Virtual target guidance-based distributed model predictive control for formation control of multiple UAVs

The paper proposes a Virtual Target Guidance (VTG)-based distributed Model Predictive Control (MPC) scheme for formation control of multiple Unmanned Aerial Vehicles (UAVs). First, a framework of distributed MPC scheme is designed in which each UAV only shares the information with its neighbors, and the obtained local Finite-Horizon Optimal Control Problem (FHOCP) can be solved by swarm intelligent optimization algorithm. Then, a VTG approach is developed and integrated into the distributed MPC scheme to achieve trajectory tracking and obstacle avoidance. Further, an event-triggered mechanism is proposed to reduce the computational burden for UAV formation control, which takes into consideration the predictive state errors as well as the convergence of cost function. Numerical simulations show that the proposed VTG-based distributed MPC scheme is more computationally efficient to achieve formation control of multiple UAVs in comparison with the traditional distributed MPC method.     

Distributed three-dimensional cooperative guidance via receding horizon control

The paper presents a new three-dimensional (3D) cooperative guidance approach by the receding horizon control (RHC) technique. The objective is to coordinate the impact time of a group of interceptor missiles against the stationary target. The framework of a distributed RHC scheme is developed, in which each interceptor missile is assigned its own finite-horizon optimal control problem (FHOCP) and only shares the information with its neighbors. The solution of the local FHOCP is obtained by the constrained particle swarm optimization (PSO) method that is integrated into the distributed RHC framework with enhanced equality and inequality con-straints. The numerical simulations show that the proposed guidance approach is feasible to imple-ment the cooperative engagement with satisfied accuracy of target capture. Finally, the computation efficiency of the distributed RHC scheme is discussed in consideration of the PSO parameters, con-trol update period and prediction horizon.     

Distributed coordinated control scheme of UAV swarm based on heterogeneous roles

This paper proposes a new distributed coordinated control scheme based on heterogeneous roles for Unmanned Aerial Vehicle(UAV) swarm to achieve formation control. First, the framework of the distributed coordinated control scheme is designed on the basis of Distributed Model Predictive Control(DMPC). Then, the effect of heterogeneous roles including leader, coordinator and follower is discussed, and the role-based cost functions are developed to improve the performance of coordinated control for...     

基于预测控制方法的UAV编队飞行

针对传统UAV编队控制律显式考虑约束能力不强的不足,基于预测控制方法和虚拟结构控制策略,设计了一种分布式编队控制律。该控制律能显式地考虑各种约束,每个UAV仅需要滚动求解有限时域优化问题得到自身控制输入,提出了一种简单的代价惩罚的方法实现障碍规避,并将优先级策略引入UAV之间避碰中。仿真结果表明,所设计的编队控制律具有...     

非合作目标交会的双层MPC全局轨迹规划控制

针对空间非合作目标近距离视线交会中的全局最优鲁棒轨迹规划与控制问题,提出了基于高斯伪谱方法（GPM）和线性时变模型预测控制（LTVMPC）的双层模型预测控制（MPC）算法。在轨迹规划方面,以视线坐标系下的相对轨道动力学为模型、能量最少和控制精度最优为性能指标构建最优控制问题,利用GPM精度高、收敛速度快的特点将最优控制问题转化为易于求解的全局非线性规划问题,在MPC框架下求解得到全局最优的标称轨迹,克服了传统的MPC不适用于全局大范围非线性规划的缺点;在轨迹跟踪控制方面,考虑预测时域内状态转移矩阵的时变特性,设计了LTVMPC算法对标称轨迹进行追踪,避免了存在不确定性时轨迹的重规划,从而降低在线计算量,保证算法在线自主实施,并且采用滚动优化的策略使算法对不确定性具有鲁棒性。由于规划层和控制层考虑的约束相同,因此规划的轨迹是可控、可达的。数字仿真表明,在燃料消耗和交会时间等方面,提出的方法均显著优于传统的MPC方法,相较于传统的MPC方法,新算法的交会时间减少50%左右,燃料消耗降低30%以上。     

基于反步推演法的多机编队队形重构控制

针对多无人机（UAV）集结期望的队形和达到稳态速度缓慢影响作战效率，基于反步推演法设计了一种协同导引控制律，用于解决多无人机快速队形重构和快速达到稳定状态。本文以一架虚拟长机为中心，3架僚机在3个顶点组成的三角形编队作为被控对象，且长机的速度方向作为编队的前行方向，僚机跟随长机编队飞行。采用长机导引机制，建立每架僚机的误差动力学模型；基于图论建立任意两架无人机之间的通讯模式，通过反步推演法得到多无人机编队队形保持的导引控制律。通过构建合理的Lyapunov函数，证明所提出的控制方法在编队集结和队形保持的有效性，同时将所提出的方法与模型预测控制（MPC）方法和拉普拉斯方法进行对比，更进一步验证所提方法有效性。仿真结果表明：每架无人机不仅能够按照期望的队形飞行，而且以动态响应快和稳态误差小收敛于虚拟长机的运动轨迹。     

Periodic event-triggered formation control for multi-UAV systems with collision avoidance

In this paper, periodic event-triggered formation control problems with collision avoidance are studied for leader–follower multiple Unmanned Aerial Vehicles (UAVs). Firstly, based on the Artificial Potential Field (APF) method, a novel sliding manifold is proposed for controller design, which can solve the problem of collision avoidance. Then, the event-triggered strategy is applied to the distributed formation control of multi-UAV systems, where the evaluation of the event condition is continuous. In addition, the exclusion of Zeno behavior can be guaranteed by the inter-event time between two successive trigger events have a positive lower bound. Next, a periodic event-triggered mechanism is developed for formation control based on the continuous event-triggered mechanism. The periodic trigger mechanism does not need additional hardware circuits and sophisticated sensors, which can reduce the control cost. The stability of the control system is proved by the Lyapunov function method. Finally, some numerical simulations are presented to illustrate the effectiveness of the proposed control protocol.     

基于扰动观测器的三相逆变电路改进型模型预测控制

经逆变电路并网的分布式发电系统中,为了解决传统模型预测控制(MPC)在电路参数变化时系统动态响应不佳的问题,在ABC三相坐标下,提出一种基于扰动观测器(DOB)的改进型模型预测控制(AdMPCDOB)方案,对被控电流进行两步预测,同时采用多重比例谐振(PR)调节器作为MPC的前馈补偿,消除预测电流的周期性误差,并提高对特定次谐波的抑制能力。考虑到电路参数变化和不确定扰动对系统造成的影响,采用电感电流扰动观测器对MPC输入信号进行精确观测。理论分析和仿真试验表明,所提改进型控制方案能够有效提高逆变器输出电能质量,降低并网电流总谐波畸变率(THD)。     

Distributed intelligent self-organized mission planning of multi-UAV for dynamic targets cooperative search-attack

This article studies the cooperative search-attack mission problem with dynamic targets and threats, and presents a Distributed Intelligent Self-Organized Mission Planning (DISOMP) algorithm for multiple Unmanned Aerial Vehicles (multi-UAV). The DISOMP algorithm can be divided into four modules: a search module designed based on the distributed Ant Colony Optimization (ACO) algorithm, an attack module designed based on the Parallel Approach (PA) scheme, a threat avoidance module designed based on the Dubins Curve (DC) and a communication module designed for information exchange among the multi-UAV system and the dynamic environment. A series of simulations of multi-UAV searching and attacking the moving targets are carried out, in which the search-attack mission completeness, execution efficiency and system suitability of the DISOMP algorithm are analyzed. The simulation results exhibit that the DISOMP algorithm based on online distributed down-top strategy is characterized by good flexibility, scalability and adaptability, in the dynamic targets searching and attacking problem.     

均等通信时滞下多UAV协同编队控制

多无人机(UAV)系统编队控制中,时滞是无法回避的问题,研究时滞对多UAV编队形成和系统稳定性的影响,具有重要理论价值。重点研究均等通信时滞下多UAV协同编队控制问题,并获得系统的稳定性条件。首先,设计具有均等通信时滞的协同编队控制律,得到多UAV编队系统的闭环时滞状态方程;在恒定均等时滞下,考虑到系统模型不确定性,基于线性矩阵不等式(LMI)理论得到系统的时滞依赖稳定性条件;最后,进行仿真实验,结果表明多UAV编队系统是稳定的,期望的编队队形能够形成并保持。     

Resilience optimization for multi-UAV formation reconfiguration via enhanced pigeon-inspired optimization

This paper develops a novel optimization method oriented to the resilience of multiple Unmanned Aerial Vehicle(multi-UAV) formations to achieve rapid and accurate reconfiguration under random attacks. First, a resilience metric is applied to reflect the effect and rapidity of multi-UAV formation resisting random attacks. Second, an optimization model based on a parameter optimization problem to maximize the system resilience is established. Third, an Adaptive Learning-based Pigeon-Inspired Optim...     

Cooperative formation control of multiple aerial vehicles based on guidance route in a complex task environment

In recent years, formation control of multi-agent has been a significant research subject in the field of cooperative control. However, previous works have mainly concentrated on formation control for simple point-mass model and linear model. In contrast, this paper presents a novel cooperative algorithm for multiple air vehicles formation control, which aims to devise a control strategy based on guidance route to achieve precisely coordinated formation control for a group of fixed-wing aircraft in a complex task environment. The proposed method introduces the leader-follower structure for effective organization of the multi-agent coordination. Moreover, the Partial Integrated Formation and Control (PIFC) is adopted to design the control law for Guidance-Route based Formation Control (GRFC). Additionally, the proposed approach designs two guidance-route generation strategies for two special situations to demonstrate the effectiveness of GRFC in complex task environments. Theoretical analysis reveals that the proposed control protocol for guidance command can ensure the overall stability and tracking accuracy of the system. Numerical simulations are performed to illustrate the theoretical results, and verify that the proposed approach can achieve coordinated formation control precisely in a complex task environment.     

多无人机系统协同多任务分配模型与仿真

针对多约束条件下的多无人机系统协同多任务分配问题(CMTAP),提出了利用多约束条件下的多车场车辆路径问题(MDVRPMC)对协同多任务分配问题进行建模,在设计基于Pareto最优的遗传模拟退火算法的基础上,对存在任务点动态时间窗口约束、任务类型约束、无人机任务能力约束等条件下的协同多任务分配问题进行了求解.实例仿真表...     

联合连通拓扑下多无人机编队控制

针对在联合连通拓扑条件下的多无人机系统,研究了具有二阶动力学系统模型的多无人机系统时变编队控制问题。基于一致性理论,设计了一致性控制器,将联合连通拓扑条件下的编队控制问题简化为低阶时间平均系统的渐进稳定性问题。利用Lyapunov函数证明了所设计的控制器能够实现编队控制,并利用线性矩阵不等式(LMI)方法给出了控制器的设计算法。在三维空间中,对多无人机系统进行了仿真,验证了所设计的一致性控制器能够使得多无人机系统在联合连通拓扑条件下形成时变编队。     

基于混合控制集预测控制的永磁同步电机电流脉动抑制方法

模型预测控制(MPC)技术近年来在高动态性能电机驱动系统中应用广泛。为了克服传统MPC技术中有限控制集(FCS)造成的稳态电流脉动问题,提出了一种基于混合控制集(MCS)预测控制的永磁同步电机(PMSM)电流脉动抑制方法。分析建立PMSM预测控制系统离散数学模型,并分析电压矢量精度与电流脉动之间的关联性；在此基础上,MCSMPC将电压源型逆变器有限的有效电压矢量数,扩展为多个以占空比形式存在的虚拟电压矢量,并基于上述虚拟电压矢量完成MPC优化问题在线求解；此外,考虑到MCSMPC系统的参数敏感性问题,对MCSMPC系统反馈噪声问题进行分析讨论。最后,搭建双15 kW PMSM对拖样机测试平台进行试验分析,分析内容包括MCS方法动态跟踪特性、电流脉动稳态效果。试验结果表明所提出的MCSMPC方法在保留了传统预测控制技术高动态响应的基础上,可有效降低PMSM稳态电流脉动幅度和运行噪声。     

基于罚函数序列凸规划的多无人机轨迹规划

多无人机（UAVs）轨迹规划是具有非线性运动约束和非凸路径约束的最优控制问题。引入序列凸规划思想,将非凸最优控制问题近似为一系列凸优化子问题,并利用成熟的凸优化算法进行求解,以更好地权衡最优性和时效性。首先,建立了多无人机协同轨迹规划的非凸最优控制模型。然后,利用离散化和凸近似方法将其转换为凸优化问题,包括对无人机运动模型的线性化,以及对威胁规避约束和无人机碰撞约束的凸化。同时,提出了一种离散点间的威胁规避方法,保证无人机在离散轨迹点间的飞行安全。在凸优化模型的基础上,给出了基于罚函数序列凸规划求解多无人机轨迹规划的具体框架。最后,通过数值仿真验证了方法的有效性,结果表明该方法在多机轨迹规划结果的最优性和时效性都要优于伪谱法,而且优势随编队数量的增加而增大。     

基于模型预测控制的永磁同步电机电流控制技术综述

永磁同步电机(PMSM)驱动控制技术的性能直接决定着整个电机驱动系统的性能。由于模型预测控制(MPC)技术可以对多输入多输出(MIMO)系统进行滚动优化控制,且容易施加约束,因此基于MPC的电机驱动技术正逐渐受到关注。回顾并总结了近年来国内外学者在基于MPC的PMSM电流控制方面所做的研究,并对现有技术中的基于单矢量、双矢量以及三矢量的电流控制技术进行了建模并进行了电流输出波形分析。最后综合比较了现有技术的优缺点,并讨论了现有技术尚存的一些问题和未来发展的主要方向。     

A new dynamic pushback control method for reducing fuel-burn costs: Using predicted taxi-out time

Long departure-taxi-out time leads to significant airport surface congestion, fuel-burn costs, and excessive emissions of greenhouse gases. To reduce these undesirable effects, a Predicted taxi-out time-based Dynamic Pushback Control(PDPC) method is proposed. The implementation of this method requires two steps: first, the taxi-out times for aircraft are predicted by the leastsquares support-vector regression approach of which the parameters are optimized by an introduced improved Firefly algorithm. Then, a dynamic pushback control model equipped with a linear gate-hold penalty function is built, along with a proposed iterative taxiway queue-threshold optimization algorithm for solving the model. A case study with data obtained from Beijing International airport(PEK) is presented. The taxi-out time prediction model achieves predictive accuracy within 3 min and 5 min by 84.71% and 95.66%, respectively. The results of the proposed pushback method show that total operation cost and fuel-burn cost achieve a 14.0% and 21.1%reduction, respectively, as compared to the traditional K-control policy.(3) From the perspective of implementation, using PDPC policy can significantly reduce the queue length in taxiway and taxi-out time. The total operation cost and fuel-burn cost can be curtailed by 37.2% and 52.1%,respectively, as compared to the non-enforcement of any pushback control mechanism. These results show that the proposed pushback control model can reduce fuel-burn costs and airport surface congestion effectively.     

Optimal trajectory and downlink power control for multi-type UAV aerial base stations

Unmanned Aerial Vehicles (UAVs) enabled Aerial Base Stations (UABSs) have been studied widely in future communications. However, there are a series of challenges such as interference management, trajectory design and resource allocation in the scenarios of multi-UAV networks. Besides, different performances among UABSs increase complexity and bring many challenges. In this paper, the joint downlink transmission power control and trajectory design problem in multi-type UABSs communication network is investigated. In order to satisfy the signal to interference plus noise power ratio of users, each UABS needs to adjust its position and transmission power. Based on the interactions among multiple communication links, a non-cooperative Mean-Field-Type Game (MFTG) is proposed to model the joint optimization problem. Then, a Nash equilibrium solution is solved by two steps: first, the users in the given area are clustered to get the initial deployment of the UABSs; second, the Mean-Field Q (MFQ)-learning algorithm is proposed to solve the discrete MFTG problem. Finally, the effectiveness of the approach is verified through the simulations, which simplifies the solution process and effectively reduces the energy consumption of each UABS.     

模型预测控制在永磁同步电机系统中的应用发展综述

随着数字技术的发展,模型预测控制(MPC)已经广泛应用于交流传动系统。首先介绍了经典MPC策略——有限控制集MPC和连续控制集MPC的原理。其次综述了多步预测控制、多矢量预测控制、具有参数鲁棒性的预测控制、广义模型预测、显式模型预测这些常见的改进MPC的研究现状,并提出相关研究思路。最后,根据MPC的应用需求和研究现状,展望了未来需要进一步深入和拓展的研究方向。