The multi-body dynamics in the launch process of a space platform deploying a server, as well as the optimal double impulse rendezvous guidance law between the server and the target spacecraft, are studied. Firstly, the space platform enters into orbit around the target, keeping its launch tube axis aiming at it. After receiving the launch command, the server shoots out from the launch tube, flying to the target. Due to body coupling, the platform's attitude is disturbed, preventing the server from accurately aiming at the target during separation. The server uses its small rocket engine to apply two velocity pulses: the first one to adjust its trajectory for rendezvous, and the second near the target to reduce relative velocity to zero for soft docking. A two-body dynamics model is established using the Newton-Euler method, and a virtual prototype is developed in ADAMS for validation. To solve the multi-objective optimization subject to energy consumption and flight time for rendezvous, an improved non-dominated sorting genetic algorithm II (NSGA-II) algorithm is proposed. Simulation results show that launch-induced perturbations are non-negligible, and the proposed algorithm effectively derives the optimal guidance law that balances energy use and flight time.

This paper investigates event-triggered predefined-time (PT) consensus control for leader-follower multi-agent systems subject to input saturation and prescribed performance constraints in the presence of unknown disturbances. Under switching topologies, the proposed protocol guarantees consensus of all agents within a user-defined time while ensuring the tracking error satisfies prescribed-performance constraints. A PT auxiliary signal is incorporated to mitigate input saturation, enabling adaptive compensation for saturation errors. Besides, a distributed estimator is developed for robust coordination, reconstructing the required tracking errors for each agent even under incomplete leader-follower communication. Furthermore, adaptive control is developed to approximate the unknown disturbance and a dynamic event-triggered mechanism is deployed to reduce controller updates. Finally, simulations are conducted to validate the feasibility and superiority of the proposed method.

This article proposes an attitude tracking control scheme for fixed-wing uncrewed aerial vehicles with state, input amplitude and rate constraints, which is able to guarantee the prescribed-time prescribed performance. Firstly, a novel modified prescribed-time performance function is proposed to balance input and performance constraints, with its boundary adaptively adjusted according to the input saturation errors. Secondly, considering the input saturation that occurs during actual flight, an error transformation function and a hyperbolic tangent function are employed to address input amplitude and rate saturation, which prevents abrupt variations in input amplitude. Thirdly, a parameter adaptive estimation method is adopted to approximate the upper bound of external disturbances. On this basis, a back-stepping control method is investigated to guarantee that tracking errors meet the performance boundary within prescribed time under multiple constraints. Comparative simulations illustrate the effectiveness and merits of the proposed control scheme. Note to Practitioners-Due to physical limitations, fixed-wing uncrewed aerial vehicles are often subject to actuator input saturation. In this paper, both input amplitude and rate saturation are considered, thereby avoiding abrupt variations in control input amplitude. Moreover, in many practical flight missions, uncrewed aerial vehicles are required to track reference trajectories within prescribed time while satisfying transient and steady-state performance constraints. To address this, a novel modified prescribed-time performance function is proposed to achieve a trade-off between input and performance constraints. A prescribed-time-performance-based back-stepping control method is proposed, which achieves improved transient and steady-state performance in trajectory tracking. This approach offers a feasible strategy for flight applications.

In low-altitude urban intelligent transportation systems, efficient cooperative task allocation and path planning for multiple unmanned aerial vehicles (UAV) are critical for ensuring the effective execution of complex tasks. This paper proposes a distributed decision-making and autonomous planning framework to achieve cooperative task allocation and path planning for multi-UAVs in low-altitude urban traffic environment. The mission requirements of task allocation and path planning are modeled using evolutionary potential games and show that there exists a Nash equilibrium for the proposed potential function. An Improved Log-linear Learning Algorithm (ILLA) is proposed, and suitable Boltzmann parameters are derived which will enable the proposed ILLA to converge to the optimal Nash equilibrium with a probability one. Furthermore, a Constraint-Based Multi-layer Bidirectional Adaptive A-Star (CBMBA A-Star) algorithm is designed to find optimal and collision free paths for each UAV. Compared with the baseline method, simulation results demonstrate that the proposed approach improves the task reward by 11.67%, reduces the task execution time by 37.41%, and decreases run time by 61.02%, confirming its effectiveness and efficiency in the complex low-altitude urban traffic scenario.

Autonomous combat unmanned aerial vehicle (UAV) systems represent a critical area of research for global military powers. This study investigates one-on-one close-range air combat scenarios involving UAVs and proposes an autonomous maneuvering decision-making model based on deep reinforcement learning (DRL). A six-degree-of-freedom continuous action space is established to simulate UAV combat environments realistically. In the proposed decision-making model, a global reward function is primarily designed based on the combat outcome and a guidance reward that incorporates four key tactical dimensions: attack angle, distance, velocity, and altitude. In addition, we design a value-based prioritized experience replay (PER) mechanism to improve sample efficiency by adaptively balancing old and new experiences, thereby accelerating convergence. Finally, a three-dimensional air combat simulation environment is developed. Experimental results demonstrate that the proposed model achieves strong convergence and practical effectiveness in autonomous air combat decision-making, significantly outperforming baseline methods in terms of tactical adaptability and mission success rates.

This article investigates the model-free adaptive control scheme under the background of the high-order fully actuated system. The general nonlinear non-affine dynamics are converted into the fully actuated subsystem form, and the uncertainties are estimated by the iterative parameter updating law and the fuzzy-logic system. Using the invertible properties of the input parameter matrix in the fully actuated subsystem, the fully actuated model-free adaptive control framework is developed, and the closed-loop performance is verified both by theoretical analysis with the aid of the contract mapping principle and simulation results in the condition of a type of coaxial dual-rotor unmanned autonomous vehicle dynamics.

With the rapid advancement of intelligent and clustered unmanned technologies, the autonomous decision-making and confrontation of multiple unmanned aerial vehicles (UAVs) has emerged as a prominent research focus among major military powers worldwide. Multi-UAV confrontation environments are characterized by high-dimensional action spaces, nonlinearity, and stringent real-time decision-making requirements, which pose significant challenges to existing decision-making algorithms. Therefore, this paper addresses the problem of the real-time maneuvering decision-making problem of multiple UAVs in the context of 2v2 close-range air combat. Firstly, a multi-UAV confrontation simulation environment based on the agent-environment cyclic (AEC) game model is developed to resolve issues of ambiguous reward allocation and dynamic variations in the number of intelligent agents. Secondly, a multi-agent soft actor-critic deep reinforcement learning method is proposed within a centralized training-distributed execution (CTDE) framework, supplemented by a strategy training and optimization approach incorporating curriculum learning. Furthermore, by integrating mainline and process rewards, collaborative rewards are introduced to strengthen tactical coordination among UAVs and enhance the effectiveness of adversarial strategies. Finally, three-dimensional simulation experiments validate the effectiveness and stability of the proposed method.

As an actuation mechanism for achieving precision attitude control in aircraft, the electromechanical actuator (EMA) plays a critical role in ensuring flight safety and stability. However, the EMA is subject to unmeasurable unknown disturbances that act through mismatched channels relative to the system's control input. To address this, this paper employs feedback linearization to transform the existing model. The transformed model effectively recasts the unknown disturbance into the same channel as the control input, thereby enabling active disturbance rejection via control law design. Furthermore, to overcome the challenge of immeasurable disturbances, an extended state observer (ESO) is designed to estimate the unknown disturbance; the estimated value is then directly utilized in the control law synthesis. Subsequently, a fuzzy logic system (FLS) is developed to perform real-time online adaptation and optimization of the controller parameters. Finally, extensive simulation results are provided to validate the effectiveness of the proposed algorithm.

In this paper, a fixed-time switching tracking control scheme based on the fixed-time disturbance observer (FTDO) is proposed for a 6-DOF unmanned helicopter (UH) with multiple constraints and composite disturbances. The developed fixed-time controller guarantees that the system tracks the desired trajectory within a certain time, regardless of initial conditions. The multiple constraints include input saturation and time-varying output constraints. An improved fixed-time auxiliary system is applied to compensate for the effects of input saturation nonlinearity. By developing a novel switching boundary protection algorithm, a switching control scheme is further designed to solve the output constraints better. An improved FTDO is developed to estimate composite disturbances containing saturation function approximation errors and external disturbances. On this basis, a fixed-time switching back-stepping control method is employed for the position and attitude loops, which enables the UH to track the desired trajectory within the flight path constraints. The experimental results verify the effectiveness of the proposed scheme.

The paper devotes to studying the formation tracking control of the UAV cluster system when external disturbance is present, which uses switching topology to depict the communication connectivity among each UAV. The leader-follower approach is introduced to tackle the formation issue. The controller is designed by combining the disturbance observer and the consistency theorem, and then the formation tracking algorithm is constructed, the controller's parameters are acquired through solving the linear matrix inequality. Besides, the segmented Lyapunov function is constructed to complete the stability proof part of the theoretical derivation. Then numerical simulation experiments based on Matlab are carried out, taking a cluster system of 6 UAVs as an example, the anti-disturbance performance and stability of the system are investigated under two situations: without adding a disturbance observer and with adding a disturbance observer. The number of UAVs is augmented to 11 for the purpose of evaluating the scalability.

The coordinated operations of Stealth Unmanned Aerial Vehicle (SUAV) and Swarming Drones (SD) have demonstrated formidable power in the military domain. Efficient path planning is a critical technology that enhances combat effectiveness. This paper proposes a game-theoretic optimization approach to achieve cooperative penetration and target search path planning for swarm UAVs. Multi-task and multi-objective optimization models are developed for complex scenarios whose optimal solutions are NP-hard. Consequently, SUAV and SD are defined as leader and followers, respectively. The formulated Stackelberg game model enables distributed intelligent decision-making for SUAV and SD. We theoretically prove that by selecting an appropriate potential function, subgames within the leader-level and followers-level become an Ordinal Potential Game (OPG) with a Nash equilibrium, thereby ensuring the existence of a Stackelberg Equilibrium (SE) through leader-follower interactions. We propose a Gradient-based Hierarchical Learning and Optimization Algorithm (GHLOA) to achieve SE. At the leader-level, a Stochastic Gradient Ascent (SGA) algorithm optimizes the penetration path for SUAV, while in the followers-level, we demonstrate that the designed Hybrid Learning-based Multimodal Adaptive Pigeon-Inspired Optimization (HLMAPIO) algorithm converges with probability one to the suboptimal solution for each SD. Numerical results indicate that our approach is suboptimal, scalable, and fast adaptable to dynamical scenarios, and it outperforms the state-of-the-art techniques. Note to Practitioners-With the rapid advancement of artificial intelligence technologies, the coordinated operations of stealth unmanned aerial vehicle (SUAV) and swarming drones (SD) have demonstrated immense potential in modern high-tech warfare. This paper investigates a heterogeneous UAV swarm system comprising a single SUAV and several, or even dozens of, SDs deployed in complex, dynamic scenarios with moving threats during the execution of diverse tasks. Our core concept is to propose a game-theoretic swarm intelligence decision-making framework that optimizes the rewards and costs of swarm members. This approach effectively mitigates potential conflicts, ensures suboptimal and collision-free paths, and enhances the scalability, adaptability, and robustness of the swarm system in dynamic environments. Notably, our method theoretically guarantees system stability and solution suboptimality. Numerical results show that the proposed approach significantly improves the swarm system's overall utility while ensuring the safety of individual UAVs. Even when the number of UAVs and targets increase by a factor of five, the computational time remains within an acceptable range. Future work will focus on integrating game theory with reinforcement learning to explore decision-making and autonomous planning technologies for multi-objective and multi-task scenarios across larger-scale scenarios.

Path planning is both a substantial issue and an essential component of intelligent decision-making technology in uncrewed autonomous systems. This article investigates a real-time path planning algorithm for autonomous uncrewed aerial vehicles (UAVs). A cooperative path planning model is proposed that accounts for radar threats, dynamic targets, UAV collaboration, and complex constraints. Then, an event-triggered multimodal adaptive pigeon-inspired optimization (ET-MAPIO) algorithm is proposed. Specifically, a multimodal state update system and adaptive inertia weights are introduced to overcome the issues of local optima and sluggish convergence in existing bioinspired optimization methods. Furthermore, an event-triggered mechanism is developed to facilitate rapid and efficient path replanning in the presence of moving targets. Finally, simulation results demonstrate the optimality, real-time performance, and efficiency of the ET-MAPIO algorithm. Our approach is scalable in larger scale scenarios and outperforms the state-of-the-art technologies.

This study is concerned with the modeling and control for the conversion mode of tiltrotor aircraft. A switched T-S fuzzy model with an exosystem is adopted to approximate the longitudinal dynamics of the conversion mode, which not only guarantees the accuracy of the model but also simplifies the control design. On this basis, the almost output regulation scheme is designed to deal with the conversion mode control issue. Moreover, the bumpless transfer technique is employed to suppress the bump phenomenon at switching instants, thereby facilitating a more smooth tilting process. Finally, the effectiveness of the proposed scheme is validated by the simulation results with regard to XV-15.

This paper investigates a path planning approach for cooperative unmanned aerial vehicle (UAV) and unmanned ground vehicle (UGV) systems. Considering the position error corrections (PEC) of UAV, threats, and other complex constraints, a UAV-UGV cooperative path planning model is developed to minimize the total cost. Then, a Hybrid Ant-Pigeon Cooperative Optimization (HAPCO) algorithm is presented to address issues in existing bio-inspired optimization algorithms. A new pheromone update strategy and adaptive inertia weight are designed to optimise the performance. the performance of the Ant Colony Optimization (ACO) algorithm and Pigeon-inspired Optimization (PIO) algorithm. Moreover, the two algorithms are integrated to perform a full-cycle search. Simulation results reveal that the HAPCO algorithm outperforms ACO and PIO regarding path quality, cooperation cost, convergence, and computational efficiency, further demonstrated the validity and superiority of our algorithm.

Task allocation is a key aspect of Unmanned Aerial Vehicle (UAV) swarm collaborative operations. With an continuous increase of UAVs’ scale and the complexity and uncertainty of tasks, existing methods have poor performance in computing efficiency, robustness, and real-time allocation, and there is a lack of theoretical analysis on the convergence and optimality of the solution. This paper presents a novel intelligent framework for distributed decision-making based on the evolutionary game theory to address task allocation for a UAV swarm system in uncertain scenarios. A task allocation model is designed with the local utility of an individual and the global utility of the system. Then, the paper analytically derives a potential function in the networked evolutionary potential game and proves that the optimal solution of the task allocation problem is a pure strategy Nash equilibrium of a finite strategy game. Additionally, a PayOff-based Time-Variant Log-linear Learning Algorithm (POTVLLA) is proposed, which includes a novel learning strategy based on payoffs for an individual and a time-dependent Boltzmann parameter. The former aims to reduce the system’s computational burden and enhance the individual’s effectiveness, while the latter can ensure that the POTVLLA converges to the optimal Nash equilibrium with a probability of one. Numerical simulation results show that the approach is optimal, robust, scalable, and fast adaptable to environmental changes, even in some realistic situations where some UAVs or tasks are likely to be lost and increased, further validating the effectiveness and superiority of the proposed framework and algorithm.

In this paper, the integral event-triggered time-varying formation tracking (TVFT) problem is investigated for unmanned aerial vehicle (UAV) swarm systems with switching directed topologies. An improved integral event-triggered mechanism (ETM) is introduced into the TVFT control protocol, which can further reduce communication frequency and resource consumption compared with traditional static ETM. The switching topology is considered to improve the reliability of the communication network. Different from traditional TVFT, the addition of the switching signal and the ETM brings challenges to the system analysis and design. To this end, the control protocol of TVFT, the admissible edge-dependent average dwell time switching signal and the ETM are co-designed, the global exponential stability criterion is further deduced. The simulation verifies the effectiveness and merits of the design scheme.

Aero-engine is an important component of aviation equipment. Due to the special working environment and the poor temporality of fault diagnosis, it is difficult for aircraft maintenance personnel to make accurate fault diagnosis of aero-engine. To address this problem, this paper proposes an aero-engine fault diagnosis method based on an improved snake optimization algorithm (ISO) and a bidirectional long and short-term memory network (BiLSTM). The idea of balanced pool of EO algorithm is introduced in the snake optimization algorithm to build an elite pool to compensate for the low accuracy of calculation caused by randomly selected individuals, while the Lévy flight perturbation mechanism is introduced to enhance the SO’s performance of finding the best; the optimized parameters are substituted into the BiLSTM to reconstruct the model, train the fault data for prediction and output the results. In the simulation experiments, five failure modes are diagnosed with real monitoring data of a certain type of aero-engine and compared with the BiLSTM and SO-BiLSTM fault diagnosis models. The experimental results show that the ISO-BiLSTM fault diagnosis method proposed in this paper has better fault diagnosis effect compared with BiLSTM and SO-BiLSTM, and provides a new idea for aero-engine fault diagnosis.

This paper presents a collaborative target search method based on Improved Wolf Pack Algorithm (IWPA) for resolving the challenge of UAV swarm target search in an unknown 3D environment. The proposed approach enables UAVs to locate targets by maximizing the intensity of target signals, which can be classified into two modes: roaming search state and collaborative search state. In the collaborative search state, adaptive parameter adjustment and differential evolution techniques are utilized to overcome the limitations of the classical Wolf Pack Algorithm (WPA), such as fixed step size and local optima. Results from simulations demonstrate that IWPA significantly enhances the efficiency of target search tasks.