Cunyu Bao

Hypersonic morphing vehicles (HMVs), renowned for their adaptive structural reconfiguration and cross-domain maneuverability, confront formidable reentry guidance challenges under multiple no-fly zones, stringent path constraints, and nonlinear dynamics exacerbated by morphing-induced aerodynamic uncertainties. To address these issues, this study proposes a hierarchical framework integrating an A-based energy-optimal waypoint planner, a deep deterministic policy gradient (DDPG)-driven morphing policy network, and a quasi-equilibrium glide condition (QEGC) guidance law with continuous sliding mode control. The A* algorithm generates heuristic trajectories circumventing no-fly zones, reducing the evaluation function by 6.2% compared to greedy methods, while DDPG optimizes sweep angles to minimize velocity loss and terminal errors (0.09 km position, 0.01 m/s velocity). The QEGC law ensures robust longitudinal-lateral tracking via smooth hyperbolic tangent switching. Simulations demonstrate generalization across diverse targets (terminal errors < 0.24 km) and robustness under Monte Carlo deviations (0.263 ± 0.184 km range, −12.7 ± 42.93 m/s velocity). This work bridges global trajectory planning with real-time morphing adaptation, advancing intelligent HMV control. Future research will extend this framework to ascent/dive phases and optimize its computational efficiency for onboard deployment.

An event-triggered practical prescribed time control (PPTC) strategy is proposed for hypersonic morphing vehicles (HMVs) with system uncertainties and limited computation resources. HMVs can frequently change the aerodynamic profile to keep the desired aerodynamic properties. Attitude stabilization is essential for HMVs in continuous morphing stages. First of all, the HMV model is established and investigated to support the control system design. As a result, the complete morphing process will lead to significant improvements in aerodynamic coefficients, requiring fast-tracking and adaptation. Then, an appointed-time prescribed performance function (ATPPF) is proposed. Based on the ATPPF and the HMV model, the PPTC technique is induced for HMVs. Thirdly, the event-triggered PPTC is presented based on morphing information-driven (MI-driven) events, which are a part of the actual flight status. The developed MI-driven event-triggering mechanism could lead to a lower triggering number than the conventional time-triggering mechanism (TTM) and the commonly used relative-threshold event-triggering mechanism (RTETM). Finally, the prescribed time stability is theoretically proven via Lyapunov thesis while excluding the Zeno behavior. The simulations validated the effectiveness and the properties of the proposed control scheme for HMVs.

In this study, we present a sequential convex programming method for the reentry trajectory optimization problem with a piecewise constant bank angle profile constraint. Firstly, we design a piecewise constant bank angle profile that includes two free-time nodes to reserve maneuverability. Then, to handle the nondifferentiability of the profile at the piecewise points, the reentry problem is reformulated in a multiphase form so that the profile is differentiable in each phase. Finally, we convert the nonlinear multiphase problem into a sequence of convex subproblems by successive convexification. By iteratively solving these subproblems, a feasible solution to the original problem can be obtained. Numerical simulations demonstrate the effectiveness of our proposed method in short, medium, and long downrange reentry scenarios.

An onboard three-dimensional (3D) trajectory generation approach based on the reinforcement learning (RL) algorithm and deep neural network (DNN) is proposed for hypersonic vehicles in glide phase. Multiple trajectory samples are generated offline through the convex optimisation method. The deep learning (DL) is employed to pre-train the DNN for initialising the actor network and accelerating the RL process. Based on the offline deep policy deterministic actor-critic algorithm, a flight target-oriented reward function with path constraints is designed. The actor network is optimised by the end-to-end RL and policy gradients of the critic network until the reward function converges to the maximum. The actor network is considered as the onboard trajectory generator to compute optimal control values online based on the real-time motion states. The simulation results show that the single-step online planning time meets the real-time requirements of onboard trajectory generation. The significant improvement in terminal accuracy of the online trajectory and the better generalisation under biased initial states for hypersonic vehicles in glide phase is observed.

Aiming at the attitude control of a class of hypersonic morphing vehicles (HMVs) with variable sweep wings, a model-free adaptive dynamic planning (MFADP) optimal control method based on data-driven and finite-time fuzzy disturbance observer is proposed in this paper. An integrated oriented-control attitude-morphing model is established, and morphing is considered as the state to carry out the integrated coupling attitude control. The control scheme is organized by a steady-feedback-compensation framework. To overcome the dependence on the unknown dynamic knowledge of HMVs, the dynamic model is reconstructed using neural networks for steady-state control. Subsequently, based on the MFADP algorithm, an Off-On serial policy learning strategy is designed for the error model to obtain a real-time approximate optimal feedback control. Additionally, a fuzzy disturbance observer with finite-time convergence ability is proposed to estimate and compensate the multiple uncertainties. Finally, the stability of the closed-loop system is proved theoretically and the simulation results demonstrate the improved performance of the proposed method.

An autonomous optimal trajectory planning method based on the deep deterministic policy gradient (DDPG) algorithm of reinforcement learning (RL) for hypersonic vehicles (HV) is proposed in this paper. First, the trajectory planning problem is converted into a Markov Decision Process (MDP), and the amplitude of the bank angle is designated as the control input. The reward function of the MDP is set to minimize the trajectory terminal position errors with satisfying hard constraints. The deep neural network (DNN) is used to approximate the policy function and action-value function in the DDPG framework. The Actor network then computes the control input directly according to flight states. Using a limited exploration strategy, the optimal policy network would be considered fully trained when the reward value reached maximum convergence. Simulation results show that the policy network trained using a DDPG algorithm accomplishes 3-dimensional (3D) trajectory planning during the HV glide phase with high terminal precision and stable convergence. Additionally, the single step calculation time of the policy network occurs in near real time, which suggests great potential as an autonomous online trajectory planner. Monte Carlo experiments prove the strong robustness of the implementation of an autonomous trajectory planner under aerodynamic disturbances.

The morphing aircraft can improve the flight performance of hypersonic vehicles by satisfying the flight requirements of large airspace and large velocity field. In this paper, for the hypersonic variable span missile, the dynamic model and aerodynamic model are established on the variable span characteristic. The adaptive dynamic surface control back-stepping method is used to establish the integrated guidance and control (IGC) with terminal angular constraint in the dive phase of the hypersonic variable span missile. The span variety is used to assist the lift control to achieve fast and stable control for the centroid motion. The simulation results demonstrate that the feasibility and robustness of the IGC method of the hypersonic variable span missile is better than the invariable span.