The unexpected centroid shift of an aircraft can alter model parameters by introducing additional moments that degrade controller performance. This can lead to failed command tracking or flight accidents. To address these challenges, in this study, an L1 adaptive robust control strategy is proposed based on nonlinear dynamic inversion (NDI). By leveraging the time-scale separation principle, the method integrates L1 adaptive dynamic inversion (L1-NDI) with incremental nonlinear dynamic inversion (INDI) control, thereby substantially enhancing the stability and robustness of the attitude controller. The design concurrently satisfies INDI’s requirements for state derivatives while applying filters to the adaptive control to prevent controller-induced high-frequency oscillations caused by abrupt model parameter changes. First, a dynamic model of the aircraft accounting for centroid shift is constructed. Assuming that the aircraft is a rigid body with constant mass, the net external force and net external moment acting on it after the centroid shift are calculated using Newtonian mechanics, thereby deriving the angular velocity dynamics. In this model, the effects induced by a sudden centroid shift are treated as disturbance terms, thereby establishing an accurate aircraft model with a centroid shift for subsequent simulations. Additionally, the dynamic equations of the attitude angles and angular rates are transformed into an affine nonlinear form to facilitate controller design. Next, a cascaded dual-loop nonlinear controller is designed for attitude angle and angular velocity regulation. This hierarchical architecture achieves precise and stable control of the aircraft attitude via a hierarchical control strategy. A core control algorithm based on the NDI is adopted in the design of the inner-loop control system. By constructing precise nonlinear state feedback channels, it compensates for the strong nonlinear coupling characteristics present in an aircraft's dynamic system in real time, thereby transforming the originally complex nonlinear system into a globally linearized system. Recognizing that NDI performance is fundamentally dependent on the availability of an accurate system model, an L1 adaptive control architecture is incorporated. This hybrid control approach guarantees system stability in the presence of disturbances through its L1-norm condition, while simultaneously resolving the high-frequency oscillation issues characteristic of conventional adaptive control schemes. The combined structure maintains rapid response characteristics while significantly enhancing robust performance. The outer loop for attitude-angle control uses incremental nonlinear dynamic inversion control. This is because the derivatives of the state variables can be readily obtained in the outer loop. This in turn makes the incremental nonlinear dynamic inversion control method particularly suitable owing to its simple structure and strong robustness. Finally, the stability of the incremental nonlinear dynamic inversion and L1 adaptive dynamic inversion control algorithms is rigorously proven based on the Lyapunov theory. Numerical simulations demonstrated that for angular rate tracking, the system successfully re-establishes command tracking within 0.5 s after centroid shift while maintaining minimal error bounds. Regarding attitude angle tracking, the system requires only 0.8 s to stabilize from the instant of centroid shift, achieving a maximum tracking error of merely 0.7°. These results conclusively validate that the proposed control framework not only delivers satisfactory control performance but also exhibits a strong disturbance rejection capability with respect to perturbations induced by an abrupt centroid shift.

This paper proposes a progressive dispersion-based salp swarm algorithm (DSSA) to improve convergence efficiency and solution accuracy in high-dimensional optimization. DSSA extends the multi-chain SSA through a dynamically expanding chain structure, where the number of subgroups increases linearly with iterations to progressively disperse the population. The leader salps' foraging behavior further incorporates the DE/best/1 mutation strategy within a hybrid selection framework, synergizing SSA's chain-driven exploitation with DE's directed global search. Consequently, DSSA exhibits a progressive behavioral transition---operating as multi-chain SSA in early iterations and gradually evolving into a DE-like algorithm upon full dispersion. The algorithm is validated on CEC-2017 benchmark functions against multiple DE variants, SSA variants, particle swarm optimization, and the grey wolf optimizer. Experimental results demonstrate that DSSA achieves superior performance across most test functions, particularly excelling in uni-modal and convex landscapes, while maintaining strong robustness and efficiency for problems with dimensionality exceeding 20.

For the quadrotor unmanned aerial vehicle(QUAV)attitude tracking problem under external disturbance and model uncertainty,a fixed-time command-filtered control approach is developed based on the composite adaptive radial basis function(RBF)neural network.Firstly,a fixed-time command filter based on the hyperbolic tangent function is proposed,which avoids the differential explosion problem during the derivation of virtual control and eliminates the singularity phenomena of traditional command filters with fractional order effectively.Secondly,the online approximation impact is enhanced by using a RBF neural network to approximate the model uncertainty and designing the adaptive adjustment law of neural network weights based on the tracking deviation.Additionally,combined with the backstepping method and disturbance observer,a fixed-time control strategy for the QUAV system is established,and the external disturbance is estimated and compensated by the disturbance observer,enabling rapid and accurate tracking of desired attitudes.The stability of the proposed control strategy is rigorously proved via Lyapunov theory.Finally,the effectiveness of the control strategy is verified by numerical simulation.

This study presents a performance-driven control strategy incorporating a super-twisting algorithm, which enforces the tracking error of a multi-body folding wings system to evolve within the bounds prescribed by a performance function, effectively addressing the challenges arising from aerodynamic load variations, gravity-vector reversals, and external disturbances during coordinated morphing maneuvers. A dynamic model of the folding-wing system incorporating unsteady aerodynamic effects is first established. Then, leveraging positive system theory and Metzler matrix properties, the proposed control framework ensures that the deviation between the tracking error and the prescribed performance boundaries remains non-negative, thereby guaranteeing strict error confinement. The introduction of the Super-Twisting Algorithm as a robust compensation enables the continuous cumulative estimation and cancellation of disturbances, while significantly suppressing high-frequency switching in the control torque, thereby achieving smooth control inputs. Simulation results confirm that the proposed method maintains smooth control responses and bounded tracking errors even under structural deformation and time-varying aerodynamic disturbances, demonstrating its robustness and practical applicability.

In this paper, an adaptive fixed-time reinforcement learning optimized backstepping control strategy is proposed for the attitude tracking problem of Reusable Launch Vehicles (RLVs) under system uncertainties and state constraints. First, an improved Asymmetric Barrier Lyapunov Function (ABLF) is introduced to enforce strict constraints on attitude angles and angular velocities, thereby enhancing flight safety. Meanwhile, Radial Basis Function (RBF) Neural Networks (NNs) are employed to approximate dynamic uncertainties, and a Series-Parallel Estimation Model (SPEM) is developed to improve the estimation accuracy and convergence rate. Moreover, based on the Identifier-Actor-Critic (IAC) framework, a fixed-time optimal backstepping control strategy utilizing the hyperbolic tangent function is developed, which not only eliminates the singularity issues inherent in conventional fractional-order controllers but also optimizes control performance while balancing energy consumption. Finally, the fixed-time stability of the proposed method is rigorously proved via Lyapunov theory, and the effectiveness of the developed control scheme is demonstrated through numerical simulations.

The incremental nonlinear dynamic inversion (INDI) method has been widely applied across various fields and is theoretically capable of achieving precise control of complex nonlinear systems. However, its performance is affected when control effectiveness is uncertain or actuator bandwidth is constrained. In this paper, we propose an adaptive nonlinear dynamic inversion algorithm with actuator dynamics (ANDIa) that incorporates a virtual control matrix and a nonlinear system estimator to address these limitations. Comparative analyses with the INDI controller demonstrate that ANDIa significantly improves control performance under conditions of uncertain control effectiveness and limited actuator bandwidth.

This article discusses a learning algorithm for nonlinear aircraft systems, which targets the weaknesses of data-driven algorithms, mainly poor generalization ability and limited interpretability. It handles these constraints by integrating physical information with data-driven techniques. Referred to as the physics-informed SINDY (PI-SINDY) framework in this article, it improves the standard SINDY algorithm to tackle strongly time-varying nonlinear flight systems. This method incorporates the physical information described by the aircraft's differential kinematic equations into the SINDY algorithm and can also deal with the effects of measurement noise, making it more robust and practical. The proposed method displays higher robustness and generalization ability in comparison with the original SINDY algorithm and the WSINDy method, as confirmed by simulation results. Finally, we use the nonlinear system model learned with the suggested method for tracking control to supplement its efficiency.

An integrated optimization approach based on Bayesian optimization theory is suggested for hypersonic aircraft in order to lower the computing cost and speed up the rate of convergence during multidisciplinary design optimization (MDO). This approach simultaneously gives the best wing arrangement and matching mission trajectory. Firstly, surrogate models of aerodynamic characteristics coefficients are constructed for hypersonic aircraft with different wing configurations. Based on this, an integrated iterative design process for the wing layout and trajectory is built using the Bayesian optimization approach. The output of this process is the optimal fuel consumption determined by the hp adaptive Radau pseudospectral method, while the input is the wing design parameters. With the sample points updated through the expected improvement (EI) function, the wing layout and corresponding optimal mission trajectory for specific flight missions are updated automatically. Simulation results show that the proposed method can significantly improve the iterative design efficiency while keeping the convergence accuracy, and it shows great value in engineering applications.

Quadcopter UAVs (Unmanned Aerial vehicles) are widely used in various fields due to their convenience, unmanned operation, and operational flexibility. Since the flight principle relies on the rotor fan to generate air flow for lift, and the quadcopter drone has four rotors with different speeds, it can achieve six degrees of freedom. Therefore, random wind disturbances will affect the quadcopter’s flight, causing it to deviate from the predetermined trajectory and leading to unexpected situations or even drone crashes. In order to avoid this situation, it is necessary to consider the corresponding algorithm to solve it. Therefore, this paper proposes a PID control strategy with anti-disturbance capability for quadcopter drones, based on a linear observer. The strategy aims to use the observer to detect unknown random disturbances and then pass the observation results to the PID controller, forming a feedforward compensation mechanism. The observation value and the output value of the PID controller are passed to the quadcopter drone model together to offset the impact of the disturbance. In the simulation, in order to simulate the real wind disturbance signal as much as possible, the Gaussian white noise module will be used for simulation, which has strong randomness and can be adjusted to meet the conditions of random unknown. The simulation results show that compared with the traditional PID without an observer, this paper performs better in trajectory tracking and anti-interference ability. In addition, the observer can observe the system state variables in addition to the disturbance, which improves the fault tolerance of the system. This paper provides a feasible control strategy for quadrotor drones in random unknown environments and sensor failures.

This paper investigates the characteristic modeling of a novel foldable wing aircraft and evaluates the control performance of various command tracking controllers. Firstly, a longitudinal four-degree-of-freedom nonlinear dynamical model is developed by employing the screw theory in conjunction with the Newton–Euler method. Then, the linear variable parameter (LPV) model is established with flight-path angle and folding angle as time-varying parameters. A trim and small-disturbance linearization is applied to each working point within the flight mission. Eventually, to ensure the flight stability of the deformation process, this paper employs both LQR and PID controllers, accomplishing the command tracking control for the nonlinear model. Simulation results show that compared with the fixed configuration control strategy, adopting a variable configuration control strategy reduces the angle of attack, thereby mitigating the stall risk caused by the excessive angle of attack during the climbing process. Additionally, the propeller speed is diminished under the variable configuration control strategy, which helps to minimize the flight energy consumption and improve the cruise capability.

In the engineering design of aerospace vehicles, design data at different stages exhibit hierarchical and heterogeneous distribution characteristics. Specifically, high-fidelity design data (such as from computational fluid dynamics simulations and flight tests) are costly and time-consuming to obtain. Moreover, the limited high-precision samples that are acquired often fail to cover the entire design space, resulting in a distribution characterized by small sample sizes. A critical challenge in data-driven modeling is efficiently fusing low-fidelity data with limited heterogeneous high-fidelity data to improve model accuracy and predictive performance. In response to this challenge, this paper introduces a Gaussian process fusion method for multi-fidelity data, founded on distribution characteristics. Multi-fidelity data are represented as intermediate surrogates using Gaussian processes, identifying heteroscedastic noise properties and deriving posterior distributions. The fusion is then treated as an optimization problem for prediction variance, using K-nearest neighbors and spatial clustering to determine optimal weights, which are adaptively adjusted based on sample density. These weights are adaptively adjusted based on the sample density to strengthen the local modeling behavior. The paper concludes with a comparative analysis, evaluating the proposed method against other conventional approaches using numerical cases and an aerodynamic prediction scenario for aerospace vehicles. A comparative analysis shows that the proposed method improves global modeling accuracy by 45% and reduces the demand for high-fidelity samples by over 40% compared to traditional methods. Applied in aerospace design, the method effectively merges multi-source data, establishing a robust hypersonic aerodynamic database while controlling modeling costs and demonstrating robustness to sample distribution.

Based on the variable gain extended state observer, a finite-time fault-tolerant control strategy is developed for the quadrotor unmanned aerial vehicle with actuator faults and external disturbances. Firstly, a novel variable gain extended state observer is designed to estimate the unknown external disturbances, which mitigates the initial peaking phenomenon existing in traditional extended state observer-based methods. Meanwhile, the neural networks are applied to accurately approximate unknown couplings online. Moreover, with the help of the projection operator technique, the unknown actuator faults are observed in real time. Combined with the backstepping framework, the finite-time robust fault-tolerant control scheme is constructed and the stability is strictly proved via Lyapunov’s theory. Finally, the validity of the developed control scheme is demonstrated through numerical simulations.

This paper proposes an improved pigeon-inspired optimization (IPIO) to solve the transition trajectory planning problem for a vertical takeoff and landing (VTOL) morphing unmanned aerial vehicle (UAV). Firstly, the dynamic model of the morphing aircraft is constructed. To ensure the stability and safety of the morphing aircraft during the transition process, a transition corridor of aircraft is established, which considers the dynamic changes of the flight speed and flight-path angle. And the trajectory optimization problem is constructed. Secondly, an IPIO algorithm is designed by combing the reverse learning strategy with the mutation and crossover operation to solve the problem that the pigeon-inspired optimization (PIO) can easily fall into local optimal due to premature convergence. Finally, a comparative simulation of IPIO and PIO is performed on the above-mentioned optimization mentioned above. Simulation results show that more optimal efficiency and engineering value of IPIO is shown to solve the complexly constrained optimization problems.

Aiming at the multiple Unmanned Aerial Vehicle (multi-UAV) task assignment problems, a multi-UAV task assignment algorithm based on the improved discrete pigeon-inspired optimization (PIO) algorithm is proposed considering various fitness functions and constraints. And a correction algorithm is designed for the constraint overflow problem in the algorithm. First, a multi-UAV task fitness function problem model is established with various benefits, costs, and constraints. In addition, referring to the idea of the learning factor in the particle swarm optimization (PSO) algorithm, the PIO algorithm is improved to strengthen the learning ability of the pigeons for global and local optimal information. Then, the improved PIO algorithm is discretized to fit the discrete task assignment model. Finally, aiming at the constraint overflow problem, a constraint check correction algorithm is designed to correct the constraint overflow sequence. Simulation experiments show that the improved discrete PIO algorithm can effectively solve the multi-UAV task assignment problem.

以异构多无人机协同执行复杂的耦合多任务为背景,提出一种求解分布式任务分配问题非死锁的顺序扩展一致性包算法.首先,建立考虑任务载荷资源、任务时序、威胁区等约束条件的时序多任务分配模型;其次,对一致性包算法的任务包构建过程和冲突消解规则进行扩展,并设计一种基于有向图深度优先搜索的方法进行任务方案的死锁检测和修正,以实现无冲突和无死锁的任务分配;然后,将关联任务之间的时序约束转化为软时间窗约束,利用顺序分层的策略进行求解;最后,为了提高任务分配结果的可靠性,采用Dubins曲线路径将航路规划耦合到任务分配中.仿真实验表明,所提出的算法能够快速有效地求解异构多无人机分布式耦合多任务分配问题,具备良好的最优性和时效性.

This paper investigates a rapid modeling method and robust analysis of hypersonic vehicles using multidisciplinary integrated techniques. First, the geometrical configuration is described using parametric methods based on the class–shape technique. Aerodynamic forces and moments are estimated for the specific configuration using engineering methods. Moreover, the nonlinear model is simplified by the polynomial fitting expressions, and the linear variable parameter model is obtained for the tracking control design and dynamic characteristic analysis with the aid of the sensitivity analysis and gap metric methods. A velocity-driven trajectory design method is deduced for hypersonic ascent, and the tracking control law is developed to realize the flight process from the initial point to the cruise point. Furthermore, a robust analysis process based on gap margin is proposed for climb trajectory tracking. Simulation results are provided to verify the feasibility of the proposed modeling method and show that the flight control of a hypersonic vehicle is more sensitive to altitude variation.

This paper models the Mars UAV formation exploring the surface of Mars, and then the formation obstacle avoidance is brought up with the assumptions of the Mars circumstance and the UAVs. Based on their specialty, constrained Delaunay triangulation, Yen-[Formula: see text] shortest path algorithm, the collaborative function, and the improved pigeon-inspired optimization (PIO) algorithm are integrated to solve the obstacle avoidance for the formation. Since the steering maneuver costs much energy and increases instabilities vulnerable in extraterrestrial exploration, the paper focuses on the route smoothness problem. The PIO is improved to be suitable for smooth routes and is compatible with other PIO variants. The simulation results show that the sum of the steering angle, namely the performance index, is effectively reduced and satisfies the obstacle avoidance requirements for Mars UAV formation.