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.

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.

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 article presents a novel attitude tracking scheme for reentry-phase reusable launch vehicles that overcomes the limitations of traditional controllers in achieving fast convergence and high precision. The proposed fixed-time PPC approach, incorporating command filters and backstepping design, simultaneously ensures prescribed transient behavior, steady-state accuracy, and time-certain stability. First, the derivative of the virtual control input is estimated through a 1st-order filter architecture featuring smooth hyperbolic tangent. Meanwhile, the filtering error is compensated to achieve fixed-time convergence of the filtering error. Secondly, a new error transformation function is constructed by using a scaling function. This transformation method removes the initial condition constraints inherent in traditional PPC. Ultimately, a backstepping-based fixed-time prescribed performance control scheme is developed, guaranteeing performance attainment of all tracking errors within a fixed-time interval under any initial conditions. Simulation results confirm that the proposed method effectively achieves attitude tracking for RLV, providing a reliable solution for the guidance and control of reusable launch vehicles during the reentry phase.

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.

The attitude control of reusable launch vehicle (RLV) during reentry faces challenges such as hypersonic flight characteristics, strong nonlinear couplings, and complex disturbances, which traditional control methods struggle to address with high precision and robustness. This paper proposes a backstepping-based prescribed-time control approach. The method achieves initial-state-independent convergence within a prescribed time and mitigate control gain mutation issues. Firstly, a prescribed-time control law is designed using backstepping to ensure attitude tracking errors converge within the user-defined time, independent of initial conditions. Secondly, a prescribedtime filter is introduced to resolve the “explosion of complexity” problem in backstepping while guaranteeing that filter errors converge within the prescribed time. Finally, the controller structure is modified to eliminate control gain mutations near the prescribed time. Theoretical analysis based on Lyapunov stability theory rigorously demonstrates the prescribed-time stability of the proposed method, and numerical simulations verify its effectiveness.

This article introduces an innovative adaptive control strategy for precise attitude tracking control of Quadrotor Unmanned Aerial Vehicles (QUAVs), addressing critical challenges posed by state constraints, model uncertainties, and external disturbances. Departing from conventional extended state observers, a Compensation Function Observer (CFO) is established to compensate for the lumped disturbances through fully exploiting system information. Subsequently, by integrating the command filter and Barrier Lyapunov Function (BLF), an adaptive backstepping control strategy is proposed, which ensures rigorous boundedness of tracking errors, without violation of state constraints. Finally, comprehensive simulations are conducted to illustrate the advantages of the approach proposed.

In this work, a robust adaptive anti-swing control strategy is proposed for the quadrotor slung-load system (QSLS) with constrained flight states and unknown disturbances. Based on the online trajectory planning approach, the smooth reference trajectory is generated. Different from the traditional extended state observer method, a novel compensation function observer is developed to handle the unknown disturbances, which has better observation accuracy. Meanwhile, by means of the barrier Lyapunov function (BLF) technique, the boundary of the state errors can be guaranteed within the specified range. Especially, the presented method can solve the singularity problem that the denominator of the BLF reaches zero at some point. Following the design flow of backstepping methodology, an adaptive tracking control scheme is developed for the QSLS such that the swing angles are stable with time and the constrained flight states are not violated invariably. Finally, contrastive simulation results validate the validity of the proposed control algorithm.

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.

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.

In this paper, an extended state observer (ESO)-based sliding mode tracking control method is designed for the quadrotor unmanned aerial vehicle (UAV) with external disturbances and actuator faults. Firstly, the nonlinear model of the quadrotor UAV is established. Then the ESO is constructed to tackle the unknown differentiable disturbances and the sliding mode control technique is combined with adaptive estimation to address the unknown nondifferentiable actuator faults, respectively. Finally, a robust fault-tolerant tracking control method is proposed to make sure that all closed-loop system errors are uniformly ultimate bounded via Lyapunov stability analysis, and the efficiency of the proposed approach is confirmed by the numerical simulation.

This paper proposes a hypersonic vehicle aerodynamic parameter identification method, based on the improved harris hawks optimization (IHHO) algorithm. It is used to deal with the problem that the traditional great likelihood method in hypersonic vehicle aerodynamic parameter identification can lead to sensitivity to initial values, transforming the identification problem into an optimization problem. This algorithm simulates the hunting behaviour of the harris hawk. It makes the hawk population evolve in a better direction by switching the exploration and exploitation stages and choosing different besiege strategies. It can improve the way of updating the escape energy and enhance the hawk flock's global searching ability and pre-search efficiency. The identification result shows that this method effectively reduces the initial value sensitivity, accelerates the convergence speed, and improves the identification accuracy, which is valuable in engineering applications.

针对放宽静稳定度条件下水平起降空天飞行器控制舵面尺寸设计难度大的问题,提出了一种基于代理模型的控制舵面一控制参数一体化设计方法.首先,基于鸽群算法构建了包含结构参数的空天飞行器气动特性代理模型,获得了气动特性参数随飞行条件、控制舵面尺寸及质心位置的变化关系,为控制舵面一体化设计提供输入.然后,设计了基于C*结构的空天飞行器纵向参考模型跟踪控制律,并将考虑飞行品质约束的空天飞行器控制舵面一体化设计问题转化成多约束条件下的多目标优化问题.并采用非光滑优化算法计算得到了同时满足飞行品质、舵面饱和、舵面偏转速率等约束的最小控制舵面及对应的控制参数.仿真结果表明,该方法能够在满足性能指标约束的前提下有效减小控制舵面的尺寸,具有较强的工程应用价值.

For an aerospace vehicle (AV), its high-order dynamics increases the pole placement complexity and makes the classic short-period and phugoid-period modes are difficult to distinguish from one another. To solve these two problems, an improved identification algorithm is proposed to obtain a low-order equivalent system (LOES) of AV, which effectively reflects the modal coupling characteristics. In the improved identification algorithm, a pigeon-estimation algorithm based on simulated anneal (SA) is proposed to optimize the LOES parameters. Then, a robust controller is designed based on the obtained LOES, with all the regional stability and robust performance requirements satisfied. Simulation results verify the effectiveness of the proposed method and show that the obtained LOES model can capture the motion characteristics of AV and is suitable for the control system design.

In this work, a method has been presented to analyze the influence of control saturation and structural flexibility on the stable radius of highly flexible aircraft. A dynamic model of aircraft is constructed followed by the analysis of kinetic characteristics. In this paper, the closed-loop stability boundary of highly flexible aircraft with open-loop instability is studied. The amplitude limit and bandwidth limit of the control signal are considered in the closed-loop stability boundary calculation. Our analysis shows that the boundary is related to the left eigenvector corresponding to the unstable poles and the amplitude constraint of the control signals. Stability of the boundary of feedback control system further reduces the limitation of the bandwidth of actuators. Focused on the phugoid instability of highly flexible aircraft, computational formulation of the closed-loop stable boundary is achieved. The Monte Carlo analysis has been employed to validate the stable region, under the LQR controller. Both the theory and simulations have nice correlations with each other which verify the stability of the closed-loop system, restricted by the open-loop system, and the influence of control signal bandwidth constraints.

The hypersonic vehicle has problems with multiple systems coupling and multivariate design. Hence, it is difficult to determine the surrogate model structure of the hypersonic vehicle under larger samples. To solve this problem, this paper proposes a surrogate model structure optimization method based on the idea of PIO algorithm. The method can independently search for a polynomial surrogate model structure that satisfies the accuracy and prediction performance requirements. Through comparative analysis, utilizing this method outperforms Davidson’s genetic programming algorithm. Furthermore, it was applied to the global aerodynamic data fitting and control design of a hypersonic vehicle, and the simulation results reveal that the error of the prediction data obtained by this method was less than 5%, which has a good fitting effect and can meet the engineering application requirements.

本文提出一种新的方法对随机系统进行运动预测和控制指令设计,该方法可以充分利用已知信息设计控制指令以提高闭环随机系统的鲁棒性.首先采用混饨多项式对随机信息进行数学表述,并利用Galerkin投影法将随机变量的混饨多项式引入常微分方程中.然后,将随机变量的均值和方差考虑至优化问题的成本函数中,并利用伪谱法对控制指令进行鲁棒优化.最后,将该方法应用于飞行器的动力学预测以及控制指令设计.仿真结果表明,该方法能够预测飞行器飞行过程中不确定性的演化,其精度与蒙特卡罗方法相当,并且计算效率更高.此外,获得的控制指令对存在不确定参数或初始条件的随机系统具有强鲁棒性.

针对混合翼垂直起降无人机过渡过程中模型参数变化大、特性耦合严重的问题,本文提出了一种基于保护映射理论的垂直起降无人机过渡过程自适应切换控制器的设计方案.以雅可比线性化方法为基础,搭建混合翼垂直起降无人机过渡过程的线性变参数模型,选取线性二次型为基本控制结构并求取过渡过程起始点的控制参数.基于保护映射理论求取初始控制参数的稳定范围,进而通过自动迭代获取整个过渡过程中满足性能指标的控制器参数集合.对所得控制器参数进行插值拟合,获得混合翼垂直起降无人机过渡过程自适应控制律.仿真结果表明,所设计的自适应控制律能够保证闭环系统的鲁棒稳定,满足混合翼垂直起降无人机过渡过程中的定高加速稳定控制.

We present a control-oriented low-speed dynamic modeling and trade-off study framework for a conceptual air-breathing horizontal take-off and horizontal landing (HTHL) aerospace vehicle, which is powered by a turbine-based combined cycle engine. First, the 3D class/shape transformation method is modified to enhance the continuity property between different blocks, combined with the power function. Then, the panel method based on potential theory is employed to calculate the pressure distribution over discretized panel surfaces, resulting in the aerospace vehicle’s aerodynamic coefficients. To overcome the intractability of the physics-based model, stepwise regression analysis is adopted and simplified polynomials of aerodynamic coefficients are evaluated. Finally, stability and control analysis is conducted, aiming to find the proper center-of-gravity locations under different constraints. The proposed framework is verified through a conceptual aerospace vehicle simulation, with emphasis on horizontal take-off rotation and landing nose hold-off capabilities. Simulation results indicate that the proposed framework is capable of rapid control-oriented dynamic modeling and iterative design of HTHL aerospace vehicles.

This paper presents a low speed stability and control analysis of air-breathing horizontal take-off and horizontal landing (HTHL) aerospace vehicle, which is powered by turbine based combined cycle engine in turbofan mode. First, the conceptual HTHL vehicle and control-oriented dynamic modeling framework is introduced. Then, in order to emphasize stability and flying quality constraints in the concept design phase of HTHL aerospace vehicle, necessary conditions for the maximum allowable center-of-gravity travel are derived and casted as linear matrix inequalities, for which efficient numerical solver is available. Finally, as a preliminary investigation, and to verify the proposed dynamic modeling framework, the feasible center-of-gravity position of the concept HTHL aerospace vehicle is studied.