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.      

采用滑模观测器的六旋翼指令滤波鲁棒控制

针对包含复合干扰的六旋翼无人机鲁棒控制问题,提出了一种基于滑模观测器的指令滤波鲁棒控制方法。建立了包含复合干扰的六旋翼无人机位置和姿态的数学模型,并对位置回路设计了滑模控制律,从而解算出姿态指令;根据姿态角回路输出的虚拟控制律,设计了指令滤波器来抑制微分爆炸现象,并利用辅助滤波器补偿指令滤波的误差;在角速度回路鲁棒控制律中引入滑模观测器,对包括模型误差和外界扰动的复合干扰进行补偿,实现了六旋翼UAV的指令滤波鲁棒控制。仿真结果表明：提出的指令滤波鲁棒控制律与指令滤波自适应控制方法相比,在复合干扰下具有更优的稳定性、准确性和快速性,位置和姿态的最大误差分别仅为0.05?m和0.5°,滑模观测器的估计误差也仅为0.2 （°）/s,能够在更短的时间内实现对六旋翼UAV位移和姿态的鲁棒控制。     

共轴八旋翼无人飞行器的偏航静态抗饱和补偿控制

设计了一种共轴八旋翼无人飞行器,与四旋翼飞行器相比,其具有更大的驱动能力、更强的带载能力和一定的冗余能力．首先,建立了飞行器的动力学模型．针对共轴八旋翼飞行器偏航运动能力比俯仰、滚转运动能力弱,偏航容易出现执行器饱和现象的问题,从实际工程出发提出了基于线性自抗扰控制器的静态抗饱和补偿器．线性自抗扰算法易于工程调节,能够实时估计与补偿外界扰动．静态抗饱和补偿器不增加系统阶次,有效抑制偏航执行器饱和．利用李亚普诺夫稳定理论证明了基于线性自抗扰控制器的静态抗饱和偏航控制系统的稳定性．最后,通过共轴八旋翼飞行器的仿真实验与原型机比较实验验证了算法的有效性与鲁棒性．原型机实验结果表明：在室内固定干扰下,执行器退出饱和的最长时间约为4 s,偏航角误差收敛到±0.085 rad;在室外变干扰下,执行器退出饱和的最长时间约为9 s,偏航角误差收敛到±0.127 rad．基于线性自抗扰控制器的静态抗饱和补偿器在外界干扰情况下能够有效地抑制执行器饱和,具有良好的偏航控制性能与强鲁棒性．     

四旋翼无人飞行器的轨迹跟踪与滑模事件驱动控制

四旋翼飞行器作为一个典型的欠驱动的系统,具有强耦合、非线性等特性.针对飞行器外部干扰、和通信资源受限条件下的轨迹跟踪控制问题,进行滑模事件驱动控制方法的研究.首先,分析动力学特性,通过时间尺度分解方法将系统解耦成位置子系统和姿态子系统.其次,将位置子系统转化为严格反馈形式,设计反步滑模控制器,实现位置轨迹稳定跟踪;针对姿态子系统存在时变有界扰动及通信受限,设计滑模事件驱动控制律,在抑制干扰的同时实现对虚拟姿态跟踪指令的跟踪.根据Lyapunov分析方法证明了所设计控制器的稳定性,并通过理论分析证明闭环控制系统不会出现Zeno现象.最后,仿真结果验证了滑模事件驱动控制律在存在外部扰动和通信受限时四旋翼无人飞行器轨迹跟踪的鲁棒性.     

基于扩张状态观测器和反步滑模法的四旋翼无人机轨迹跟踪控制

为了解决欠驱动四旋翼无人机（UAV）在实际飞行中存在的外界干扰问题,同时提高在系统参数摄动情况下的精确轨迹跟踪效果,设计了一种基于扩张状态观测器（ESO）和积分型反步滑模算法的飞行控制策略。首先,根据系统的半耦合特性和严反馈结构特点,采用反步法设计姿态内环和位置外环控制器;然后,将抗干扰能力较强的滑模控制融入其中,使得系统的鲁棒性得到增强;接着,为了减小系统的稳态误差,引入积分环节;最后,利用ESO实时估算出系统的内、外总扰动并对控制量进行补偿。通过Lyapunov稳定判据,可以说明该系统是一个全局渐进稳定的系统,并通过仿真分析验证了所提控制方法的有效性和鲁棒性。     

四旋翼无人机鲁棒自适应姿态控制

 四旋翼无人机的姿态控制是自主飞行控制的核心,针对四旋翼姿态易受外界环境干扰和内部参数摄动等不确定性影响的问题,设计了一种鲁棒自适应反步控制器,以提高四旋翼的鲁棒性。建立了四旋翼完整的姿态运动模型,并将其转化为含有广义不确定性的多输入多输出非线性系统。根据该系统满足严格反馈的结构特点,设计了反步控制器; 针对系统中存在的外部干扰和内部参数摄动等不确定性,引入了一类鲁棒自适应函数来抵消该不确定性对系统的影响; 采用非线性跟踪微分器估计虚拟控制量的微分信号,减小了反步控制器设计中普遍存在的“计算膨胀”问题; 通过构造Lyapunov 函数证明闭环系统是稳定且指数收敛的。仿真结果表明,所设计控制器具有良好的控制效果和鲁棒性。     

Robust adaptive controller with disturbance observer for vehicular radar servo system

In vehicular radar servo system, parameter variations of the executive motor and external disturbance uncertainties have great effects on the position tracking precision of the system. In this paper, a robust adaptive controller with disturbance observer is designed for vehicular radar servo system, which combines the merits of disturbance observer, adaptive backstepping method and sliding mode control. The system is modeled, and a disturbance observer is employed to observe and compensate for the unknown uncertainties. Adaptive backstepping method is used to design the sliding model controller to guarantee the global stability of the overall system. Simulation results show that the proposed robust adaptive controller has good performance in position tracking and enhances the robustness of vehicular radar servo system while observing the uncertainties precisely and quickly.     

A New Robust Adaptive Neural Network Backstepping Control for Single Machine Infinite Power System With TCSC

For a single machine infinite power system with thyristor controlled series compensation (TCSC) device, which is affected by system model uncertainties, nonlinear time-delays and external unknown disturbances, we present a robust adaptive backstepping control scheme based on the radial basis function neural network (RBFNN). The RBFNN is introduced to approximate the complex nonlinear function involving uncertainties and external unknown disturbances, and meanwhile a new robust term is constructed to further estimate the system residual error, which removes the requirement of knowing the upper bound of the disturbances and uncertainty terms. The stability analysis of the power system is presented based on the Lyapunov function, which can guarantee the uniform ultimate boundedness (UUB) of all parameters and states of the whole closed-loop system. A comparison is made between the RBFNN-based robust adaptive control and the general backstepping control in the simulation part to verify the effectiveness of the proposed control scheme.      

Adaptive Nonsingular Terminal Sliding Mode Control Design for Near Space Hypersonic Vehicles

This paper presents an adaptive nonsingular terminal sliding mode approach for the attitude control of near space hypersonic vehicles (NSHV) in the presence of parameter uncertainties and external disturbances. Firstly, a novel nonsingular terminal sliding surface is developed and its finitetime convergence is analyzed. Then, an adaptive nonsingular terminal sliding mode control law is proposed, which is chattering free. In the proposed approach, all parameter uncertainties and external disturbances are lumped into one term, which is estimated by an adaptive uncertainty estimation for eliminating the boundary requirement needed in the conventional control design. Subsequently, stability of the closed-loop system is proven based on Lyapunov theory. Finally, the proposed approach is applied to the attitude control design for NSHV. Simulation results show that the proposed approach attains a satisfactory performance in the presence of parameter uncertainties and external disturbances.      

Observer-based adaptive sliding mode control for robust tracking and model following

This paper presents a methodological approach to design an observer-based adaptive sliding mode control to realize the problem of robust tracking and modeling following for a class of uncertain linear systems. Only partial information of the system states is known. Based on Lyapunov stability theorem, it will be shown that the proposed scheme guarantees the stability of closed-loop system and achieves zero-tracking error in the presence of parameter uncertainties and external disturbances. The proposed observer-based adaptive sliding mode control scheme can be implemented without requiring a priori knowledge of upper bounds on the norm of the uncertainties and external disturbances. This scheme assures robustness against system uncertainties and disturbances. Both the theoretical analysis and illustrative example demonstrate the validity of the proposed scheme.     

自适应神经网络四旋翼无人机有限时间轨迹跟踪控制

针对带有模型不确定性和未知外部干扰的四旋翼无人机轨迹跟踪控制问题,提出一种基于径向基(radial basis function, RBF)神经网络的自适应全局快速终端滑模控制方法,确保系统对期望轨迹的有限时间跟踪。该方法考虑到全局快速终端滑模控制在实际应用中的适应性和抖振问题,利用RBF神经网络替代等效控制量,以神经网络的在线学习能力补偿系统内部的不确定性和未知的外部干扰,有效地降低了系统的抖振;根据Lyapunov方法导出的自适应律在线调整神经网络权值,以保证闭环系统的稳定性。通过一系列仿真算例和飞行实验验证了该方法的有效性与可行性,结果表明:该控制方法相对于滑模控制的抖振更小,具有更好的收敛性和抗干扰能力,同时对模型的参数摄动具有更强的鲁棒性。     

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.     

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。     

四旋翼无人机姿态系统的非线性容错控制设计

本文研究了四旋翼无人机执行器发生部分失效时的姿态控制问题.通过分析其动力学特性,将执行器故障以乘性因子加入系统模型,得到执行器故障情况下四旋翼无人机的姿态动力学模型.在同时存在未知外部扰动和执行器故障的情况下,设计了一种基于自适应滑模控制的容错控制器.利用基于Lyapunov的分析方法证明了所设计控制器的渐近稳定性.在四旋翼无人机实验平台上进行了实验,验证了该算法对存在未知外部扰动和执行器部分失效时四旋翼无人机的姿态控制具有较好的鲁棒性.     

基于快速终端滑模面的两旋翼飞行器有限时间姿态控制

针对带有模型不确定和外部干扰的两旋翼飞行器,提出一种基于快速终端滑模面的有限时间自适应姿态控制方法,保证两旋翼飞行器对期望姿态角度的有限时间跟踪。构造快速终端滑模面,并设计分段连续函数避免滑模变量求导产生的奇异值问题。在此基础上,设计有限时间姿态控制器,并设计系统不确定上界的自适应更新律,抵消模型不确定性和外部干扰的影响。经李雅普诺夫方法证明滑模变量、姿态角误差、角速度误差等闭环信号最终一致有界,且有限时间收敛至平衡点邻域,收敛时间与系统状态变量初始值有关。最后,采用了矩形波和 曲线作为设定信号,设计相应的跟踪实验,并在两旋翼飞行器平台上验证所提控制方法的有效性,且分析双曲正切函数对系统控制输入影响,经实验测试其可减少系统颤振现象。     

Robust and adaptive variable structure output feedback control of uncertain systems with input nonlinearity

This brief proposes a robust control algorithm for stabilization of a three-axis stabilized flexible spacecraft in the presence of parametric uncertainty, external disturbances and control input nonlinearity/dead-zone. The designed controller based on adaptive variable structure output feedback control satisfies the global reaching condition of sliding mode and ensures that the system state globally converges to the sliding mode. A modified version of the proposed control law is also designed for adapting the unknown upper bounds of the lumped uncertainties and perturbations. The stability of the system under the modified control law is established. Numerical simulations show that the precise attitude pointing and vibration suppression can be accomplished using the derived robust or adaptive controller.     

Bio-inspired backstepping adaptive sliding mode control for parallel mechanism with actuation redundancy

This paper presents a bio-inspired backstepping adaptive sliding mode control strategy for a novel 3 degree of freedom (3-DOF) parallel mechanism with actuation redundancy. Based on the kinematic model and the dynamic model, a sliding mode controller is designed to assure the tracking performance, and an adaptive law is introduced to approximate the system uncertainty including parameters’ variation, external disturbances and un-modeled part. Furthermore, a bio-inspired model is introduced to solve the inherent chattering problem of sliding mode control and provide a chattering free control. The simulation and experimental results testify that the proposed bio-inspired backstepping adaptive sliding mode control can achieve better performance (the tracking accuracy, robustness, response speed, etc.) than the conventional slide mode control.     

四旋翼无人机L1 自适应块控反步姿态控制器设计

针对四旋翼无人机的姿态控制问题,提出一种L1自适应块控反步控制方法.将四旋翼姿态运动模型转化为一类多输入多输出不确定非线性系统的形式;根据该系统严格反馈的结构特点,对外回路设计了块控反步控制器;针对内回路存在的外部干扰和内部参数摄动等不确定性,引入L1自适应控制思想补偿其影响.稳定性分析表明闭环系统内所有信号一致有界.仿真和姿态稳定实验验证了所提控制策略的有效性和鲁棒性.     

基于神经网络的无人机姿态自适应控制仿真

针对四旋翼无人机姿态控制中模型不完整、部分参数和扰动不确定的问题,提出了一种基于神经网络的自适应控制方法,采用RBF神经网络对无人机姿态动力学模型中不确定和扰动部分进行学习,设计了以类反步法为基础,包含反馈控制和神经网络控制的自适应控制器,实现了对未知动态的准确逼近,解决了传统控制方法中过于依赖精确模型的问题。同时设计了神经网络的权值自适应律,实现了控制过程中的在线学习和调整,并且通过李雅普诺夫方法证明了闭环系统的稳定性。仿真结果表明,在存在较大扰动的情况下,上述控制器可得到很好的控制效果,可以实现误差的快速收敛,具有较好的鲁棒性和自适应性。