具有未知干扰的无人机鲁棒滑模飞行控制

研究无人机飞行稳定性控制问题,由于无人机飞行控制系统存在时变外部干扰,飞行过程中升阴比变化激烈,控制稳定性难度较大。利用滑模控制良好的鲁棒能力提出一种神经网络的鲁棒飞行控制方法。因神经网络有良好非线性逼近能力,可对无人机飞行系统中的不确定进行在线逼近,并将神经网络权值误差引入到权值的自适应律中用以改善系统的动态性能。利用神经网络的组合,设计无人机鲁棒滑模飞行控制器。控制器分为两部分,一部分是等效控制器,另一部分是滑模控制器,能有效减小系统的跟踪误差。最后将所设计的鲁棒滑模控制对无人机飞行姿态控制进行仿真。仿真结果表明,新方法能提高无人机的鲁棒飞行控制能力且能实现无人机姿态的精确跟踪和稳定性控制。     

数据链指挥下的战斗机H∞飞行控制器设计

基于神经网络对数据链指挥下的战斗机提出了鲁棒飞行控制器设计方案. 为了克服由于数据链的引入对战斗机飞行控制所带来的不利影响, 设计了基于 RBF 神经网络的鲁棒飞行控制器. 通过对神经网络参数在线调整, 使飞行控制系统能跟踪期望指令, 并满足给定的性能指标. 最后将所设计的飞行控制系统用于数据链指挥下的战斗机飞行控制, 仿真结果表明所设计的飞行控制系统是有效的.     

基于鲁棒自适应的无人直升机悬停控制

小型无人直升机凭借良好的机动特性,在军事和民用方面有着广泛的用途。针对小型无人直升机悬停模型,考虑模型参数不确定对无人机控制的影响,提出一种鲁棒自适应控制律,实现无人机控制系统对不确定扰动的抑制。首先,基于无人直升机线性化悬停模型设计滑模面,并结合标称系统控制的反馈增益,获得滑模面的设计参数;在此基础上,利用传统滑模趋近律设计方法设计控制器;为改善系统控制性能,设计基于自适应增益的趋近律,实现系统鲁棒自适应控制。其次,利用Lyapunov稳定理论对所设计的鲁棒自适应控制策略的稳定性进行分析说明。最后,通过与基于指数趋近律以及变速趋近律的两种滑模控制方法仿真对比,验证了所设计的控制方法的有效性和优越性。     

电液伺服系统的多滑模鲁棒自适应控制

针对一类参数与外负载非匹配不确定的非线性高阶系统,提出了一种基于逐步递推方法的多滑模鲁棒自适应控制策略.应用逐步递推的多滑模控制方法简化了高阶系统的控制问题,同时在自适应控制中加入鲁棒控制的方法,以消除不确定性对控制性能的影响.首先利用逐步递推方法与状态反馈精确线性化理论,得出确定系统的多滑模控制器设计方法;然后基于Lyapunov稳定性分析方法,给出不确定系统的参数自适应律,及鲁棒自适应控制器的设计方法.本文把该控制策略应用到电液伺服系统的位置跟踪控制中,仿真结果显示,该控制方法具有较强的鲁棒性及良好的跟踪效果.     

时滞不确定飞行系统神经网络非脆弱H∞控制

针对具有参数摄动和状态时延的时滞不确定飞行系统,提出了一种神经网络非脆弱H_∞控制方案。该方案将鲁棒H_∞控制和神经网络控制结合起来,利用径向基神经网络的非线性逼近能力,对飞行系统的非线性不确定项进行逼近。由线性矩阵不等式（LMI）设计系统标称部分的鲁棒控制器,然后利用神经网络的输出来消除系统控制输入中的不确定部分。Lyapunov稳定性分析中,综合考虑了系统参数摄动、时延和神经网络逼近误差的影响,并证明了在所设计的飞行控制器作用下,闭环系统的稳定性。仿真实例验证了提出的飞行控制方案的可行性和有效性。     

近空间飞行器纵向抗干扰轨迹控制研究

针对近空间飞行器( nearspace vehicle,NSV) 在高超音速飞行时,气动参数变化剧烈且容易受到外界干扰的特点,研究了NSV 纵向轨迹系统的干扰问题,提出了鲁棒自适应动态面的回馈递推控制方法。首先对高度非线性、高度复杂的NSV 的纵向运动的模型进行坐标变换,采用输入－输出反馈线性化方法,将其转化为仿射非线性模型; 然后通过一阶低通滤波器对控制器设计中的虚拟控制律进行估计,从而避免了对其求导带来的计算膨胀问题; 再结合神经网络逼近理论以及虚拟控制器中的鲁棒项,一起消除近空间飞行器的纵向系统中存在的参数摄动不确定和外界干扰。最后通过稳定性分析,表明了该方法在降低系统控制器复杂性的同时仍具有很好的鲁棒性。     

饱和输入下四旋翼无人机滑模轨迹跟踪控制

为解决四旋翼无人机在饱和输入下的轨迹跟踪控制问题,同时兼顾系统存在的参数不确定性和外部风力扰动影响,设计了一种改进的抗干扰自适应鲁棒滑模控制方法;基于六自由度架构,设计四旋翼无人机简化的系统模型,进而降低控制器设计的复杂程度;引入带有误差信号的滑模函数,设计带有误差信号的饱和补偿自适应控制律,同时增加鲁棒控制项,降低由于饱和输入问题带来的抖振影响,并减小参数不确定和外部风力扰动对系统稳定性的影响;系统模型与抗干扰自适应控制律相结合,形成了改进的抗干扰自适应鲁棒滑模控制策略,实现四旋翼无人机的位置轨迹和姿态轨迹的稳定跟踪;最后通过数值仿真与传统PD控制算法进行仿真比较,验证控制方法的有效性和优越性。     

AUV深度的模糊神经网络滑模控制

本文设计了一个模糊神经网络滑模变结构控制器，通过模糊神经网络对滑模控制律 的控制增益进行在线调整，并在海浪干扰条件下，用此控制器对AUV进行深度控制．仿真结 果验证了该智能控制方法具有很好的控制性能和鲁棒牲．     

一种混合神经网络飞行控制方法研究

讨论了一种基于神经网络控制的飞行控制方法。针对复杂非线性系统难以建立精确模型的特点,利用神经网络的任意非线性逼近能力进行控制器设计,首先应用神经网络在线辨识对象逆模型,进行控制系统反馈线性化;接着利用circle theorem（圆定理）设计线性PID鲁棒控制器,控制系统输出跟随系统输入,然后应用神经网路自适应逆方法设计混合控制器,最后以F-8飞机纵向飞行控制模态为研究对象进行仿真。仿真结果表明,该控制方法具有较强的自适应和抗干扰能力。     

固定翼无人机集群分布式固定时间编队跟踪控制

为实现固定翼无人机集群对理想编队构型的跟踪,研究了执行器存在未建模动态和不确定性的无人机集群系统的固定时间编队跟踪控制问题。首先,建立了无人机集群的三自由度运动学模型。其次,基于一种收敛时间独立于初始条件的固定时间终端滑模,设计了分布式固定时间编队协同跟踪控制器,利用控制器中的鲁棒项补偿外部扰动,通过Lyapunov稳定性理论证明了闭环系统的固定时间稳定性。最后,对所设计的控制器进行了数值仿真验证,仿真结果表明：所设计的控制器能够使无人机集群在固定时间内组成理想编队构型,同时系统具有鲁棒性。     

自主水下航行器的鲁棒自适应姿态控制算法

针对自主水下航行器（Autonomous Underwater Vehicle，AUV）在自动巡航任务中的姿态控制问题，提出了一种神经网络与滑模控制相结合的鲁棒自适应姿态控制算法。采用了RBF神经网络对AUV数学模型中的不确定项进行逼近，抑制了未建模动态和参数摄动的影响，进而基于反步法和滑模控制设计了姿态控制律，其中引入鲁棒项以克服外界干扰和神经网络逼近误差，并通过Lyapunov定理证明了控制系统的稳定性。将所设计的控制算法应用在AUV的姿态控制系统中进行数值仿真，验证了该控制算法的有效性和鲁棒性。     

Adaptive Sliding Mode Control for Re-entry Attitude of Near Space Hypersonic Vehicle Based on Backstepping Design

Combining sliding mode control method with radial basis function neural network (RBFNN), this paper proposes a robust adaptive control scheme based on backstepping design for re-entry attitude tracking control of near space hypersonic vehicle (NSHV) in the presence of parameter variations and external disturbances. In the attitude angle loop, a robust adaptive virtual control law is designed by using the adaptive method to estimate the unknown upper bound of the compound uncertainties. In the angular velocity loop, an adaptive sliding mode control law is designed to suppress the effect of parameter variations and external disturbances. The main benefit of the sliding mode control is robustness to parameter variations and external disturbances. To further improve the control performance, RBFNNs are introduced to approximate the compound uncertainties in the attitude angle loop and angular velocity loop, respectively. Based on Lyapunov stability theory, the tracking errors are shown to be asymptotically stable. Simulation results show that the proposed control system attains a satisfied control performance and is robust against parameter variations and external disturbances.      

Modeling and Robust Backstepping Sliding Mode Control with Adaptive RBFNN for a Novel Coaxial Eight-rotor UAV

This paper focuses on the robust attitude control of a novel coaxial eight-rotor unmanned aerial vehicles (UAV) which has higher drive capability as well as greater robustness against disturbances than quad-rotor UAV. The dynamical and kinematical model for the coaxial eight-rotor UAV is developed, which has never been proposed before. A robust backstepping sliding mode controller (BSMC) with adaptive radial basis function neural network (RBFNN) is proposed to control the attitude of the eightrotor UAV in the presence of model uncertainties and external disturbances. The combinative method of backstepping control and sliding mode control has improved robustness and simplified design procedure benefiting from the advantages of both controllers. The adaptive RBFNN as the uncertainty observer can effectively estimate the lumped uncertainties without the knowledge of their bounds for the eight-rotor UAV. Additionally, the adaptive learning algorithm, which can learn the parameters of RBFNN online and compensate the approximation error, is derived using Lyapunov stability theorem. And then the uniformly ultimate stability of the eight-rotor system is proved. Finally, simulation results demonstrate the validity of the proposed robust control method adopted in the novel coaxial eight-rotor UAV in the case of model uncertainties and external disturbances.      

Robust attitude control for tail‐sitter unmanned aerial vehicles in flight mode transitions

Tail‐sitter unmanned aerial vehicles (UAVs) can flight as rotorcrafts as well as fixed‐wing aircrafts, but it is hard to control the flight mode transition. The vehicle dynamics involves serious parametric uncertainties, highly nonlinear dynamics, and is easy to be affected by external disturbances, especially during the mode transition. This paper presents a robust control method for a kind of tail‐sitter UAVs to achieve the flight mode transition. The robust controller is proposed based on the state‐feedback control scheme and the robust compensation method. The proposed control method does not need to switch the coordinate system, the controller structure, or the controller parameters during the mode transitions. Theoretical analysis is given to guarantee the robustness stability of the designed flight control system. Numerical simulation results are presented to show the advantages of the proposed control method compared with the state‐feedback control method and the sliding mode control approach.     

Backstepping approach for design of PID controller with guaranteed performance for micro-air UAV

Flight controllers for micro-air UAVs are generally designed using proportional-integral-derivative (PID) methods, where the tuning of gains is difficult and time-consuming, and performance is not guaranteed. In this paper, we develop a rigorous method based on the sliding mode analysis and nonlinear backstepping to design a PID controller with guaranteed performance. This technique provides the structure and gains for the PID controller, such that a robust and fast response of the UAV (unmanned aerial vehicle) for trajectory tracking is achieved. First, the second-order sliding variable errors are used in a rigorous nonlinear backstepping design to obtain guaranteed performance for the nonlinear UAV dynamics. Then, using a small angle approximation and rigorous geometric manipulations, this nonlinear design is converted into a PID controller whose structure is naturally determined through the backstepping procedure. PID gains that guarantee robust UAV performance are finally computed from the sliding mode gains and from stabilizing gains for tracking error dynamics. We prove that the desired Euler angles of the inner attitude controller loop are related to the dynamics of the outer backstepping tracker loop by inverse kinematics, which provides a seamless connection with existing built-in UAV attitude controllers. We implement the proposed method on actual UAV, and experimental flight tests prove the validity of these algorithms. It is seen that our PID design procedure yields tighter UAV performance than an existing popular PID control technique.     

倾转式三旋翼无人机的自适应鲁棒容错控制

本文针对受到外界未知扰动和模型不确定性影响的倾转式三旋翼无人机,研究了其在尾部舵机发生堵塞故障时的容错控制问题.通过对倾转式三旋翼无人机姿态动力学特性的分析,将尾部舵机堵塞故障加入到力矩解算方程中.基于自适应反步法和非奇异终端滑模控制,提出了一种不需要故障诊断的鲁棒容错控制设计.利用基于Lyapunov的分析方法证明了闭环系统的稳定性,以及姿态误差的渐近收敛性质.通过在三旋翼无人机半实物仿真平台的实时实验以及与滑模控制器的对比,验证了该算法在无人机尾部舵机发生堵塞故障时,对姿态运动具有较好的控制效果.     

Robust dynamic surface trajectory tracking control for a quadrotor UAV via extended state observer

In this paper, we present an extended state observer–based robust dynamic surface trajectory tracking controller for a quadrotor unmanned aerial vehicle subject to parametric uncertainties and external disturbances. First, the original cascaded dynamics of a quadrotor unmanned aerial vehicle is formulated in a strict form with lumped disturbances to facilitate the backstepping design. Second, based on the separate outer‐ and inner‐loop control methodologies, the extended state observers are constructed to online estimate the unmeasurable velocity states and lumped disturbances existed in translational and rotational dynamics, respectively. Third, to overcome the problem of “explosion of complexity” inherent in backstepping control, the technique of dynamic surface control is utilized for trajectory tracking and attitude stabilization, and with the velocity and disturbance estimates incorporated into the dynamic surface control, a robust dynamic surface flight controller that guarantees asymptotic tracking in the presence of lumped disturbances is synthesized. In addition, the stability analysis is given, showing that the present robust controller can ensure the ultimate boundedness of all signals in the closed‐loop system and make the tracking errors arbitrarily small. Finally, comparisons and extensive simulations under different flight scenarios are performed to validate the effectiveness and superiority of the proposed scheme in accurate tracking performance and enhanced antidisturbance capability.     

Integrated multiple-model adaptive fault identification and reconfigurable fault-tolerant control for Lead-Wing close formation systems

This paper investigates the attitude and position tracking control problem for Lead-Wing close formation systems in the presence of loss of effectiveness and lock-in-place or hardover failure. In close formation flight, Wing unmanned aerial vehicle movements are influenced by vortex effects of the neighbouring Lead unmanned aerial vehicle. This situation allows modelling of aerodynamic coupling vortex-effects and linearisation based on optimal close formation geometry. Linearised Lead-Wing close formation model is transformed into nominal robust H-infinity models with respect to Mach hold, Heading hold, and Altitude hold autopilots; static feedback H-infinity controller is designed to guarantee effective tracking of attitude and position while manoeuvring Lead unmanned aerial vehicle. Based on H-infinity control design, an integrated multiple-model adaptive fault identification and reconfigurable fault-tolerant control scheme is developed to guarantee asymptotic stability of close-loop systems, error signal boundedness, and attitude and position tracking properties. Simulation results for Lead-Wing close formation systems validate the efficiency of the proposed integrated multiple-model adaptive control algorithm.     

垂直起降无人机基于图像的目标跟踪控制

针对安装有惯性测量单元和摄像机的低成本四旋翼无人机,研究无位置、速度、航向测量情况下的机动目标基于图像的跟踪控制方法.首先,结合无人机的动力学方程在图像空间中推导了系统的误差方程.其次,为克服无航向测量的问题,设计了一种位置控制器,使用图像矩作为反馈输入并输出油门和姿态指令.最后,针对缺少图像速度测量问题,设计了一种super-twisting滑模观测器和控制器,生成的期望姿态和拉力指令无颤振,并通过李雅普诺夫理论证明了控制系统的稳定性.最终无人机通过调整倾斜姿态实现了跟踪飞行,且避免了响应慢的航向调整.跟踪机动目标的仿真结果验证了所提出方法的有效性.