Design methodology of a mini-missile considering flight performance and guidance precision

The design of mini-missiles(MMs)presents several novel challenges.The stringent mission requirement to reach a target with a certain precision imposes a high guidance precision.The miniaturization of the size of MMs makes the design of the guidance,navigation,and control(GNC)have a larger-thanbefore impact on the main-body design(shape,motor,and layout design)and its design objective,i.e.,flight performance.Pursuing a trade-off between flight performance and guidance precision,all the relevant interactions have to be accounted for in the design of the main body and the GNC system.Herein,a multi-objective and multidisciplinary design optimization(MDO)is proposed.Disciplines pertinent to motor,aerodynamics,layout,trajectory,flight dynamics,control,and guidance are included in the proposed MDO framework.The optimization problem seeks to maximize the range and minimize the guidance error.The problem is solved by using the nondominated sorting genetic algorithm II.An optimum design that balances a longer range with a smaller guidance error is obtained.Finally,lessons learned about the design of the MM and insights into the trade-off between flight performance and guidance precision are given by comparing the optimum design to a design provided by the traditional approach.

Applying the disciplinary relation matrix to multidisciplinary design optimization modeling and solving

A new efficient coupling relationship description method has been developed to provide an automated and visualized way to multidisciplinary design optimization (MDO) modeling and solving. The disciplinary relation matrix (DRM) is proposed to describe the coupling relationship according to disciplinary input/output variables, and the MDO definition has been reformulated to adopt the new interfaces. Based on these, a universal MDO solving procedure is proposed to establish an automated and efficient way for MDO modeling and solving. Through a simple and convenient initial configuration, MDO problems can be solved using any of available MDO architectures with no further effort. Several examples are used to verify the proposed MDO modeling and solving process. Result shows that the DRM method has the ability to simplify and automate the MDO procedure, and the related MDO framework can evaluate the MDO problem automatically and efficiently.

Cooperative maneuver decision making for multi-UAV air combat based on incomplete information dynamic game

Cooperative autonomous air combat of multiple unmanned aerial vehicles(UAVs)is one of the main combat modes in future air warfare,which becomes even more complicated with highly changeable situation and uncertain information of the opponents.As such,this paper presents a cooperative decision-making method based on incomplete information dynamic game to generate maneuver strategies for multiple UAVs in air combat.Firstly,a cooperative situation assessment model is presented to measure the overall combat situation.Secondly,an incomplete information dynamic game model is proposed to model the dynamic process of air combat,and a dynamic Bayesian network is designed to infer the tactical intention of the opponent.Then a reinforcement learning framework based on multiagent deep deterministic policy gradient is established to obtain the perfect Bayes-Nash equilibrium solution of the air combat game model.Finally,a series of simulations are conducted to verify the effectiveness of the proposed method,and the simulation results show effective synergies and cooperative tactics.

Two-layer formation-containment fault-tolerant control of fixed-wing UAV swarm for dynamic target tracking

This paper tackles the formation-containment control problem of fixed-wing unmanned aerial vehicle(UAV)swarm with model uncertainties for dynamic target tracking in three-dimensional space in the faulty case of UAVs’actuator and sensor.The fixed-wing UAV swarm under consideration is organized as a“multi-leader-multi-follower”structure,in which only several leaders can obtain the dynamic target information while others only receive the neighbors’information through the communication network.To simultaneously realize the formation,containment,and dynamic target tracking,a two-layer control framework is adopted to decouple the problem into two subproblems:reference trajectory generation and trajectory tracking.In the upper layer,a distributed finite-time estimator(DFTE)is proposed to generate each UAV’s reference trajectory in accordance with the control objective.Subsequently,a distributed composite robust fault-tolerant trajectory tracking controller is developed in the lower layer,where a novel adaptive extended super-twisting(AESTW)algorithm with a finite-time extended state observer(FTESO)is involved in solving the robust trajectory tracking control problem under model uncertainties,actuator,and sensor faults.The proposed controller simultaneously guarantees rapidness and enhances the system’s robustness with fewer chattering effects.Finally,corresponding simulations are carried out to demonstrate the effectiveness and competitiveness of the proposed two-layer fault-tolerant cooperative control scheme.

Flight control for air-breathing hypersonic vehicles using linear quadratic regulator design based on stochastic robustness analysis

吸气式高超声速飞行器(air-breathing hypersonic vehicle,AHV)的飞行动力学模型具有高度非线性与多变量耦合等特性,且受到内部不确定性与外部干扰的综合影响,因此需要具有强鲁棒性与高精度的控制器。本文介绍了一种改进的基于随机鲁棒性分析的线性二次调节器(linear-quadratic regulator,LQR)设计方法,用于AHV的纵向飞行控制器设计。首先,应用输入输出反馈线性化技术设计LQR控制器。其次,基于系统参数的不确定性,将系统鲁棒性表征为满足稳定性与设计指标要求的概率,并构建系统鲁棒性与LQR参数之间的映射关系。为了实现系统鲁棒性最大化的目标,提出一种全新的混合粒子群优化算法对LQR的控制参数进行寻优计算。在优化迭代的过程中,使用切诺夫边界理论确定蒙特卡洛估计的随机样本量。最后,仿真结果表明该优化算法可以高效地获取满足设计要求的LQR控制参数最优解。

Application and Improvements of the Wing Deformation Capture with Simulation for Flapping Micro Aerial Vehicle

Wing deformation capture with simulation is a mixed experimental-numerical approach whereby the wing deformation during flapping is captured using high-speed cameras and used as an input for the numerical solver.This is an alternative approach compared to pure experiment or full fluid structure interaction simulation.This study is an update to the previous paper by Tay et al.,which aims to address the previous limitations.We show through thrust and vorticity contour plots that this approach can simulate Flapping Micro Aerial Vehiclex(FMAVs)with reasonable accuracy.Next,we use this approach to explain the thrust improvement when an additional rib is added to the original membrane wing,which is due to longer duration for the new wing to open during the fling stage.Lastly,by decreasing the number of points and frames per cycle on the wing,we can simplify and shorten the digitization process.These results show that this approach is an accurate and practical alternative which can be applied to general bio-inspired research.

A study of morphing aircraft on morphing rules along trajectory

Morphing aircraft can meet requirements of multi-mission during the whole flight due to changing the aerodynamic shape,so it is necessary to study itsmorphing rules along the trajectory.However,trajectory planning considering morphing variables requires a huge number of expensive CFD computations due to the morphing in view of aerodynamic performance.Under the given missions and trajectory,to alleviate computational cost and improve trajectory-planning efficiency formorphing aircraft,an offline optimizationmethod is proposed based onMulti-Fidelity Kriging(MFK)modeling.The angle of attack,Mach number,sweep angle and axial position of the morphing wing are defined as variables for generating training data for building the MFK models,in which many inviscid aerodynamic solutions are used as low-fidelity data,while the less high-fidelity data are obtained by solving viscous flow.Then the built MFK models of the lift,drag and pressure centre at the different angles of attack andMach numbers are used to predict the aerodynamic performance of the morphing aircraft,which keeps the optimal sweep angle and axial position of the wing during trajectory planning.Hence,themorphing rules can be correspondingly acquired along the trajectory,aswell as keep the aircraftwith the best aerodynamic performance during thewhole task.The trajectory planning of amorphing aircraft was performed with the optimal aerodynamic performance based on the MFK models,built by only using 240 low-fidelity data and 110 high-fidelity data.The results indicate that a complex trajectory can take advantage of morphing rules in keeping good aerodynamic performance,and the proposed method is more efficient than trajectory optimization by reducing 86%of the computing time.

Twistor-based synchronous sliding mode control of spacecraft attitude and position

A synchronous control of relative attitude and position is required in separated ultraquiet spacecraft, such as drag-free, disturbance-free, and distributed spacecraft. Thus, a twistorbased synchronous sliding mode control is investigated in this paper to solve the control problem of relative attitude and position among separated spacecraft modules. The twistor-based control design and the stability proof are implemented using the Modified Rodrigues Parameter（MRP）.To evaluate the effectiveness of the proposed control method, this paper presents a case study of separated spacecraft flying control considering the mass uncertainty and external disturbances. In addition, a simulation study of the Proportional-Derivative（PD） control is also presented for comparison. The results indicate that the twistor-based sliding mode controller can ensure global asymptotic stability. The states converge fast with ultra-precision and ultra-stability in both the attitude and position. Moreover, the proposed twistor-based sliding mode control system is robust to the mass uncertainty and external disturbances.

Unsupervised Interpolation Recovery Method for Spectrum Anomaly Detection and Localization

With the growing efficiency of the use of unlicensed spectrum,the challenge of ensuring spectrum security has become increasingly daunting.Spectrum managers aim to accurately and efficiently detect and recognize anomaly behaviors in the spectrum.In this study,we propose a novel framework for spectrum anomaly detection and localization by spectrum interpolation recovery.Spectrum interpolation recovery refers to the recovery of the rest of the spectrum distribution based on a part of the spectrum distribution,which is achieved through a masked autoencoder(MAE)model with a core of multi-head self-attention(MHSA)mechanism.The spectrum interpolation recovery method restores the region where the masked abnormal signals are present,yielding anomaly-free results,with the difference between the restored and the masked representing the anomaly signals.The proposed method has been demonstrated to effectively reduce model-induced over-recovery of anomalous signals and dilute large-scale generation errors caused by anomalies,thereby improving the detection and localization performance of anomaly signals,and improving the area under the receiver operating characteristic curve(AUC)and the area under the precision-recall curve(AUPRC)by 0.0382(3.68%)and 0.1992(68.90%),respectively.On a designed dataset containing 3 variables of interference-to-signal ratio(ISR),signal-to-noise ratio(SNR),and anomaly type,the total recall of anomaly detection and localization at a 5%false alarm rate reached 0.8799 and 0.5536,respectively.Furthermore,a comparative study among different methods demonstrates the effectiveness and rationality of the proposed method.