Ground threat prediction-based path planning of unmanned autonomous helicopter using hybrid enhanced artificial bee colony algorithm

Unmanned autonomous helicopter(UAH)path planning problem is an important component of the UAH mission planning system.Aiming to reduce the influence of non-complete ground threat information on UAH path planning,a ground threat prediction-based path planning method is proposed based on artificial bee colony(ABC)algorithm by collaborative thinking strategy.Firstly,a dynamic threat distribution probability model is developed based on the characteristics of typical ground threats.The dynamic no-fly zone of the UAH is simulated and established by calculating the distribution probability of ground threats in real time.Then,a dynamic path planning method for UAH is designed in complex environment based on the real-time prediction of ground threats.By adding the collision warning mechanism to the path planning model,the flight path could be dynamically adjusted according to changing no-fly zones.Furthermore,a hybrid enhanced ABC algorithm is proposed based on collaborative thinking strategy.The proposed algorithm applies the leader-member thinking mechanism to guide the direction of population evolution,and reduces the negative impact of local optimal solutions caused by collaborative learning update strategy,which makes the optimization performance of ABC algorithm more controllable and efficient.Finally,simulation results verify the feasibility and effectiveness of the proposed ground threat prediction path planning method.

机遇与挑战:人类命运共同体理念国际认知状况的多维审视

特定时空环境下诸多因素和机制的混合交织决定了人类命运共同体理念在内的中国理念国际认知状况的复杂性。以“人类命运共同体”为例,该理念在时空维度契合了全球化趋势,但逆全球化运动使其遭到了排斥;在主体维度,理念契合了寻求加强和改革全球治理、改善自身发展环境的全球南方的普遍诉求,而试图维护“自由国际秩序”的霸权国家对中国理念持敌视态度;在利益维度,全球性问题与风险虽然建构了基于共同利益的共识基础,但国家间的利益分歧和冲突始终是影响中国理念国际认知的负面因素;在文化维度,理念凭借开放性、包容性特质具备被其他文明理解的可能,但地方性文明对普世价值的抗拒心理以及多元文化差异阻隔了理念认同的形成与内化;在传播维度,多元传播机制成为促成和提升中国理念国际认同的重要武器,但国际话语权的“西强东弱”成为影响传播效能的短期“天堑”。鉴于此,中国需要全面审视和理性应对中国理念及其国际传播与认同建构工作面临的机遇与挑战,以使“认同”真正成为推动构建人类命运共同体的观念性力量。

Improved lazy theta algorithm based on octree map for path planning of UAV

This paper investigates the path planning method of unmanned aerial vehicle(UAV)in threedimensional map.Firstly,in order to keep a safe distance between UAV and obstacles,the obstacle grid in the map is expanded.By using the data structure of octree,the octree map is constructed,and the search nodes is significantly reduced.Then,the lazy theta*algorithm,including neighbor node search,line-of-sight algorithm and heuristics weight adjustment is improved.In the process of node search,UAV constraint conditions are considered to ensure the planned path is actually flyable.The redundant nodes are reduced by the line-of-sight algorithm through judging whether visible between two nodes.Heuristic weight adjustment strategy is employed to control the precision and speed of search.Finally,the simulation results show that the improved lazy theta*algorithm is suitable for path planning of UAV in complex environment with multi-constraints.The effectiveness and flight ability of the algorithm are verified by comparing experiments and real flight.

Disturbance Observer-Based Safe Tracking Control for Unmanned Helicopters With Partial State Constraints and Disturbances

In this paper, a disturbance observer-based safe tracking control scheme is proposed for a medium-scale unmanned helicopter with rotor flapping dynamics in the presence of partial state constraints and unknown external disturbances. A safety protection algorithm is proposed to keep the constrained states within the given safe-set. A second-order disturbance observer technique is utilized to estimate the external disturbances. It is shown that the desired tracking performance of the controlled unmanned helicopter can be achieved with the application of the backstepping approach, dynamic surface control technique, and Lyapunov method. Finally, the availability of the proposed control scheme has been shown by simulation results.

Air combat decision-making of multiple UCAVs based on constraint strategy games

Game theory can be applied to the air combat decision-making problem of multiple unmanned combat air vehicles(UCAVs).However,it is difficult to have satisfactory decision-making results completely relying on air combat situation information,because there is a lot of time-sensitive information in a complex air combat environment.In this paper,a constraint strategy game approach is developed to generate intelligent decision-making for multiple UCAVs in complex air combat environment with air combat situation information and time-sensitive information.Initially,a constraint strategy game is employed to model attack-defense decision-making problem in complex air combat environment.Then,an algorithm is proposed for solving the constraint strategy game based on linear programming and linear inequality(CSG-LL).Finally,an example is given to illustrate the effectiveness of the proposed approach.

Robust Tracking Control for Self-balancing Mobile Robots Using Disturbance Observer

In this paper,a robust tracking control scheme based on nonlinear disturbance observer is developed for the self-balancing mobile robot with external unknown disturbances.A desired velocity control law is firstly designed using the Lyapunov analysis method and the arctan function.To improve the tracking control performance,a nonlinear disturbance observer is developed to estimate the unknown disturbance of the self-balancing mobile robot.Using the output of the designed disturbance observer,the robust tracking control scheme is presented employing the sliding mode method for the selfbalancing mobile robot.Numerical simulation results further demonstrate the effectiveness of the proposed robust tracking control scheme for the self-balancing mobile robot subject to external unknown disturbances.

Robust Tracking Control of Uncertain MIMO Nonlinear Systems with Application to UAVs

In this paper, we consider the robust adaptive tracking control of uncertain multi-input and multi-output (MIMO) nonlinear systems with input saturation and unknown external disturbance. The nonlinear disturbance observer (NDO) is employed to tackle the system uncertainty as well as the external disturbance. To handle the input saturation, an auxiliary system is constructed as a saturation compensator. By using the backstepping technique and the dynamic surface method, a robust adaptive tracking control scheme is developed. The closed-loop system is proved to be uniformly ultimately bounded thorough Lyapunov stability analysis. Simulation results with application to an unmanned aerial vehicle (UAV) demonstrate the effectiveness of the proposed robust control scheme. © 2014 Chinese Association of Automation.

Reinforcement Learning Based Data Fusion Method for Multi-Sensors

In order to improve detection system robustness and reliability, multi-sensors fusion is used in modern air combat. In this paper, a data fusion method based on reinforcement learning is developed for multi-sensors. Initially, the cubic B-spline interpolation is used to solve time alignment problems of multisource data. Then, the reinforcement learning based data fusion(RLBDF) method is proposed to obtain the fusion results. With the case that the priori knowledge of target is obtained, the fusion accuracy reinforcement is realized by the error between fused value and actual value. Furthermore, the Fisher information is instead used as the reward if the priori knowledge is unable to be obtained. Simulations results verify that the developed method is feasible and effective for the multi-sensors data fusion in air combat.

Sensor Fault Diagnosis for a Class of Time Delay Uncertain Nonlinear Systems Using Neural Network

In this paper,a sliding mode observer scheme of sensor fault diagnosis is proposed for a class of time delay nonlinear systems with input uncertainty based on neural network.The sensor fault and the system input uncertainty are assumed to be unknown but bounded.The radial basis function (RBF) neural network is used to approximate the sensor fault.Based on the output of the RBF neural network,the sliding mode observer is presented.Using the Lyapunov method,a criterion for stability is given in terms of matrix inequality.Finally,an example is given for illustrating the availability of the fault diagnosis based on the proposed sliding mode observer.

Fractional-Order Control for a Novel Chaotic System Without Equilibrium

The control problem is discussed for a chaotic system without equilibrium in this paper.On the basis of the linear mathematical model of the two-wheeled self-balancing robot,a novel chaotic system which has no equilibrium is proposed.The basic dynamical properties of this new system are studied via Lyapunov exponents and Poincar′e map.To further demonstrate the physical realizability of the presented novel chaotic system,a chaotic circuit is designed.By using fractional-order operators,a controller is designed based on the state-feedback method.According to the Gronwall inequality,Laplace transform and Mittag-Leffler function,a new control scheme is explored for the whole closed-loop system.Under the developed control scheme,the state variables of the closed-loop system are controlled to stabilize them to zero.Finally,the numerical simulation results of the chaotic system with equilibrium and without equilibrium illustrate the effectiveness of the proposed control scheme.

Robust bounded control for uncertain flight dynamics using disturbance observer

The robust bounded flight control scheme is developed for the uncertain longitudinal flight dynamics of the fighter with control input saturation invoking the backstepping technique. To enhance the disturbance rejection ability of the robust flight control for fighters, the sliding mode disturbance observer is designed to estimate the compounded disturbance including the unknown external disturbance and the effect of the control input saturation. Based on the backstepping technique and the compounded disturbance estimated output, the robust bounded flight control scheme is proposed for the fighter with the unknown external disturbance and the control input saturation. The closed-loop system stability under the developed robust bounded flight control scheme is rigorously proved using the Lyapunov method and the uniformly asymptotical convergences of all closed-loop signals are guaranteed. Finally, simulation results are presented to show the effectiveness of the proposed robust bounded flight control scheme for the uncertain longitudinal flight dynamics of the fighter.

Robust adaptive compensation control for unmanned autonomous helicopter with input saturation and actuator faults

This paper studies a robust adaptive compensation Fault Tolerant Control(FTC)for the medium-scale Unmanned Autonomous Helicopter(UAH)in the presence of external disturbances,actuator faults and input saturation.To improve the disturbance rejection capacity of the UAH system in actuator healthy case,an adaptive control method is adopted to cope with the external disturbances and a nominal controller is proposed to stabilize the system.Meanwhile,compensation control inputs are designed to reduce the negative effects derived from actuator faults and input saturation.Based on the backstepping control and inner-outer loop control technologies,a robust adaptive FTC scheme is developed to guarantee the tracking errors convergence.Under the presented FTC controller,the uniform ultimate boundedness of all closed-loop signals is ensured via Lyapunov stability analysis.Simulation results demonstrate the effectiveness of the proposed control algorithm.

Safety flight envelope calculation and protection control of UAV based on disturbance observer

In this paper,as for the unmanned air vehicle(UAV)under external disturbance,an attainable-equilibrium-set-based safety fight envelope(SFE)calculation method is proposed,based on which a prescribed performance protection control scheme is presented.Firstly,the existing definition of the SFE based on attainable equilibrium set(AES)is extended to make it consistent and suitable for the UAV system under disturbance.Secondly,a higher-order disturbance observer(HODO)is developed to estimate the disturbances and the disturbance estimation is applied in the computation of the SFE.Thirdly,by using the calculated SFE,a desired safety trajectory based on the time-varying safety margin function and first-order filter is developed to prevent the states of the UAV system from exceeding the SFE.Moreover,an SFE protection controller is proposed by combining the desired safety trajectory,backstepping method,HODO design,and prescribed performance(PP)control technique.In particular,the closed-loop system is established on the basis of disturbance estimation error,filter error,and tracking error.Finally,the stability of the closed-loop system is verified by the Lyapunov stability theory,and the simulations are presented to illustrate the effectiveness of the proposed control scheme.

Preface:Security and safety in unmanned systems

An unmanned system is defined as an electro-mechanical system capable of exerting its power to perform designated missions with no human operator aboard.Thanks to the development of digital design(artificial intelligence,control,etc.)and robotics in recent years,unmanned systems are making a revolution as an emerging technology with many different applications in the military,civilian,and commercial fields such as autonomous driving,medicine and healthcare,deep space exploration,and national security and defense.

编者按

近年来,随着新一代信息技术、机器人技术、大数据与人工智能技术等新兴技术的快速发展,无人机及其相关技术得到了迅猛发展.无人机作为一种不搭载飞行员的飞行器,具有成本低、机动性好、操控灵活等特点,已广泛应用于军事和民用领域.在民用领域,无人机已成功应用于航空遥感、空中摄影、环境监测、农作物监管、农药喷洒、火灾监控、空中安保、海岸搜救、海岸线和航道监测等不同任务场景.

非完备信息下无人机空战目标意图预测

实际空战的复杂性和不确定性及部分空战信息未知性,给无人机空战目标意图预测带来巨大挑战.针对非完备信息下无人机空战目标意图预测问题,本文提出了一种基于长短时记忆(long shortterm memory,LSTM)网络的非完备信息下空战目标意图预测模型.采用分层的方法建立空战目标意图预测特征集,并将空战信息编码成时序特征,将专家经验封装成标签,引入三次样条插值函数拟合以及平均值填充法来修补不完备数据,利用自适应矩估计(adaptive moment estimation,Adam)优化算法,加快目标意图预测模型训练速度,以便有效地防止局部最优的问题.最后通过仿真验证了所建立的无人机空战目标意图预测模型能有效预测无人机空战目标意图.

基于干扰观测器的变后掠翼近空间飞行器鲁棒跟踪控制

针对变后掠翼飞行器具有多工作模式、大飞行包络的特性,本文建立了非线性多模型切换系统,并研究了机翼后掠角变化过程中对飞行高度和飞行速度的跟踪控制.为了减小姿态系统中不确定性和外部未知扰动的影响,利用非线性干扰观测器对复合干扰进行逼近并设计滑模姿态控制器,保证了良好的姿态跟踪控制效果.采用平均驻留时间方法证明了所设计的控制器能够保证切换系统的稳定性.最后,通过仿真对所提方法的有效性进行了验证.

非完备策略集下人机对抗空战决策方法

在真实的空战对抗环境中,不确定性广泛存在.本文考虑人机对抗场景中的空战决策问题,提出了一种非完备策略集下的博弈决策方法.首先,将对抗的无人机群和有人机群的动态博弈过程分解为不同的决策阶段,并将当前的决策阶段建模为一个零和博弈模型.所建模型考虑了有人机群的策略不完全可知的情形,并用一个不确定效用矩阵来描述这种非完备策略.随后,基于矩阵博弈中的最大最小策略概念,提出了非完备策略下博弈模型的求解方法,从而给出了无人机群的决策序列.最后通过仿真实验验证了所提方法的有效性.

基于变权威胁评估的无人机安全飞行区域确定方法

针对复杂战场环境下确定无人机安全飞行区域的过程中面对的复杂性和不确定性问题,本文提出了一种基于变权理论和马尔可夫模型的无人机安全飞行区域确定方法.根据无人机相对于威胁区域距离的不同,引入变权理论对威胁指标权值进行调整,完成综合威胁大小计算.然后,利用马尔可夫模型对无人机飞行过程中受到的潜在威胁进行预测.接着,使用模糊评估方法评估无人机飞行区域的威胁等级,并引入云模型构建隶属度函数,以提高评估结果的可靠性.最后,根据威胁阈值确定无人机安全飞行区域.仿真结果表明所研究的无人机安全飞行区域确定方法在复杂战场环境下是有效的.

Robust control for an unmanned helicopter with constrained flapping dynamics

In this paper, a neural network based adaptive prescribed performance control scheme is proposed for the altitude and attitude tracking system of the unmanned helicopter in the presence of state and output constraints. For handling the state constraints, the barrier Lyapunov function and the saturation function are employed. And, the prescribed performance method is used to deal with the flapping angle constraints for the unmanned helicopter. It is proved that the proposed control approach can ensure that all the signals of the resulting closed-loop system are bounded, and the tracking errors are within the prescribed performance bounds for all time. The numerical simulation is given to illustrate the performance of the proposed scheme.