基于滑模与控制分配的六旋翼飞行器容错控制

针对六旋翼飞行器的执行机构失效故障, 提出一种基于滑模和控制分配方法的容错控制方案; 采用了分层设计结构, 将基本控制律设计与控制分配律分为两个独立的部分, 针对系统建模不确定性与外界干扰, 设计滑模控制律, 提高了系统的鲁棒性, 运用控制分配方法, 在飞行器发生失效故障甚至卡死故障时, 在线调解控制分配矩阵, 减小故障机构对整体飞行性能的影响。仿真结果验证了所提容错控制的正确性和有效性。     

神经网络动态逆在歼击机安全着陆中的控制

给出了基于神经网络动态逆的自适应跟踪控制方法,用以解决飞机着陆过程中的复杂非线性和出现舵机故障的情况.应用神经网络直接对非线性系统故障模型求逆,使得所设计的逆系统能够包含故障信息,克服了传统的控制设计中将过程模型线性化,从而将不可忽视的非线性关系用线性关系代替或忽略的弊端.对由于建模误差、不确定性因素等引起的非线性系统逆误差,通过自组织模糊小脑模型关节控制器(SOFCMAC)神经网络在线进行修正.并在此基础上对3个通道分别设计了参考模型和线性控制器,以实现对伪线性系统进行跟踪控制.通过将这种方法用于某型歼击机在着陆过程中发生平尾卡死故障控制的过程仿真,验证了该方法的可行性.     

四旋翼飞行器模型参考自适应容错控制

针对四旋翼飞行器执行器发生故障同时存在扰动时的姿态控制问题,设计了一种基于模型参考自适应(MRAC)的容错控制器。通过分析四旋翼飞行器的动力学特性,将执行器故障以加性因子的形式加入系统模型中,得到执行器故障下的动力学模型。所设计控制器的自适应律由参考模型和执行器故障下的模型的误差信号驱动,可实现较好的姿态控制。利用Lyapunov的分析方法证明了所设计控制器的渐进稳定性,最后在Matlab下进行了仿真实验。仿真结果表明,系统能克服扰动且在执行器故障下可良好地跟踪参考姿态角,验证了该算法的可行性和有效性。     

带有指令滤波器的模糊反步自适应控制

针对固定翼飞行器在飞行控制过程中存在的状态受限以及执行器物理特性限制等问题,提出了一种带有指令滤波器的反步模糊自适应控制器。首先,建立带有误差项的MIMO严反馈系统;其次,建立模糊系统在线逼近子系统误差项,基于Backstepping法对每一个子系统设计虚拟控制律;再次,考虑状态受限和执行器的物理特性(包括幅值和速率受限),将设计的虚拟控制律通过引入幅值、速率和带宽限制的指令滤波器,对滤波误差项进行补偿;最后,运用Lyapunov稳定性定理证明了闭环系统有界且跟踪误差指数收敛于零的一个邻域内。仿真结果表明,设计的控制器具有很强的稳定性和鲁棒性。     

模糊滑模控制在CAV再入标准轨迹制导中的应用

针对CAV再入标准轨迹制导问题,提出了一种新的基于模糊滑模的纵向轨迹制导方案,根据CAV状态模型的特点和模糊系统设计要求,对其纵向运动学方程进行了简化处理,设计了模糊逼近+切换控制律补偿的控制器,利用模糊系统的万能逼近特性来对倾侧角增量进行逼近,用切换控制率来补偿逼近误差。仿真验证了该方案能够实现对纵向轨迹的精确跟踪,且具有较好的鲁棒性和实时性。     

重装空投过程的有限时间收敛自适应滑模控制

针对带有不确定性且不确定性边界未知的重装空投过程飞机运动系统,提出了一种有限时间收敛增益自适应滑模控制律。该控制律由标称控制和补偿控制两部分构成,前者用于获得有限时间的收敛性能,后者用于克服不确定性,提高系统鲁棒性。采用自适应方法在线近似补偿控制器的增益,解决了控制增益选取依赖于不确定性边界的问题,并有效抑制了滑模控制的抖振现象。理论分析表明,该方法能够保证飞机速度和俯仰角的跟踪误差有限时间收敛。仿真验证了控制方法的有效性和优越性。     

新型终端滑模在光电稳定平台中的应用

为了提高光电伺服稳定平台的跟踪精度,针对系统中干扰的影响提出一种新型终端滑模控制算法。首先,提出一种新型终端滑模干扰观测器的设计方法,实现对系统中干扰的快速估计和实时补偿。其次,设计新型终端滑模控制器来提高系统的跟踪精度,结合有限时间收敛和自适应控制的思想,对切换增益进行在线调整,有效地抑制了滑模控制中的抖振问题,使系统状态能够在有限时间内快速地收敛到所设计的滑模面上,并对未估计干扰进行精细化补偿。最后利用Lyapunov理论证明控制系统的稳定性。实验结果表明:该控制策略保证了光电跟踪系统视轴对运动目标的跟踪精度,在0.05 Hz时误差小于0.002,在2 Hz时误差小于0.034,增强了系统的鲁棒性。     

超机动飞行快回路的自适应模糊滑模控制

针对超机动飞行快回路的不确定非线性模型,提出了一种利用自适应模糊滑模控制器算法。在所得的最终控制信号中,采用模糊逻辑系统来逼近未知系统函数和开关项;所设计的鲁棒自适应律用来减小逼近误差,从而有效降低抖振。仿真结果表明,所设计的控制律能在过失速机动条件下控制飞机跟踪指令飞行,确保系统具有良好的动态和稳态性能,而且控制器具有很强的鲁棒性。     

基于最优控制的神经网络自适应飞行控制

针对非线性动态逆控制鲁棒性差的缺点,综合最优控制和神经网络控制,提出一种自适应非线性控制策略。首先采用非线性动态逆进行基本控制律设计,然后对由于建模误差或舵面故障等因素导致的动态逆误差,利用神经网络进行在线补偿,根据最优控制理论得到神经网络权值的自适应律,并基于Lyapunov直接法证明该自适应律的稳定性和收敛性。针对某飞机的仿真结果表明,在存在较大逆误差的情况下,所设计的控制系统具有良好的鲁棒性。     

考虑约束的卫星抗扰反步姿态控制

针对具有不确定性、外干扰及饱和约束的卫星非线性姿态跟踪问题, 将约束反步法与状态观测器相结合, 提出分块自适应抗扰反步控制器。卫星模型由修正罗德里格参数进行描述。利用带参数投影的非线性扩张状态观测器对时变的“总干扰”项进行在线估计补偿, 以提高反步控制器的鲁棒性。在设计反步控制器时, 引入指令滤波器和修正跟踪误差信号以施加系统状态和执行器的饱和限制, 同时较容易获得虚拟控制导数, 并且放宽了干扰估计律投影算子的投影集范围。Lyapunov理论证明了闭环系统在非线性阻尼的作用下输入-状态稳定。对比仿真表明, 与传统自适应反步法相比, 所提出的控制器具有更高的姿态跟踪性能和干扰估计精度。     

Design and implementation of a fault tolerant controller for EMS systems

Conventional EMS (electromagnetic suspension) systems are susceptible to instability problems, and could even break down upon failures of the air-gap sensor or the accelerometer. Therefore, in order to improve EMS performance, a fault tolerant controller and a fault detection and isolation (FDI) algorithm are presented in this work. The fault tolerant controller is an extended version of the linear fault tolerant controller designed for known actuator or sensor failures, and it adopts the LMI-based H_∞ control for a class of nonlinear systems. The fault detection algorithm employs fuzzy inference. The merits of the proposed control scheme have been verified by the experiments with a single-axis two-magnet suspension system subjected to failures of the actuator or the sensors.     

Fractional delay compensated discrete-time SMC for networked control system

Time-varying network induced delay in the communication channel severely affects the performance of closed loop network control systems. In this paper, a novel idea of compensating the fractional time varying communication delay in the sliding surface is presented. The fractional time delay in the sensor to controller and controller to actuator channel is approximated using the Thiran approximation technique to design the sliding surface. A discrete-time sliding mode control law is derived using the proposed surface that compensates fractional time delay in sensor to controller and controller to actuator channels for uncertain network control systems. The sufficient condition for closed loop stability of the system is derived using the Lyapunov function. The efficacy of the proposed strategy is supported by the simulation results.     

Modeling, control and experimental validation of a novel actuator based on shape memory alloys

The paper presents the development of a mechanical actuator using a shape memory alloy with a cooling system based on the thermoelectric effect (Seebeck–Peltier effect). Such a method has the advantage of reduced weight and requires a simpler control strategy as compared to other forced cooling systems. A complete mathematical model of the actuator was derived, and an experimental prototype was implemented. Several experiments are used to validate the model and to identify all parameters. A robust and nonlinear controller, based on sliding-mode theory, was derived and implemented. Experiments were used to evaluate the actuator closed-loop performance, stability, and robustness properties. The results showed that the proposed cooling system and controller are able to improve the dynamic response of the actuator.     

舵机加载系统的最优控制

分析了舵机加载系统的结构原理,建立其简化模型,并研究了最优控制在舵机加载系统中的应用.舵机加载系统中加载设备和舵机系统间存在强烈的耦合,针对舵机运动对加载系统跟踪性能的影响,应用前馈控制对舵机运动扰动进行补偿.为进一步提高系统性能,引入最优控制.根据舵机加载系统的特点,比较研究了3种不同的最优控制方法.常用的是工程近似最优控制方法,为进一步消除稳态误差,可以引入积分控制.与上述方法不同,对加载系统进行最优控制时,采用积分策略增广系统状态方程,并引入期望衰减度定义性能指标构造系统的最优控制.仿真结果表明,在文中所构造的最优控制和前馈控制的复合控制下,加载系统具有相对最佳的跟踪性能.     

FPGA-Based Adaptive Backstepping Sliding-Mode Control for Linear Induction Motor Drive

A field-programmable gate array (FPGA)-based adaptive backstepping sliding-mode controller is proposed to control the mover position of a linear induction motor (LIM) drive to compensate for the uncertainties including the friction force. First, the dynamic model of an indirect field-oriented LIM drive is derived. Next, a backstepping sliding-mode approach is designed to compensate the uncertainties occurring in the motion control system. Moreover, the uncertainties are lumped and the upper bound of the lumped uncertainty is necessary in the design of the backstepping sliding-mode controller. However, the upper bound of the lumped uncertainty is difficult to obtain in advance of practical applications. Therefore, an adaptive law is derived to adapt the value of the lumped uncertainty in real time, and an adaptive backstepping sliding-mode control law is the result. Then, an FPGA chip is adopted to implement the indirect field-oriented mechanism and the developed control algorithms for possible low-cost and high-performance industrial applications. The effectiveness of the proposed control scheme is verified by some experimental results. With the adaptive backstepping sliding-mode controller, the mover position of the FPGA-based LIM drive possesses the advantages of good transient control performance and robustness to uncertainties in the tracking of periodic reference trajectories.     

基于DBFNN的推设及其在电力系统励磁控制中的应用

本文采用后推设计算法为一类严格反馈系统设计了基于方向基函数神经网络(DBFNN)的自适应控制器.在后推算法中的每步都引入一积分型的Lyapunov函数来设计一个虚拟控制器,并在最后一步为闭环系统综合设计了神经网络控制器.网络权值的调整基于所选择的Lyapunov函数,于是设计方案能保证整个闭环系统是最终一致有界的.把所设计控制方案用于带有未知参数和外部干扰的电力系统励磁控制中.仿真结果表明了所设计控制器的有效性.     

Fault-tolerant vibration control in a networked and embedded rocket fairing system

Active vibration control using piezoelectric actuators in a networked and embedded environment has been widely applied to solve the rocket fairing vibration problem. However, actuator failures may lead to performance deterioration or system dysfunction. To guarantee the desired system performance, the remaining actuators should be able to coordinate with each other to compensate for the damaging effects caused by the failed actuator in a timely manner. Further, in the networked control environment, timing issues such as sampling jitter and network-induced delay should be considered in the controller design. In this study, a timing compensation approach is implemented in an adaptive actuator failure compensation controller to maintain the fairing system performance by also considering the detrimental effects from real-time constraints. In addition, time-delay compensation in the networked control system is discussed, which is able to reduce damaging effects of network-induced delays.     

基于DBFNN的后推设计及其在电力系统励磁控制中的应用

本文采用后推设计算法为一类严格反馈系统设计了基于方向基函数神经网络(DBFNN)的自适应控制器．在后推算法中的每步都引入一积分型的Lyapunov函数来设计一个虚拟控制器，并在最后一步为闭环系统综合设计了神经网络控制器．网络权值的调整基于所选择的Lyapunov函数，于是设计方案能保证整个闭环系统是最终一致有界的．把所设计控制方案用于带有未知参数和外部干扰的电力系统励磁控制中．仿真结果表明了所设计控制器的有效性．     

Design and implementation of industrial neural network controller using backstepping

In this paper, a novel neural network (NN) backstepping controller is modified for application to an industrial motor drive system. A control system structure and NN tuning algorithms are presented that are shown to guarantee stability and performance of the closed-loop system. The NN backstepping controller is implemented on an actual motor drive system using a two-PC control system developed at The University of Texas at Arlington. The implementation results show that the NN backstepping controller is highly effective in controlling the industrial motor drive system. It is also shown that the NN controller gives better results on actual systems than a standard backstepping controller developed assuming full knowledge of the dynamics. Moreover, the NN controller does not require the linear-in-the-parameters assumption or the computation of regression matrices required by standard backstepping.     

BTT导弹自适应反演控制律设计与仿真

针对倾斜转弯(BTT)导弹控制中的多变量强耦合问题,研究了一种适用于BTT导弹的反演算法,以实现自动驾驶仪的自适应解耦控制。根据BTT导弹控制的基本特性,建立导弹的非线性控制模型,并将其转化为适合于反演设计的反馈块模型。在此模型上,基于反演的非线性控制系统综合设计方法,加入自适应神经网络逼近系统中存在的不确定性,利用Lyapunov稳定性定理推导了自适应调节律,设计了导弹控制律。通过仿真验证了该设计方法的有效性和可行性,该控制器能够实现控制解耦目的,且对指令信号跟踪效果良好。