基于自抗扰控制的共轴直升机姿态控制律设计

以某型共轴双旋翼无人直升机作为研究对象,对无人机返航进场与降落着舰阶段的抗扰动问题进行研究。针对海上着舰环境下的外界干扰与不确定性问题,选用自抗扰控制器设计共轴直升机姿态控制律,以提高共轴直升机对外部扰动的抗扰性能。针对自抗扰控制律调整参数过多问题,设计改进粒子群算法进行自动参数优化。仿真结果表明:改进粒子群算法能够显著提高参数整定速度,且精度更高;优化参数后的自抗扰控制律在强干扰环境下具备良好的动态响应与鲁棒性。     

倾转双旋翼无人机风场扰动下的建模和控制器设计

针对可倾转双旋翼无人机容易受外界风场扰动和模型不确定性影响的问题,设计了基于自抗扰控制技术的飞行姿态和PID轨迹组合控制。通过分析阵风对无人机姿态的影响,利用牛顿-欧拉法推导出有风条件下的双旋翼动力学方程;然后,通过Dryden大气紊流模型建立了无人机所处的风场环境;最后,用自抗扰控制器对无人机的动力学模型作干扰补偿,并采用粒子群算法进行参数整定,解决自抗扰技术参数繁多、人工调节复杂的问题。仿真结果表明,所设计的自抗扰姿态控制和PID轨迹组合控制器能有效抑制紊流风扰动,实现稳定飞行。     

无人机参数优化自抗扰编队保持控制器设计

针对无人机编队飞行中的队形保持问题,采用结合粒子群算法的自抗扰控制技术对控制器进行设计,使响应过程具有良好的动态品质和较小的稳态误差。建立三维空间内基于相对误差的无人机编队飞行模型,并在此基础上设计了跟踪微分器、扩展状态观测器和非线性状态反馈自抗扰控制器,采用粒子群算法对控制器中部分参数进行优化整定。仿真结果表明,设计的结合粒子群算法的改进自抗扰控制器具有良好的控制性能,能以较高的精度实现无人机编队保持任务。     

基于QPSO算法的自抗扰控制器设计及参数整定

提出了一种基于量子粒子群(QPSO)的自抗扰控制器(ADRC)设计及参数整定方法.利用自抗扰控制器抗于扰能力强、鲁棒性好、对模型参数变化适应能力强的特点,设计了自抗扰纵向飞行控制器以提高飞行控制性能.针对所设计的自抗扰控制器参数多,需要依靠专家经验或试凑进行整定的问题,应用QPSO算法进行控制器参数整定.仿真结果表明,...     

教与学优化算法在风力机变桨自抗扰控制中的应用

随着风力机的发展越来越智能化,除了要求风力机的运行可靠性,还要求其有稳定的输出功率。为了使风力机变桨控制器在运行过程中具有优良的动态品质,提出了一种自抗扰控制(ADRC)的变桨距控制方案,但ADRC也存在参数多、整定难度大这一明显缺点。为此,将教与学算法应用到ADRC的整定过程中,实现了参数的自动整定。仿真结果验证了通过教与学算法自动整定ADRC参数的可行性,与传统PID控制器相比,整定后的ADRC能较好地满足风力机变桨距控制要求,有效维持了风力机输出功率的稳定性。     

受油机指定时间姿态稳定控制

针对空中加油任务受油阶段受油机姿态稳定的需求，考虑受油机时变转动惯量、不确定转动惯量和风干扰的影响，提出了一种受油机指定时间姿态稳定控制策略。首先，建立了含时变转动惯量、内部不确定性和外部扰动下的受油机姿态动力学和运动学模型；其次，为了提高姿态稳定的快速性和确定性，使用指定时间稳定理论作为基础，设计了受油机姿态稳定鲁棒滑模控制器。与现有受油机控制策略相比，所提出的控制器优势在于可确保受油机在指定的时间内实现姿态稳定。同时，收敛时间显式存在于控制器参数中，设计者可根据任务需求和执行机构的物理限制，在允许的范围内自由调控。李雅普诺夫稳定性分析和数字仿真结果表明，该控制策略具有良好的控制性能，可实现指定时间稳定。     

空中加油变质量建模与干扰补偿控制

为抑制空中加油给飞机带来的变质量影响,将质量的变化等效为对飞机的干扰力和干扰力矩,建立了变质量飞机的一般动力学模型,并经过小扰动线性化得到飞机带干扰的纵向状态方程.采用比例积分观测器(PIO)对干扰进行观测,基于观测值设计了补偿控制器以实现对变质量干扰的抑制.以受油机为例进行计算机仿真,结果表明,该模型有效地反映了变质量受油机的动力学特性,干扰补偿控制器能够实现对变质量受油机的精确控制.     

一种方向舵-螺旋桨联用的全翼式太阳能无人机横航向控制方法

以全翼式太阳能无人机（UAV）为研究对象,针对无副翼状态下横航向控制问题,提出了采用方向舵偏转和螺旋桨差动进行横航向控制的方法。首先,分析了两个螺旋桨条件下该类无人机横航向的稳定性与操纵性;然后,基于线性自抗扰控制（LADRC）理论,以方向舵偏和螺旋桨差动为控制输出,分别设计了滚转角控制器和偏航角控制器。最后,结合滚转角控制和偏航角控制的优缺点,在L1轨迹跟踪算法的基础上设计了方向舵和螺旋桨联用的直线轨迹跟踪器。仿真结果表明：所设计的控制器具有较好的控制性能、鲁棒性和抗风性。同时参数整定过程相对简单,并采用实际可测的物理量,为进一步工程应用提供参考。     

面向无人机空中加油紧密编队的鲁棒控制方法

针对无人机空中加油紧密编队系统鲁棒控制问题，提出了一种基于障碍函数的自适应干扰观测器的分布式鲁棒编队控制方法。对固定翼无人机外环动力学模型进行转换，构造了具有非匹配和匹配扰动的二阶多体系统简化模型，并基于障碍函数设计了相应通道的自适应干扰观测器；利用邻机状态信息定义了相应的一致误差函数，在此基础上，基于编队系统通信拓扑结构，引入干扰补偿机制，开发了空中加油无人机紧密编队系统分布式鲁棒控制器，以实现理想的异构无人机编队跟踪控制性能。基于Lyapunov稳定性理论，分析了闭环系统的稳定性和收敛性。最后，通过将所提方法应用在由不同型号的1架加油机和2架受油机构成的编队系统上，进行数值仿真验证。所得到仿真结果与理论分析一致，验证了设计的干扰观测器和控制器的有效性。     

基于极值搜索的四旋翼无人机自抗扰控制

为了提高四旋翼无人机飞行过程中的抗干扰能力,提出了基于极值搜索算法的自抗扰控制技术。首先,建立了四旋翼无人机非线性数学模型;然后,设计了基于极值搜索的自抗扰控制器,对无人机的位置和姿态进行控制;同时,设计扩张状态观测器估计系统内部及外部总扰动,对系统扰动进行补偿。仿真结果表明,所提方法不仅能抑制外界干扰,而且能显著改善飞行控制系统的瞬态性能和稳态性能。     

A multi-strategy pigeon-inspired optimization approach to active disturbance rejection control parameters tuning for vertical take-off and landing fixed-wing UAV

In this paper, Active Disturbance Rejection Control(ADRC) is utilized in the pitch control of a vertical take-off and landing fixed-wing Unmanned Aerial Vehicle(UAV) to address the problem of height fluctuation during the transition from hover to level flight. Considering the difficulty of parameter tuning of ADRC as well as the requirement of accuracy and rapidity of the controller, a Multi-Strategy Pigeon-Inspired Optimization(MSPIO) algorithm is employed. Particle Swarm Optimization(PSO), Gen...     

无人机空中加油自主会合的制导与控制

针对无人机空中加油的自主会合问题,进行了相应制导律和非线性控制器的设计。通过改进的带角度约束的三维比例制导律实现对航向角的控制,以协调转弯的方式将航迹角指令转化为姿态角指令。基于无人机六自由度的动力学模型,针对无人机的姿态控制,采用时标分离的方法设计了慢子系统和快子系统,并对这两个子系统分别进行动态逆控制设计。同时,基于滑模控制的方法设计了满足自主会合要求的速度控制律。在保证无人机飞行稳定的基础上,实现了对控制和制导指令的精确跟踪。仿真结果表明,所设计的制导律和控制律能够实现无人机空中加油的自主会合,具有良好的动态特性。     

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.     

A New Hybrid Control Scheme for an Integrated Helicopter and Engine System

A new hybrid control scheme is presented with a robust multiple model fusion control(RMMFC) law for a UH-60 helicopter and an active disturbance rejection control(ADRC) controller for its engines.This scheme is a control design method with every subsystem designed separately but fully considering the couplings between them.With three subspaces with respect to forward flight velocity,a RMMFC is proposed to devise a four-loop reference signal tracing control for the helicopter,which escapes the closed-loop system from unstable state due to the extreme complexity of this integrated nonlinear system.The engines are controlled by the proposed ADRC decoupling controller,which fully takes advantage of a good compensation ability for unmodeled dynamics and extra disturbances,so as to compensate torque disturbance in power turbine speed loop.By simulating a forward acceleration flight task,the RMMFC for the helicopter is validated.It is apparent that the integrated helicopter and engine system(IHES) has much better dynamic performance under the new control scheme.Especially in the switching process,the large transient is significantly weakened,and smooth transition among candidate controllers is achieved.Over the entire simulation task,the droop of power turbine speed with the proposed ADRC controller is significantly slighter than with the conventional PID controller,and the response time of the former is much faster than the latter.By simulating a rapid climb and descent flight task,the results also show the feasibility for the application of the proposed multiple model fusion control.Although there is aggressive power demand in this maneuver,the droop of power turbine speed with an ADRC controller is smaller than using a PID controller.The control performance for helicopter and engine is enhanced by adopting this hybrid control scheme,and simulation results in other envelope state give proofs of robustness for this new scheme.     

Simulation Environment for Machine Vision Based Aerial Refueling for UAVs

The design of a simulation environment is described for a machine vision (MV)-based approach for the problem of aerial refueling (AR) for unmanned aerial vehicles (UAVs) using the USAF refueling method. MV-based algorithms are implemented within the proposed scheme to detect the relative position and orientation between the UAV and the tanker. Within this effort, techniques and algorithms for the visualization the tanker aircraft in a virtual reality (VR) setting, for the acquisition of the tanker image, for the feature extraction (FE) from the acquired image, for the feature matching (FM) of the features, for the tanker-UAV pose estimation (PE) have been developed and extensively tested in closed-loop simulations. Detailed mathematical models of the tanker and UAV dynamics, refueling boom, turbulence, wind gusts, and tanker's wake effects, along with the UAV docking control laws have been implemented within the simulation environment. This paper also presents the results of a study relative to the use of passive markers versus feature extraction for the problem of estimating in real time the UAV-tanker relative position and orientation vectors.     

一种航空发动机多变量自抗扰解耦控制律设计

研究了航空发动机多变量解耦控制律设计问题。提出了一种用于航空发动机多回路控制的多变量自抗扰解耦控制算法:首先通过静态解耦算法实现多变量耦合系统的静态解耦,而后通过ADRC非线性扩张观测器的补偿控制实现各回路的动态解耦,最终实现复杂多变量耦合系统的解耦控制。以某涡扇发动机非线性部件级实时数学模型为被控对象,基于上述多变量自抗扰解耦控制算法设计了发动机中间状态以上多变量控制律。在全包线内,与基于增广LQR控制方法设计发动机闭环系统,进行了对比研究。数字仿真结果表明,前者使得发动机闭环系统具有更好的指令跟踪和多回路解耦能力。     

Neural adaptive control for a ground experiment of the space proximity operation in a six-degree-of-freedom micro-gravity simulation system

In this study, a neural adaptive controller is developed for a ground experiment with a spacecraft proximity operation. As the water resistance in the experiment is highly nonlinear and can significantly affect the fidelity of the ground experiment, the water resistance must be estimated accurately and compensated using an active force online. For this problem, a novel control algorithm combined with Chebyshev Neural Networks (CNN) and an Active Disturbance Rejection Control (ADRC) is proposed. Specifically, the CNN algorithm is used to estimate the water resistance. The advantage of the CNN estimation is that the coefficients of the approximation can be adaptively changed to minimize the estimation error. Combined with the ADRC algorithm, the total disturbance is compensated in the experiment to improve the fidelity. The dynamic model of the spacecraft proximity maneuver in the experiment is established. The ground experiment of the proximity maneuver that considers an obstacle is provided to verify the efficiency of the proposed controller. The results demonstrate that the proposed method outperforms the pure ADRC method and can achieve close-to-real-time performance for the spacecraft proximity maneuver.     

无人机软管式自主空中加油视觉导航技术

近年来,机器视觉技术的迅速发展给无人机自主空中加油提供了一种新思路,克服了传统GPS动态适应能力差的不足。基于双目视觉,提出了一种实用的无人机自主空中加油导航方案,并提出了一种利用深度学习进行检测跟踪配合几何约束进行位姿计算的技术。根据加油机和受油机距离的不同,将导航分为远端导航和近端导航。全程对加油锥套进行检测和跟踪,并在近端进行锥套位姿计算。实验结果表明,该算法满足加油的鲁棒性要求,稳定跟踪锁定目标距离范围大于40m。当与目标距离小于10m时,距离误差优于±10cm。说明本文提出的方法具有较强的应用前景。