This paper investigates the design problem of the attitude controller for air-breathing supersonic vehicle subject to uncertainties and disturbances. Firstly, the longitudinal model is established for the attitude controller design which is devised as a strict feedback formulation, and a transformed tracking error is derived with the prescribed performance control technique such that it can limit the tracking error to a predefined region. Then, a novel linear active disturbance rejection control scheme is proposed for the attitude system to enhance the steady-state and transient-state performances by incorporating the transformed tracking error. On the basis of the Lyapunov stability theorem, the convergence and stability characteristics are both rigorously proved for the closed-loop system. Finally, extensive contrast simulations are conducted to demonstrate the effectiveness, robustness, and advantage of the proposed control strategy.

Precise trajectory planning is crucial in terms of enabling unmanned aerial vehicles (UAVs) to execute interference avoidance and target capture actions in unknown environments and when facing intermittent target loss. To address the trajectory planning problem of UAVs in such conditions, this paper proposes a UAV trajectory planning system that includes a predictor and a planner. Specifically, the system employs a bidirectional Temporal Convolutional Network (TCN) and Gated Recurrent Unit (GRU) network algorithm with an adaptive attention mechanism (BITCN-BIGRU-AAM) to train a model that incorporates the historical motion trajectory features of the target and motion intention the inferred by a Dynamic Bayesian Network (DBN). The resulting predictions of the maneuvering target are used as terminal inputs for the planner. An improved Radial Basis Function (RBF) network is utilized in combination with an offline–online trajectory planning method for real-time obstacle avoidance trajectory planning. Additionally, considering future practical applications, the predictor and planner adopt a parallel optimization and correction algorithm structure to ensure planning accuracy and computational efficiency. Simulation results indicate that the proposed method can accurately avoid dynamic interference and precisely capture the target during tasks involving dynamic interference in unknown environments and when facing intermittent target loss, while also meeting system computational capacity requirements.

A novel three-dimensional integrated guidance and control (3D-IGC) approach is developed for sliding-to-turn (STT) missile, which achieves an asymptotic tracking performance with various disturbances. First, a nonlinear state-space model is established to design the 3D-IGC law. Then a control approach based on improved robust integral of the sign of error (RISE) is proposed to guarantee both the angle and angular rates of look of sight (LOS) to be tightly tracked. In addition, a novel error transformation is introduced so as to transmit the mismatched modeling uncertainties to control input channel. Then the improved RISE can handle both mismatched and matched modeling uncertainties together in the design of controller. The closed-loop system stability and the convergence characteristic are rigorously proved via Lyapunov theory. Finally, extensive contrast simulations are implemented to demonstrate the effectiveness and robustness of the proposed control strategy.

Under stringent flight constraint conditions, trajectory optimization of Hypersonic Vehicles (HV) is crucial for guidance. Therefore, this paper proposes an hp-adaptive dynamic trust region SCP method to address the problems of initial value sensitivity and low computational efficiency that arise in traditional Sequential Convex Programming (SCP) methods during HV trajectory optimization. First, the discrete symmetry principle is utilized to simplify the boundary conditions and constraint handling of the trajectory model while combining scale transformation invariance to achieve nondimensionalization of the model’s physical quantities, thereby constructing the trajectory optimization model. On this basis, the hp-adaptive method is adopted to dynamically adjust the discretization step size and polynomial approximation order, and combined with an adaptive adjustment strategy for the trust region radius, to improve computational efficiency while ensuring optimization accuracy. Finally, the effectiveness of the algorithm is validated through optimizing the gliding phase of HV, and compared with GPOPS-II and fixed trust region SCP methods. The experimental results show that the algorithm has superiority in improving convergence speed, computational efficiency and solution accuracy, with its performance significantly outperforming traditional trajectory optimization schemes.

Optimization based on conventional neural networks can only guarantee calculation speed. However, the accuracy of network prediction is also a key index in online trajectory optimization. This paper proposes a novel online trajectory optimization approach based on an Improved Radial Basis Function Neural Network (IRBFNN). First, multiple optimal trajectories for different variations of Air-Breathing Supersonic Vehicles (ASVs) are generated, considering attitude, and collected as the dataset. Then, an IRBFNN is trained to predict trajectories online with the above dataset, in which an enhanced loss function is introduced to improve prediction accuracy. Finally, numerical simulations are presented to demonstrate the feasibility and superiority of the proposed online trajectory optimization method. The open-source code of the proposed method is shared in the form of a link to showcase its implementation.