基于鲁棒H∞控制的多机协同编队

针对环境风干扰情况下无人机编队保持精度差的问题,设计了基于邻居无人机相对状态的编队控制协议。通过定义恰当的被控输出来量化环境风干扰对多机编队的影响,将受扰多机编队控制问题转化为鲁棒H∞控制问题。基于H∞控制方法,得到了满足期望H∞干扰抑制指标的多机编队充分条件,并以线性矩阵不等式的形式给出。此外,对于僚机之间的通信拓扑为无向图的情形,可以通过只求解2个线性矩阵不等式确定控制协议。最后数值仿真结果表明,该控制协议能够有效抑制阵风干扰对多机编队的影响,提高了多机协同编队的鲁棒性。     

A guidance law for UAV autonomous aerial refueling based on the iterative computation method

The rendezvous and formation problem is a significant part for the unmanned aerial vehicle（UAV） autonomous aerial refueling（AAR） technique. It can be divided into two major phases： the long-range guidance phase and the formation phase. In this paper, an iterative computation guidance law（ICGL） is proposed to compute a series of state variables to get the solution of a control variable for a UAV conducting rendezvous with a tanker in AAR. The proposed method can make the control variable converge to zero when the tanker and the UAV receiver come to a formation flight eventually. For the long-range guidance phase, the ICGL divides it into two sub-phases： the correction sub-phase and the guidance sub-phase. The two sub-phases share the same iterative process. As for the formation phase, a velocity coordinate system is created by which control accelerations are designed to make the speed of the UAV consistent with that of the tanker.The simulation results demonstrate that the proposed ICGL is effective and robust against wind disturbance.     

多无人机协同控制方法及应用研究

多无人机集群作战是未来战争的重要形式。作为集群作战中的关键技术,协同控制有着极为广泛的应用,例如多无人机编队飞行、协同侦查与集群攻击等。简述了多无人机集群作战的发展历程,归纳了集群作战过程中的关键技术,给出了协同控制方法的分类与体系结构。然后,从编队控制、合围控制、跟踪控制3个方面,总结了近年来国内外关于协同控制方法的研究成果。重点介绍了编队控制中的四种典型方法及相关应用,分析了各类编队控制方法的优缺点。最后对多无人机协同控制方法的未来发展方向进行了展望。     

Cooperative task assignment of multi-UAV system

With the rapid development of Unmanned Aerial Vehicle(UAV) technology, one of the emerging fields is to utilize multi-UAV as a team under autonomous control in a complex environment. Among the challenges in fully achieving autonomous control, Cooperative task assignment stands out as the key function. In this paper, we analyze the importance and difficulties of multiUAV cooperative task assignment in characterizing scenarios and obtaining high-quality solutions.Furthermore, we present three promising directions for future research: Cooperative task assignment in a dynamic complex environment, in an unmanned-manned aircraft system and in a UAV swarm. Our goal is to provide a brief review of multi-UAV cooperative task assignment for readers to further explore.     

Multi-UAV reconnaissance task allocation for heterogeneous targets using an opposition-based genetic algorithm with double-chromosome encoding

This paper presents a novel multiple Unmanned Aerial Vehicles (UAVs) reconnaissance task allocation model for heterogeneous targets and an effective genetic algorithm to optimize UAVs’ task sequence. Heterogeneous targets are classified into point targets, line targets and area targets according to features of target geometry and sensor’s field of view. Each UAV is regarded as a Dubins vehicle to consider the kinematic constraints. And the objective of task allocation is to minimize the task execution time and UAVs’ total consumptions. Then, multi-UAV reconnaissance task allocation is formulated as an extended Multiple Dubins Travelling Salesmen Problem (MDTSP), where visit paths to the heterogeneous targets must meet specific constraints due to the targets’ feature. As a complex combinatorial optimization problem, the dimensions of MDTSP are further increased due to the heterogeneity of targets. To efficiently solve this computationally expensive problem, the Opposition-based Genetic Algorithm using Double-chromosomes Encoding and Multiple Mutation Operators (OGA-DEMMO) is developed to improve the population variety for enhancing the global exploration capability. The simulation results demonstrate that OGA-DEMMO outperforms the ordinary genetic algorithm, ant colony optimization and random search in terms of optimality of the allocation results, especially for large scale reconnaissance task allocation problems.     

Modeling novel methodologies for unmanned aerial systems–Applications to the UAS-S4 Ehecatl and the UAS-S45 Bálaam

The rising demand for Unmanned Aerial Systems(UASs) to perform tasks in hostile environments has emphasized the need for their simulation models for the preliminary evaluations of their missions. The efficiency of the UAS model is directly related to its capacity to estimate its flight dynamics with minimum computational resources. The literature describes several techniques to estimate accurate aircraft flight dynamics. Most of them are based on system identification. This paper presents an alternative methodology to obtain complete model of the S4 and S45 unmanned aerial systems. The UAS-S4 and the UAS-S45 models were divided into four sub-models, each corresponding to a specific discipline: aerodynamics, propulsion, mass and inertia, and actuator. The‘‘aerodynamic\" sub-model was built using the Fderivatives in-house code, which is an improvement of the classical DATCOM procedure. The ‘‘propulsion\" sub-model was obtained by coupling a two-stroke engine model based on the ideal Otto cycle and a Blade Element Theory(BET) analysis of the propeller. The ‘‘mass and the inertia\" sub-model was designed utilizing the Raymer and DATCOM methodologies. A sub-model of an actuator using servomotor characteristics was employed to complete the model. The total model was then checked by validation of each submodel with numerical and experimental data. The results indicate that the obtained model was accurate and could be used to design a flight simulator.     

固定翼无人机编队集结控制算法研究

针对固定翼无人机协同作战时的编队集结问题,提出了一种新的路径规划和位置分配方法,并设计了包括航迹跟踪、高度保持和速度控制在内的自动驾驶仪。该路径规划算法通过矩阵迭代得到一组较优的目标点分配方案,满足总航程较小和同时到达约束。根据得到的各无人机飞向目标点的航迹,算出无人机编队集结的代价矩阵。在每架无人机确定了应飞航路后,开始沿航路飞向目标点,在此过程中,纵向采用高度保持自动驾驶仪,横向采用航迹跟踪自动驾驶仪,控制无人机按规定航迹飞行。速度调节自动驾驶仪可根据速度指令调节油门大小加减速,跟踪上目标速度,进而实现编队集结。仿真结果验证了所提出的编队集结控制方法的有效性和可行性。     

Adaptive path planning for unmanned aerial vehicles based on bi-level programming and variable planning time interval

This paper presents an adaptive path planner for unmanned aerial vehicles (UAVs) to adapt a real-time path search procedure to variations and fluctuations of UAVs’ relevant performances, with respect to sensory capability, maneuverability, and flight velocity limit. On the basis of a novel adaptability-involved problem statement, bi-level programming (BLP) and variable planning step techniques are introduced to model the necessary path planning components and then an adaptive path planner is developed for the purpose of adaptation and optimization. Additionally, both probabilistic-risk-based obstacle avoidance and performance limits are described as path search constraints to guarantee path safety and navigability. A discrete-search-based path planning solution, embedded with four optimization strategies, is especially designed for the planner to efficiently generate optimal flight paths in complex operational spaces, within which different surface-to-air missiles (SAMs) are deployed. Simulation results in challenging and stochastic scenarios firstly demonstrate the effectiveness and efficiency of the proposed planner, and then verify its great adaptability and relative stability when planning optimal paths for a UAV with changing or fluctuating performances.     

Optimization of beamforming and path planning for UAV-assisted wireless relay networks

Recently, unmanned aerial vehicles （UAVs） acting as relay platforms have attracted considerable attention due to the advantages of extending coverage and improving connectivity for long-range communications. Specifically, in the scenario where the access point （AP） is mobile, a UAV needs to find an efficient path to guarantee the connectivity of the relay link. Motivated by this fact, this paper proposes an optimal design for beamforming （BF） and UAV path planning. First of all, we study a dual-hop amplify-and-forward （AF） wireless relay network, in which a UAV is used as relay between a mobile AP and a fixed base station （BS）. In the network, both of the AP and the BS are equipped with multiple antennas, whereas the UAV has a single antenna. Then, we obtain the output signal~to-noise ratio （SNR） of the dual-hop relay network. Based on the criterion of maximizing the output SNR, we develop an optimal design to obtain the solution of the optimal BF weight vector and the UAV heading angle. Next, we derive the closed-form outage probability （OP） expression to investigate the performance of the dual-hop relay network conveniently. Finally, computer simulations show that the proposed approach can obtain nearly optimal flying path and OP performance, indicating the effectiveness of the proposed algorithm. Furthermore, we find that increasing the antenna number at the BS or the maximal heading angle can significantly improve the performance of the considered relay network.     

一种自主飞行的小型无人机系统设计

介绍了小型无人机系统的总体结构,分析了无人机的结构设计和算法,阐述了弹射起飞系统和伞降着陆系统的设计原理,采用嵌入式系统和GPS设计无人机的飞行姿态控制、导航控制、任务控制系统和数据链,实现了空中机器人的自主飞行。     

Formation tracking control for time-delayed multi-agent systems with second-order dynamics

In this paper, formation tracking control problems for second-order multi-agent systems (MASs) with time-varying delays are studied, specifically those where the position and velocity of followers are designed to form a time-varying formation while tracking those of the leader. A neigh-boring relative state information based formation tracking protocol with an unknown gain matrix and time-varying delays is presented. The formation tracking problems are then transformed into asymptotically stable problems. Based on the Lyapunov-Krasovskii functional approach, condi-tions sufficient for second-order MASs with time-varying delays to realize formation tracking are examined. An approach to obtain the unknown gain matrix is given and, since neighboring relative velocity information is difficult to measure in practical applications, a formation tracking protocol with time-varying delays using only neighboring relative position information is introduced. The proposed results can be used on target enclosing problems for MASs with second-order dynamics and time-varying delays. An application for target enclosing by multiple unmanned aerial vehicles (UAVs) is given to demonstrate the feasibility of theoretical results.     

无人机自主控制发展趋势研究

根据无人机自主控制的定义与内涵,将无人机自主控制关键技术划分为态势感知技术、规划与协同技术、自主决策技术和执行任务技术,并针对这四项关键技术发展所涉及内容分别进行了层级研究,初步给出了我国无人机自主控制层级发展规划和层级发展趋势图,可以为我国建立无人机自主控制能力标准或者开展无人机自主控制研究项目提供参考.     

Real-time trajectory planning for UCAV air-to-surface attack using inverse dynamics optimization method and receding horizon control

This paper presents a computationally efficient real-time trajectory planning framework for typical unmanned combat aerial vehicle (UCAV) performing autonomous air-to-surface (A/S) attack. It combines the benefits of inverse dynamics optimization method and receding horizon optimal control technique. Firstly, the ground attack trajectory planning problem is mathematically formulated as a receding horizon optimal control problem (RHC-OCP). In particular, an approximate elliptic launch acceptable region (LAR) model is proposed to model the critical weapon delivery constraints. Secondly, a planning algorithm based on inverse dynamics optimization, which has high computational efficiency and good convergence properties, is developed to solve the RHCOCP in real-time. Thirdly, in order to improve robustness and adaptivity in a dynamic and uncer- tain environment, a two-degree-of-freedom (2-DOF) receding horizon control architecture is introduced and a regular real-time update strategy is proposed as well, and the real-time feedback can be achieved and the not-converged situations can be handled. Finally, numerical simulations demon- strate the efficiency of this framework, and the results also show that the presented technique is well suited for real-time implementation in dynamic and uncertain environment.     

A memetic algorithm for path planning of curvature-constrained UAVs performing surveillance of multiple ground targets

The problem of generating optimal paths for curvature-constrained unmanned aerial vehicles （UAVs） performing surveillance of multiple ground targets is addressed in this paper. UAVs are modeled as Dubins vehicles so that the constraints of UAVs＇ minimal turning radius can be taken into account. In view of the effective surveillance range of the sensors equipped on UAVs, the problem is formulated as a Dubins traveling salesman problem with neighborhood （DTSPN）. Considering its prohibitively high computational complexity, the Dubins paths in the sense of terminal heading relaxation are introduced to simplify the calculation of the Dubins distance, and a boundary-based encoding scheme is proposed to determine the visiting point of every target neighborhood. Then, an evolutionary algorithm is used to derive the optimal Dubins tour. To further enhance the quality of the solutions, a local search strategy based on approximate gradient is employed to improve the visiting points of target neighborhoods. Finally, by a minor modification to the individual encoding, the algorithm is easily extended to deal with other two more sophisticated DTSPN variants （multi-UAV scenario and multiple groups of targets scenario）. The performance of the algorithm is demonstrated through comparative experiments with other two state-of-the-art DTSPN algorithms identified in literature. Numerical simulations exhibit that the algorithm proposed in this paper can find high-quality solutions to the DTSPN with lower computational cost and produce significantly improved performance over the other algorithms.     

变论域模糊控制在无人飞行器马赫数控制中的应用研究

针对无人飞行器的控制要求,提出了一种增量式变论域模糊智能控制算法。该算法具有调节裕度大、鲁棒性强的特点,适用于非线性控制系统,解决了无人飞行器巡航马赫数缓慢升高不能稳定跟踪目标值的问题。通过模拟飞行轨迹的半实物仿真试验,验证了模糊智能控制算法的控制效果,试验结果表明,通过采用模糊控制方法大大提高飞行马赫数控制的动态性能,满足无人飞行器控制系统设计要求,取得了较好的控制效果。 