To determine the optimal trajectory for the dive phase of a hypersonic missile, a maneuver strategy using inverted flight is presented. The combat scene that the hypersonic missile attack the fixed target on the ground is considered in this paper. Particularly, a maneuvering form named inverted flight is first applied to the hypersonic missiles. Afterwards, an optimal trajectory is designed by minimizing the attack time with a preset terminal flight path angle, where the constraints of angle of attack, dynamic pressure, heating transfer rate and normal overload are taken into account. In order to solve the designed trajectory optimization problem, an improved hp-adaptive pseudospectral method with mesh size reduction is presented. Simulation results show that the proposed algorithm can significantly reduce the mesh scale with a satisfactory accuracy and the trajectory obtained by the proposed algorithm is in accordance with the actual flight law. Furthermore, contrast simulation demonstrates that the inverted flight has better trajectory performance than normal flight.

A robust controller with an adaptive projection-based parameter estimator is presented for the air-breathing hypersonic vehicles. First, a novel parameter-varying model is established wherein the number of variable parameters is significantly reduced. In this way, the problem of overparameterization is avoided, and the controller design procedure is simplified. Then, the framework of the controller is established based on the backstepping approach, in which a second-order filter is introduced to solve the problem of “explosion of terms”. In order to enhance the robustness of the controller, an adaptive parameter estimator is designed. The Lipschitz continuous dead-zone modification is used to solve the problem of parameter draft in the conventional adaptive control and a sufficiently smooth projection operator is introduced to guarantee the uniform boundedness of adaptive parameters. The simulation results show that the proposed robust controller achieves the stable tracking of the reference commands under the parameter perturbation, and the variable parameters are limited within the preset range.

The design of an adaptive neural back-stepping control for a flexible air-breathing hypersonic vehicle (AHV) in the presence of input constraint and aerodynamic uncertainty is discussed. Based on functional decomposition, the dynamics can be decomposed into the velocity subsystem and the altitude subsystem. To guarantee the exploited controller’s robustness with respect to parametric uncertainties, neural network (NN) is applied to approximate the lumped uncertainty of each subsystem of AHV model. The exceptional contribution is that novel auxiliary systems are introduced to compensate both the tracking errors and desired control laws, based on which the explored controller can still provide effective tracking of velocity and altitude commands when the actuators are saturated. Finally, simulation studies are made to illustrate the effectiveness of the proposed control approach in spite of the flexible effects, system uncertainties, and varying disturbances.

An adaptive neural control scheme without backstepping is proposed for the air-breathing hypersonic vehicle subject to input constraints. To estimate the unknown nonlinearity of velocity subsystem and altitude subsystem, two radial basis function neural networks (RBFNNs) are constructed. Since the complex backstepping design steps are not needed, the proposed control structure is quite concise and the problem of “explosion of terms” is avoided. Moreover, a novel nonlinear auxiliary system is constructed to solve the problem of input constraints. The advantage of the proposed auxiliary system is that its high-order form has good performance and the parameter tuning is relatively easy. Simulation results show that the designed controllers achieve stable tracking of reference commands with good performance.

Traditional parameter estimation-based trajectory prediction algorithms for Reentry Glide Vehicles (RGVs) typically suffer from limitations, as they neglect the influence of state variables and rely heavily on high-dimensional motion information. To address these issues, this paper proposes an intelligent trajectory prediction algorithm for RGV via physics-informed constraints and state predictive control. First, based on linear system theory, we derived an analytical expression for the prediction error in parameter estimation methods and demonstrated the superiority of these methods through simulations. Second, the Transformer network based on parallel generative decoding pioneers a parameter estimation method of state predictive control, effectively enhancing the accuracy of medium-to-long-term trajectory prediction for RGVs and resolving the dependency of such methods on high-precision parameter estimation information. Finally, by incorporating physics-informed loss during network training based on the dynamic constraints between state variables and control inputs, the network is transformed into a knowledge-data dual-driven model. Simulation results demonstrate that compared to traditional parameter estimation methods, the proposed method exhibits higher prediction accuracy and improved robustness, offering significant engineering application value. Specifically, for prediction durations ranging from 50 to 200 s, this method keeps the average prediction error and the maximum prediction error within 1.4 km and 3.4 km, respectively.