Wei Han

Automatic carrier landing of fixed-wing UAVs remains challenging under deck motion, carrier airwake, gusts, and actuator faults. This paper proposes a robust flexible predefined-time prescribed performance control (RFPTPPC) framework with beneficial disturbance utilization (BDU). A control-oriented six-degree-of-freedom cascaded model with direct lift control is first established. To address the temporary infeasibility of fixed predefined-time PPC boundaries, direction-selective flexible boundaries restore the admissible attitude error region when the nominal envelope is threatened, while a smoothly coordinated recovery branch drives the error inward. An adaptive super-twisting extended state observer (ASTESO) reconstructs lumped disturbances in the cascaded loops. Using the ASTESO outputs, BDU evaluates each disturbance component, retains those that favor error convergence, and compensates for adverse components, thereby improving attitude tracking accuracy and maintaining PPC feasibility. Lyapunov analysis establishes practical predefined-time stability of the closed-loop system. Comparative simulations demonstrate improved trajectory tracking, attitude regulation, actuator coordination, and robustness under randomized landing conditions.

To address ambiguous maneuver boundaries, background interference, and uneven multi-sensor quality in long-duration flight parameter recordings, this paper proposes an adaptive flight maneuver boundary localization algorithm that integrates spectral entropy-weighted multi-channel spectrogram fusion with attitude-constrained structural correction. Multi-channel Short-Time Fourier Transform (STFT) spectrograms are first constructed from flight parameter time series. Spectral entropy (SE) is introduced to quantify the uncertainty of each channel’s time–frequency energy distribution and is combined with the maneuver activation ratio (MAR) and the linear contrast ratio (LCR) to form objective credibility weights, thereby suppressing channels dominated by aerodynamic turbulence and high frequency structural vibration. Normal overload soft gating and logarithmic noise floor subtraction are then applied to obtain an enhanced fused spectrogram, from which candidate intervals are extracted by low band energy thresholding. Finally, roll and pitch angle steady-state priors refine the event structure through local boundary refinement, cross-segment expansion/chain merging, and semantic post-processing, recovering continuous maneuvers fragmented by instantaneous energy valleys. On the held-out test sorties (SE_018–SE_020; 61 annotated intervals), the proposed algorithm achieves Precision, Recall, and F1-scores of 0.967. On the full primary corpus of 20 sorties (461 intervals), used for ablation and sensitivity analyses, the corresponding figures are Precision 0.934, Recall 0.959, and F1 0.946, with start and end boundary mean absolute errors of 1.484 s and 1.471 s. Under the same IoU protocol, consistent superiority is observed against learning-based baselines, and an independent external set of 10 sorties yields F1 = 0.938. The results indicate that entropy-constrained multi-sensor time–frequency fusion mainly improves maneuver/background separability, whereas attitude-constrained structural correction restores the integrity of long continuous maneuvers.

Carrier-based fixed-wing UAV landing is challenged by deck motion, carrier airwake, gust disturbances, strong trajectory–attitude coupling, and actuator constraints. To address these issues, this paper proposes a Predefined-Time Direct Lift/Side-Force-Integrated Approach Landing (PTDIAL) method. An integrated direct-force architecture is constructed using the trailing-edge flap for direct lift and the spoiler for direct side force, thereby reducing the dependence of trajectory correction on angle-of-attack- and bank-angle/sideslip-mediated regulation. A preview-based reference glide slope is generated from the predicted Ideal Touch Point (ITP) sequence to improve the response to deck motion. Predefined-time control laws are developed for the cascaded position, trajectory, attitude, angular rate, and velocity loops, with prescribed-performance constraints imposed on the attitude response. A predefined-time disturbance observer is introduced to estimate the lumped aerodynamic disturbances, while an auxiliary anti-saturation mechanism compensates for the effect of trailing-edge flap saturation. Lyapunov analysis establishes the practical predefined-time stability of the closed-loop system under bounded disturbances and actuator constraints. Various simulations demonstrate that the proposed architecture improves lateral and vertical tracking while preserving the UAV attitude, and Monte Carlo simulations further confirm the robustness of PTDIAL.

To address the challenges of generating autonomous, high-quality control laws for high-performance Unmanned Aerial Vehicles (UAVs) performing long-horizon complex aerobatic maneuvers, specifically the difficulty of achieving energy-altitude closure, low-level control chattering, and low utilization of high-quality experience, this paper proposes an improved Soft Actor-Critic (SAC) algorithm incorporating a Quality-Aware Expert Pool (QAEP). Using the aerobatic loop maneuver as a representative scenario, this study explores the autonomous generation of control-surface manipulation policies comparable to those of skilled human pilots for agile fixed-wing UAVs. First, a singularity-free feature state representation and a rate-integrated action space are constructed. Combined with a symmetric shaping reward, these suppress control chattering at the physical level and achieve energy-altitude closure throughout the maneuver. Second, a dual-threshold expert pool driven by task reward and trajectory quality, together with a progressive mixed-sampling mechanism, is designed to effectively filter out low-quality samples and improve algorithmic convergence stability. Simulation experiments based on JSBSim with a high-fidelity F-16 model, which serves as a representative surrogate for a high-performance UAV, demonstrate that the proposed method generates maneuver strategies with manipulation quality comparable to that of skilled human pilots. The Dynamic Time Warping (DTW) similarity between the generated control commands and human expert demonstration data exceeds 0.97, the Manipulation Smoothness Index (MSI) is improved by 7.3%, and the loop completion rate under randomized initial conditions reaches 96.2%. These results suggest that the proposed framework enables human-like energy coordination and fine-grained control sequence generation in complex simulation environments, offering a promising approach to advancing maneuver intelligence and autonomous control capability in UAV systems.

Optimizing naval aircraft deck sortie scheduling is important for improving the sortie efficiency of naval aircraft groups. It is also an important link in realizing the automated scheduling of aircraft carriers. Firstly, a single-aircraft surface transit path library was constructed. This paper used the improved A* algorithm and the optimal control deck path planning algorithm to construct the transit path library between the deck parking position and take-off position, or between warmable and non-warmable parking positions. Secondly, a mathematical optimization model was constructed for the sortie scheduling of a mixed fleet of fixed-wing carrier aircraft and carrier helicopters. The model took the maximum sortie time of the fleet and taxiing time of the fleet of fixed-wing aircraft as the optimization objectives, and considered the process flow, spatial, and resource constraints. Thirdly, a discretized improved whale optimization algorithm (IWOA) was designed to solve the problem. To improve the optimization-seeking capability of the algorithm, we discretized the algorithm, optimized its coding method, and introduced pre-constraints, learning factors, parameter improvements, and population restart. Finally, we set up various cases and proposed a new strategy for simulation experiments: these simulations verified the validity of the model, the excellent optimization performance of the IWOA, and the superiority of the sortie strategy. The research in this study will help to implement automated aircraft carrier scheduling.

The scheduling problems in mass production, manufacturing, assembly, synthesis, and transportation, as well as internet services, can partly be attributed to a hybrid flow-shop scheduling problem (HFSP). To solve the problem, a reinforcement learning (RL) method for HFSP is studied for the first time in this paper. HFSP is described and attributed to the Markov Decision Processes (MDP), for which the special states, actions, and reward function are designed. On this basis, the MDP framework is established. The Boltzmann exploration policy is adopted to trade-off the exploration and exploitation during choosing action in RL. Compared with the first-come-first-serve strategy that is frequently adopted when coding in most of the traditional intelligent algorithms, the rule in the RL method is first-come-first-choice, which is more conducive to achieving the global optimal solution. For validation, the RL method is utilized for scheduling in a metal processing workshop of an automobile engine factory. Then, the method is applied to the sortie scheduling of carrier aircraft in continuous dispatch. The results demonstrate that the machining and support scheduling obtained by this RL method are reasonable in result quality, real-time performance and complexity, indicating that this RL method is practical for HFSP.