Currently, countries around the world are generally unable to defend effectively against high-speed vehicles, and related basic research is still in its infancy. Accelerating the development of high-speed target defense technology is crucial for maintaining aerospace security.

Given the problems present in the near-space defense confrontation under this background, such as a narrow defensive posture, significant speed disadvantages, and limited single-missile defense capability, this paper reviews the current development status of the defense guidance law for high-speed near-space vehicles. It analyzes the deficiencies of the existing guidance law research from perspectives such as complex offensive and defensive confrontation scenarios, missile cooperative guidance mechanisms, and real environment effectiveness constraints.

It also foresees the key development directions of future defense guidance laws for high-speed vehicles, aiming to offer references for the construction of future defense systems for high-speed vehicles and the frontier basic research in the field of precision guidance.

Advanced and efficient aircraft typically refer to a category of vehicles that exhibit significantly enhanced flight performance, high operational efficiency, and environmental friendliness, aided by the latest technologies and design philosophies. Such aircraft integrate innovations from fields including advanced aerodynamic design, novel materials, and intelligent control systems, aiming to improve overall efficiency, safety, and sustainability. Among the various technical components, the control system serves as the central command unit. It is capable of regulating the aircraft’s attitude and trajectory in real time, ensuring stable and accurate tracking of desired paths even in complex environments, thus constituting an indispensable technological element of advanced efficient aircraft. Sophisticated control systems can optimize the control processes, leading to comprehensive improvements in energy consumption, flight efficiency, and safety.

This paper conducts a systematic investigation into four types of flight platforms: quadrotors, helicopters, fixed-wing aircraft, and high-speed vehicles. Each category operates in distinct flight environments and presents specific control requirements, necessitating the adoption of corresponding model predictive control strategies. Specifically, quadrotors face control challenges primarily due to strong system nonlinearities and external disturbances. Robust model predictive control can be employed to enhance disturbance rejection, while Lyapunov-based model predictive control helps ensure flight stability. Fixed-wing aircraft, often deployed in long-endurance missions, must cope with dynamically changing environmental conditions. Robust model predictive control is suitable for handling such perturbations, and explicit model predictive control can be applied to optimize flight trajectories. Helicopter systems encounter challenges related to multi-mode flight transitions. Switched model predictive control offers an effective approach to achieve smooth switching between different operational modes. High-speed vehicles operate in the most complex flight environments, where control involves multiple coupled factors such as trajectory planning, external disturbances, multi-mode switching, and stability assurance. Consequently, a combined design integrating various model predictive control methods is generally required.

Model predictive control and its applications in aircraft systems remain a prominent research focus. With the ongoing development of novel aircraft, research interest in model predictive control for flight systems is expected to persist, likely giving rise to further investigative topics. Currently, model predictive control techniques for low-speed aircraft are relatively mature. In contrast, model predictive control methods suitable for high-speed vehicles still require further research and development. High-speed vehicles represent a typical class of coupled hybrid systems, operating across flight regimes ranging from subsonic to supersonic speeds. They must transit smoothly through multiple flight phases—such as boost, acceleration, cruise, and re-entry—across a wide range of Mach. Throughout different mission stages, the engine operates in multiple thrust modes, inevitably involving discrete switching between operational states. Moreover, as the vehicle traverses varying Mach, the aerodynamic environment changes drastically. The interplay between aerodynamic characteristics and engine thrust becomes significant, making the coupling between aerodynamics and propulsion a critical factor that cannot be overlooked.

For high-speed vehicle control, a significant gap persists between model predictive control theory and practical implementation. Consequently, there is a pressing need to develop a unified model predictive control framework. This requires deeper integration of various algorithms—including robust model predictive control, Lyapunov-based model predictive control, switched model predictive control, and explicit model predictive control—to propose innovative solutions that bridge the gap between ideal performance targets and practically achievable limits. Research on control problems during transonic and supersonic flight of high-speed vehicles must address the following key scientific questions: how to construct a multi-physics coupled model that accurately represents cross-regime flight conditions; how to design a performance-guaranteed control strategy that ensures—in a computationally tractable form—the vehicle remains within a safe operational envelope while maintaining stable operation; and how to achieve safe switching between different flight modes, providing sufficient conditions for switching stability to guarantee recursive feasibility and stable convergence throughout the transition process. These areas constitute critical directions for future investigation.

Intelligent flow field modeling methods, by integrating the strengths of deep learning in feature extraction and dynamic response prediction with architectural innovations in MDO (multidisciplinary design optimization), have emerged as research hotspot for achieving efficient modeling of complex flow systems and enhancing high-dimensional performance. This fusion paradigm not only strengthens the coupling between data and physics but also provides aerodynamics design with computationally efficient and physics-consistent solutions through multi-objective optimization mechanisms. A new approach for the deep integration of data knowledge and physical mechanisms was provided, aiming to inspire interdisciplinary innovations in intelligent flow field modeling in aerospace and other fields.

In recent years, data-driven deep learning models have demonstrated revolutionary breakthroughs in flow field modeling. By leveraging end-to-end nonlinear mapping capabilities, these models transcend the limitations of traditional approaches reliant on manually defined variable sets and empirical closure models, significantly enhancing cross-configuration generalization performance under varying operational conditions. While pixel-based CNNs (convolutional neural networks) have been extensively applied to 2D flow field studies, their inability to capture geometric details severely compromises generalization performance when handling complex 2D boundary conditions or 3D flow fields. This limitation has been progressively addressed through point cloud networks (PointNet) and GNNs (graph neural networks), which effectively encode global geometric features in 3D flow modeling.

Intelligent flow prediction methodologies are evolving from purely data-driven paradigms toward physics-constrained hybrid frameworks. This paradigm shift not only improves prediction accuracy but also enhances model robustness and data efficiency, providing more reliable solutions for complex fluid dynamics challenges. PINNs (physics-informed neural networks) exemplify this trend by incorporating fundamental fluid mechanics laws into neural network loss functions through partial differential equation constraints, thereby strengthening physical consistency. Although showing promise across various physical modeling applications, PINN training still faces challenges from gradient discrepancies. Operator learning methods, particularly represented by DeepONet and FNOs (Fourier neural operators), have emerged as pivotal tools for learning mappings between infinite-dimensional function spaces to replace conventional numerical PDE solvers. These approaches effectively integrate the demonstrated strengths of data-driven models while overcoming input dimensionality constraints, positioning themselves as crucial enablers for future CFD (computational fluid dynamics) innovation.

Physics-constrained modeling significantly reduces dependence on large annotated datasets while improving adherence to physical principles, showing substantial potential for enhancing aircraft design efficiency from subsystem-level optimization to integrated system development. The convergence of deep learning with fundamental physics formulations continues to reshape the landscape of fluid dynamics simulation, offering new pathways for solving previously intractable engineering problems.

Intelligent flow field prediction demonstrates substantial application potential in MDO for aircraft, where efficient flow analysis enables accelerated design iteration cycles and enhanced overall design efficiency. Despite preliminary achievements in this domain, current research outcomes remain constrained by notable limitations in applicability, primarily stemming from challenges in acquiring high-fidelity datasets, effectively representing complex boundary geometries, and establishing robust physics-constrained frameworks. Addressing key technical bottlenecks—including improving shape generalization capability, prediction accuracy, and cross-physical-scenario adaptability—is critical for transitioning flow field modeling from theoretical research to practical engineering applications.

Current investigations must prioritize resolving fundamental challenges such as fidelity preservation in cross-scale parameter transfer and interpretability enhancement of intelligent algorithms. Physics-informed architectures demonstrate potential for reconciling data-driven predictions with fundamental conservation laws, thereby improving extrapolation capability in novel aerodynamic configurations. Continued progress in these areas could substantially advance the integration of intelligent flow prediction into MDO workflows, ultimately enabling more efficient exploration of complex design spaces under multiphysics constraints.