warhead was deflected downward.

Flight control design for a hypersonic waverider configuration: A non-linear model following control approach

AbstractDLR is currently investigating the potential of hypersonic flight systems in the context of different mission scenarios. One configuration type of higher interest for civil and military purposes is the hypersonic glide vehicle (HGV). Such HGVs operate over broad flight envelopes and pose complex flight dynamic characteristics. This article presents DLR’s generic hypersonic glide vehicle 2 (GHVG-2) and proposes an integrated non-linear flight control architecture that is based on the idea of the non-linear dynamic inversion and non-linear model following control methodologies. The proposed control scheme is designed to sufficiently and robustly handle the system dynamics of the over-actuated flight vehicle. The approach is first discussed, and the performance of the suggested control laws is later investigated via simulations of a high-fidelity non-linear flight dynamic model in the nominal case and under the presence of parametric uncertainties. The presented results demonstrate that the proposed NMFC approach provides benefits for the robust control of the regarded hypersonic system.

Lateral Maneuver Discrimination for Hypersonic Glide Vehicles: A Hybrid Approach Combining Model-Driven and Data-Driven Methods

Lateral Maneuver Discrimination for Hypersonic Glide Vehicles: A Hybrid Approach Combining Model-Driven and Data-Driven Methods

Hypersonic boost-glide systems: Flight mechanics and plasma parameters evaluation through aero-thermo-chemical computational fluid dynamics

Currently, the conception, design, and development of hypersonic space-borne systems is a central topic of discussion among researchers and industrial stakeholders. In the design phase of a hypersonic manoeuvrable vehicle, it is essential to characterise the thermal heating and manage the problem of the communication blackout, in addition to other challenges concerning guidance, navigation and control, and propulsion. The interference of electromagnetic waves propagating in plasma affects the datalink and the radar detection of hypersonic targets. This work aims to provide valuable information about the aero-thermo-chemical behaviour of the fluid in the region around a hypersonic glider. Launch and re-entry phases of boost-glide systems have been modelled to perform trajectory predictions and assess range and flight paths under the effect of different manoeuvres, defining their possible performances. The resulting flight conditions are used to study the flow field surrounding a glider geometry similar to the Hypersonic Technology Vehicle-2. The thermodynamic and chemical fields around the body have been determined, and plasma parameters for novel electromagnetic interaction studies have been evaluated. Computational fluid dynamics simulations have been carried out to solve the averaged Navier-Stokes equations with the k−ω model for turbulent flows, coupled with the energy equation. Chemical non-equilibrium phenomena involving electronic energy relaxation as well as ionisation reactions have also been included. A gas mixture potentially composed of 11 species has been considered, namely oxygen and nitrogen derivatives. The electron number density is computed to evaluate the electron plasma and collision frequencies, essential parameters to characterise the electromagnetic interference caused by the plasma sheath. Among other results, high temperatures are reached at the nose region of the body in specific conditions of velocity and altitude, and non-negligible dissociation or ionisation of chemical species occurs. Plasma frequencies up to tens of GHz are reached in a region close to the body surface, while lower interference characterises the extended ionised wake.

Multiple Leap Maneuver Trajectory Design and Tracking Method Based on Prescribed Performance Control during the Gliding Phase of Vehicles

A novel standard trajectory design and tracking guidance used in the multiple active leap maneuver mode for hypersonic glide vehicles (HGVs) is proposed in this paper. First, the dynamic equation and multiconstraint model are first established in the flight path coordinate system. Second, the reference drag acceleration-normalized energy (D-e) profile of the multiple active leap maneuver mode is quickly determined by the Newton iterative algorithm with a single design parameter. The range to go error is corrected by the drag acceleration profile update algorithm, and the drag acceleration error of the gliding terminal is corrected by the aerodynamic parameter estimation algorithm. Then, the reference drag acceleration tracking guidance law is designed based on the prescribed performance control method. Finally, the CAV-L vehicle model is used for numerical simulation. The results show that the proposed method can satisfy the design requirements of drag acceleration under multiple active leap maneuver modes, and the reference drag acceleration can be tracked precisely. The adaptability and robustness of the proposed method are verified by the Monte Carlo simulations under various combined deviation conditions.

Investigation of Surface-Catalycity Effects on Hypersonic Glide Vehicle Trajectory Optimization

The optimization of a hypersonic glide vehicle’s reachable domain is studied using different models to constrain the stagnation-point heat transfer. The vehicle is modeled as a high-lift common aero vehicle that uses bank-angle modulation for crossrange maneuvering with a leading-edge thermal protection system made of an ultra-high-temperature ceramic. A constrained nonlinear optimization problem is formulated to determine the range of feasible trajectories that satisfy aerodynamic heating and loading constraints. The optimal control profile is determined using a modified particle swarm optimization routine that employs a penalty function approach to handle path and terminal constraints. Baseline trajectories are determined using an existing convective heat-flux correlation, and the effect of surface catalycity on the optimized trajectories is investigated by applying a correction factor, derived from high-fidelity computational fluid dynamics simulations, to the heat-flux correlation. Results demonstrate a high sensitivity of the optimized trajectories to the underlying aerothermodynamics model such that accounting for surface catalycity expands the reachability by an order of magnitude.