This paper takes autonomous exploration in unknown environments on a small co-axial twin-rotor unmanned aerial vehicle (UAV) platform as the task. The study of the fully autonomous positioning in unknown environments and navigation system without global navigation satellite system (GNSS) and other auxiliary positioning means is carried out. Algorithms that are based on the machine vision/proximity detection/inertial measurement unit, namely the combined navigation algorithm and indoor simultaneous location and mapping (SLAM) algorithm, are not only designed theoretically but also realized and verified in real surroundings. Additionally, obstacle detection, the decision-making of avoidance motion and motion planning methods such as Octree are also proposed, which are characterized by randomness and symmetry. The demonstration of the positioning and navigation system in the unknown environment and the verification of the indoor obstacle-avoidance flight were both completed through building an autonomous navigation and obstacle avoidance simulation system.

In order to meet the constraints of velocity and acceleration of displacement and attitude motion of an unmanned helicopter during an automatic carrier landing mission, an asymmetric tracking differentiator, which could set the speed and acceleration limits in two directions of tracked signal motion respectively, was derived based on the tracking differentiator in the active disturbance rejection control method. Based on the proposed asymmetric tracking differentiator, a fal-extended state observer based on the fal function was added to construct the attitude and angular velocity controller which is programmable during the transition process of unmanned helicopters. The mathematical simulation and result analysis show that the newly proposed attitude estimation algorithm effectively compensates for the deficiencies of the existing methods, improves the anti-jamming capability and the accuracy of attitude estimation in the maneuvering process, achieving the expected design purposes.

This paper mainly studies the longitudinal and lateral deck motion compensation technology. In order to ensure the safe landing of the carrier-based aircrafts on the flight decks of carriers during the landing process, it is necessary to introduce deck motion information into the guidance law information of the automatic landing guidance system when the aircraft is about to land so that the aircraft can track the deck motion. To compensate the influence of the height change in the ideal landing point on the landing process, the compensation effects of the deck motion compensators with different design parameters are verified by simulation. For further phase-lead compensation for the longitudinal automatic landing guidance system, a deck motion predictor is designed based on the particle filter optimal prediction theory and the AR model time series analysis method. Because the influence of up and down motions on the vertical motion of the ideal landing point is the largest, the compensation effects of the designed predictor and compensator are simulated and verified based on the up and down motion of the power spectrum. For the compensation for the lateral motion, a tracking strategy of the horizontal measurement axis of the inertial stability coordinate system to the horizontal axis of the hull coordinate system (center line of the deck) is proposed. The tracking effects of the horizontal measurement axis of the designed integral and inertial tracking strategies are simulated and compared. Secondly, the lateral deck motion compensation commands are designed, and the compensation effects of different forms of compensation commands are verified by simulations. Finally, the compensation effects for the lateral deck motion under integral and inertial tracking strategies are simulated and analyzed.

This paper introduces an innovative approach to optimizing flight corridors under complex constraints, particularly focusing on the Terminal Area Energy Management (TAEM) phases of reusable vehicles, where nominal trajectories may be inadequate due to initial condition and aerodynamic deviations. Leveraging the elegant principles of symmetry, the proposed optimal flight corridor design method, based on the Lagrange multiplier technique, offers a harmonious balance between trajectory accuracy and adaptability. By describing the TAEM flight corridor through a range–altitude profile and utilizing iterative optimization to uphold physical constraints such as dynamic pressure, overload, and roll angle, this method ensures symmetrical alignment of the design parameters. Through a comprehensive analysis of aerodynamic and initial position uncertainties, this method showcases exceptional symmetry in adapting to trajectory design uncertainties. The simulation results demonstrate the resilient nature of the designed flight corridor, capable of seamlessly accommodating initial state deviations and aerodynamic uncertainties. This symmetrical optimization of flight corridors not only enhances trajectory planning and control capabilities during the terminal energy management phase, but also showcases a paradigm shift towards precision and balance in aerospace engineering. Our simulation findings underscore the efficiency of this approach by reducing the flight corridor range by 50% compared to the nominal state while maintaining robustness across deviation conditions, embodying the symmetrical resilience needed for optimal trajectory design.

During the early developmental stages of solid rocket motors (SRMs), particularly under extreme operating conditions, the scarcity of experimental data severely limits the predictive capabilities of conventional deep learning models. To address this challenge, this paper proposes a novel hybrid predictive framework, termed Shape-Regularized Meta-Variational Multi-Scale Network (SR-MVSNet), tailored for few-shot thrust prediction via shape-regularized meta-learning. First, a variational autoencoder (VAE) maps nine static design and operating parameters to a latent probability distribution and reconstructs the parameter vector. A parallel multi-scale convolutional neural network (MSCNN) then processes the reconstructed features to predict the complete thrust curve. Crucially, a joint loss function with shape regularization is integrated within the model-agnostic meta-learning (MAML) architecture, guiding the network to reproduce the measured thrust build-up and decay through supervised first- and second-order difference matching. Experimental results demonstrate that the proposed framework achieves the lowest mean squared error among the evaluated models in the low-temperature-to-ambient-temperature transfer task. Notably, during the critical steady-state combustion phase, the mean absolute percentage error is 1.71% under the combined-source task, supporting accurate steady-state thrust prediction for the rapid performance evaluation of SRMs in engineering applications.

To address issues such as nonlinear strong coupling, time-varying parameters, and external wind disturbances in fixed-wing unmanned aerial vehicles (UAVs) operating in complex flight environments, this study develops a composite attitude control method integrating L₁ adaptive control with an extended state observer (ESO). First, pitch and roll attitude dynamic models considering nonlinear aerodynamic characteristics and channel coupling are established. Based on these models, a composite control architecture is developed in which the L₁ adaptive controller establishes the baseline prediction-error-driven command-tracking loop and introduces matched-uncertainty compensation through a low-pass-filtered adaptive channel, whereas the ESO estimates a generalized extended state and, after removal of the known nominal drift term, provides an independently scaled auxiliary feedforward correction based on the resulting lumped-disturbance estimate. Although the uncertainty contents observed by the two mechanisms may partially overlap, their control actions are coordinated through the residual closed-loop disturbance and the prediction-error-driven adaptation process rather than being independently superimposed at full amplitude. Since the L₁ adaptive law remains driven by the residual state-prediction error after the ESO action, the two mechanisms dynamically redistribute, rather than simply duplicate, the compensation effort. The control performance of the proportional–integral–derivative(PID) controller, the conventional L₁ adaptive controller, and the proposed method is comparatively evaluated through simulations under typical operating conditions, including step response, sinusoidal tracking, composite wind disturbances, and measurement noise. The results show improved transient response and disturbance/noise rejection relative to PID and conventional L₁ control under most of the tested conditions, while the high-frequency tracking benefit is channel-dependent. Overall, the proposed method improves transient response and disturbance/noise rejection while maintaining bounded tracking performance under the stated assumptions. The proposed method provides an effective approach for improving the attitude control performance of fixed-wing UAVs operating in complex environments.