基于自适应神经模糊推理算法的无人机电推进燃料电池供气系统性能优化

本文针对某无人机基于聚合物交换膜燃料电池和锂离子电池的混合动力电推进系统的应用,研究开发了一种基于自适应神经模糊推理系统的电源管理系统控制技术,以控制混合动力电力推进系统,同时优化燃料电池供气系统的性能。本文以所建立的某无人机混合电推进系统数学模型为研究对象,研究了燃料电池电流与燃料电池供气系统压缩机功率之间的关系,建立了燃料电池电流与最佳压缩机功率关系的参考模型。在参考模型的基础上,引入自适应控制器来优化燃料电池供气系统的性能。基于自适应神经模糊推理系统的控制器将压缩机的实际运行功率动态调整到参考模型中定义的最佳值。自适应控制器的在线学习和训练能力用来辨识燃料电池电流的非线性变化,并产生压缩机电机电压的控制信号,以优化燃料电池供气系统的性能。在Matlab 仿真环境中开发了质子交换膜燃料电池和锂离子混合动力电推进系统模型并对所设计的控制器进行了仿真分析,结果表明基于自适应神经模糊推理系统的控制器为燃料电池供气系统压缩机性能优化提供了一种新颖而全面的途径,使燃料电池供气系统获得最大净功率输出。将燃料电池系统的净功率输出与最佳压缩机功率和恒定压缩机功率进行了比较,结果表明优化的压缩机功率配置比恒定的压缩机功率配置节能2.62%。同时,燃料电池自适应神经模糊推理系统控制器优化了燃料电池供气系统的能量利用。     

基于神经网络的双馈感应发电机滑模控制

针对不平衡电网下双馈感应发电机运行不佳的问题,将神经网络控制和二阶滑模控制相结合构成的神经网络滑模控制器运用到双馈风力发电机的直接功率控制中。设计了二阶滑模控制器,二阶滑模能够有效地削弱传统滑模控制的抖振;接着,设计了径向基神经网络对系统的不确定部分进行逼近;最后,基于李雅普诺夫稳定性定理推导了神经网络权值更新律,证明了控制系统的稳定性。仿真结果表明所设计控制策略能对有功、无功功率及其定子电流进行有效控制,削弱了传统滑模控制中的抖振。     

Control and stabilization of nonlinear uncertain elastic roboticarm

An approach is presented to the control of an uncertain nonlinear flexible robot arm (PUMA-type) with three rotational joints. The third link is assumed to be elastic. A torquer control law, which is a function of the trajectory error, is derived for controlling the joint angles. The knowledge of the system dynamics is not required for the derivation of the controller. This controller includes a reference model to generate command joint angle trajectories, and a dynamic system in the feedback path which requires only joint angle and rate for feedback. The torquer controller asymptotically decouples the elastic dynamics into two subsystems, representing the transverse vibration of the elastic link in two orthogonal planes. For the damping of the elastic vibration, a force control law using modal velocity feedback is synthesized. Simulation results are presented to show that the combination of the torque and force control law accomplishes reference joint angle trajectory tracking and elastic mode stabilization despite the uncertainty in the system     

基于分数阶滑模的风力发电机直接功率控制

为了改善双馈风力发电系统的控制性能,将分数阶滑模控制应用于双馈风力发电机的直接功率控制系统中。将分数阶微积分引入到滑模控制中,构成分数阶滑模控制器,利用分数阶的遗传衰减特性削弱滑模控制的抖振。推导了应用于双馈风力发电系统的分数阶滑模控制律,并用李雅普诺夫稳定性定理证明了系统的稳定性。所提出的分数阶滑模控制系统省略了电流环控制,简化了控制结构,实现了有功功率和无功功率的有效控制。仿真与试验结果显示出所用分数阶滑模控制策略的有效性,同时表明该系统削弱了传统滑模控制中存在的抖振。     

Power Generation?Some Tasks and Goals

Generating capacity additions planned for the 1970's and beyond include a high percentage of nuclear power plants. The light water reactor is the dominant type of commercial nuclear reactor being installed during the 1970's. Advancement to more efficient designs, such as the high-temperature gas-cooled reactor, is expected around 1980. Prospects for the breeder reactor and fusion energy systems dictate a major research and development effort. Attempts to control effluents from fossil-fired plants have resulted in a sharp increase in the use of low sulfur fuels. Research and development efforts are being intensified in an attempt to obtain a breakthrough in methods of stack emission control.     

基于超螺旋二阶滑模的双馈风力发电机控制策略

传统双馈风力发电机(DFIG)控制存在抖振现象,容易造成系统的不稳定。为了减轻控制抖振现象,提高控制的稳定性,在分析了DFIG动态特性的基础上,建立了DFIG的数学模型,设计了超螺旋二阶滑模控制器,并研究了突变风的情况下滑模控制器的控制性能。通过MATLAB/Simulink工具进行了仿真验证。仿真结果证明:滑模控制器具有良好的最优转矩跟踪能力和无功调节能力,与一般的控制方法相比鲁棒性较强,转子控制电压连续,控制产生的抖振可以大幅减轻,系统的稳定性大大提升。     

Adaptive recurrent-neural-network control for linear inductionmotor

In this study an adaptive recurrent-neural-network controller (ARNNC) is proposed to control a linear induction motor (LIM) servo drive. First, the secondary flux of the LIM is estimated with an adaptive flux observer on the stationary reference frame and the feedback linearization theory is used to decouple the thrust force and the flux amplitude of the LIM. Then, an ARNNC is proposed to control the mover of the LIM for periodic motion. In the proposed controller, the LIM servo drive system is identified by a recurrent-neural-network identifier (RNNI) to provide the sensitivity information of the drive system to an adaptive controller. The backpropagation algorithm is used to train the RNNI on line. Moreover, to guarantee the convergence of identification and tracking errors, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the RNNI and the optimal learning rate of the adaptive controller. The effectiveness of the proposed control scheme is verified by both the simulated and experimental results. Furthermore, the advantages of the proposed control system are indicated in comparison with the sliding mode control system     

辅助动力装置系统进气风门位置控制设计与研究

辅助动力装置系统进气风门位置控制子系统用于地面和空中控制辅助动力装置进气风门的打开和关闭,通常由控制器,作动机构(电动作动器和连杆机构)组成。辅助动力装置系统进气风门位置控制子系统的设计是辅助动力装置控制系统设计的一部分,和辅助动力装置进气风门设计、进气风门气动载荷计算分析及辅助动力装置进气道设计同步进行,相互影响。对某型飞机的辅助动力装置系统进气风门位置控制设计方案进行了介绍,该风门位置控制采用单独的风门控制器,降低了辅助动力装置FADEC(Full Authority Digital Electrical Controller,全权限数字电子控制器,简称FADEC)软硬件设计复杂度,简化了接口设计;并且设计了一种新型辅助动力装置系统进气风门作动机构,该作动机构安装/拆卸方便,可达性好;具有力矩放大功能,且该机构可调节,能输出不同大小的力矩。该进气风门位置控制子系统经过型号验证,对后续型号研制具有较强的指导性。     

航空发动机多变量模糊滑模变结构模型跟踪控制

针对现代航空发动机是一个具有不确定性的强非线性系统,结合滑模变结构控制和模糊逻辑系统的优点,提出了一种模糊滑模变结构模型跟踪控制方法。采用比例积分型切换超平面设计滑模变结构控制系统,使用模糊逻辑系统自适应调节切换增益,得到某涡扇发动机的模糊滑模变结构模型跟踪控制器。数字仿真结果表明,所设计的控制器不但能使被控对象较好地跟踪参考模型,消除抖振现象,而且对系统的不确定性具有不变性,保证了被控系统在整个控制阶段都具有较强的鲁棒性。     

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.     

Adaptive control and stabilization of elastic spacecraft

This work treats the question of large angle rotational maneuver and stabilization of an elastic spacecraft (spacecraft-beam-tip body configuration). It is assumed that the parameters of the system are completely unknown. An adaptive control law is derived for the rotational maneuver of the spacecraft. Using the adaptive controller, asymptotically decoupled control of the pitch angle of the space vehicle is accomplished, however this maneuver causes elastic deformation of the beam connecting the orbiter and tip body. For the stabilization of the zero dynamics (flexible dynamics), a stabilizer is designed using elastic mode velocity feedback. In the closed-loop system including the adaptive controller and the stabilizer, reference pitch angle trajectory tracking and vibration suppression are accomplished. Simulation results are presented to show the maneuver capability of the control system     

Predictor-based model reference adaptive roll and yaw control of a quad-tiltrotor UAV

An attempt is made to apply modern control technology to the roll and yaw control of a rudderless quad-tiltrotor Unmanned Aerial Vehicle (UAV) in the latter part of the flight mode transition, where aerodynamic forces on the tiltrotor’s wings start to take effect. A predictor-based adaptive roll and yaw controller is designed to compensate for system uncertainties and parameter changes. A dynamics model of the tiltrotor is built. A Radial-Basis Function (RBF) neural network and offline adaptation method are used to reduce flight controller workload and cope with the nonlinearities in the controls. Simulations are conducted to verify the reference model response tracking and yaw-roll control decoupling ability of the adaptive controller, as well as the validity of the offline adaptation method. Flight tests are conducted to confirm the ability of the adaptive controller to track different roll and yaw reference model responses. The decoupling of roll and yaw controls is also tested in flight via coordinated turn maneuvers with different rotor tilt angles.     

Robust aeroelastic control of flapped wing systems using a sliding mode observer

The performance of a robust control strategy applied to a three-degrees-of-freedom (3-DOF) flap-wing aeroelastic system impacted by a pressure pulse in the subcritical flight speed regime is investigated. The goal of its implementation is to suppress flutter instability and reduce the vibrational level in the subcritical flight speed range. To this end, the linear quadratic Gaussian (LQG) control methodology in conjunction with a sliding mode observer (SMO) are used. Comparisons with the counterpart results obtained via implementation of LQG controller with conventional Kalman filter (KF) are also provided and pertinent conclusions are outlined.     

新一代RLV再入弹道模糊控制研究

基于阻力与能量的关系,优化并设计了再入参考弹道。利用模糊控制技术,在线推导了轨道控制器的各项参数。仿真结果表明,基于阻力与能量关系剖面设计的再入轨道满足各项约束,成功地导引飞行器到达TAEM终端,而模糊PD控制器则能排除干扰,实时跟踪参考弹道。     

基于TTP协议的飞机配电系统通信仿真研究

针对飞机配电系统典型单元,建立基于时间触发的四节点控制与通信模型;实现系统典型节点的信号交互,并进行实时控制与仿真分析。以供电系统处理机(PSP)、发电机控制器和汇流条控制器的组合(GCUs+BPCU),某个负载管理中心(ELMC)、以及多个固态功率控制器(SSPCs)为四个典型节点,在研究模型节点内部控制与逻辑关系、建立消息时刻表的基础上,仿真了配电系统在双发、单发、应急情况下,汇流条和负载的自动管理。仿真结果表明,信号严格按照确定的消息时刻表传送,通信具有较高的实时性和可靠性。研究结果为时间触发协议在航空器领域的应用提供了参考。     

基于TMS 320F28069的无人机舵系统控制器设计与实现

本文设计并实现了一种基于TMS 320F28069的小型无人机舵系统控制器。取消了原机舵系统的模拟通讯方式,改用RS485接口与飞控计算机进行通讯。在分析舵系统工作原理基础上,基于TMS 320F28069控制精度高、功耗低、外设丰富的优点,进行了软、硬件结构的模块化设计。实验表明,本文设计的控制器可靠性强、稳定性好、控制精度高,可以消除外界干扰对舵系统的影响。     

Fixed-time adaptive model reference sliding mode control for an air-to-ground missile

This paper addresses the fixed-time adaptive model reference sliding mode control for an air-to-ground missile associated with large speed ranges, mismatched disturbances and un-modeled dynamics. Firstly, a sliding mode surface is developed by the tracking error of the state equation and the model reference state equation with respect to the air-to-ground missile. More specifically,a novel fixed-time adaptive reaching law is presented. Subsequently, the mismatched disturbances and the un-modeled dynamics are treated as the model errors of the state equation. These model errors are estimated by means of a fixed-time disturbance observer, and they are also utilized to compensate the proposed controller. Therefore, the fixed-time controller is obtained by an adaptive reaching law and a fixed-time disturbance observer. Closed-loop stability of the proposed controller is established. Finally, simulation results including Monte Carlo simulations, nonlinear six-DegreeOf-Freedom(6-DOF) simulations and different ranges are presented to demonstrate the efficacy of the proposed control scheme.     

基于模糊动态逆的飞机直接升力控制

针对飞机的直接升力控制问题,设计了一种将动态逆控制器和模糊控制器结合使用的新方法,并在系统参数优化过程中对ITAE准则进行改进。在ITAE准则的基础上,引入对正向误差的积分从而约束系统响应的超调量。通过对单纯直接升力控制模态和垂直平移控制模态的仿真实验,表明文章的模糊动态逆控制器能够实现具备良好鲁棒性的直接升力控制;通过对比模糊动态逆控制器和动态逆控制器的仿真结果,表明模糊动态逆控制器的动态性能和稳态性能优于动态逆控制器。     

RFNN control for PMLSM drive via backstepping technique

A robust fuzzy neural network (RFNN) control system is proposed in this study to control the position of the mover of a permanent magnet linear synchronous motor (PMLSM) drive system to track periodic reference trajectories. First, an ideal feedback linearization control law is designed based on the backstepping technique. Then, a fuzzy neural network (FNN) controller is designed to be the main tracking controller of the proposed RFNN control system to mimic an ideal feedback linearization control law, and a robust controller is proposed to confront the shortcoming of the FNN controller. Moreover, to relax the requirement for the bound of uncertainty term, which comprises a minimum approximation error, optimal parameter vectors and higher order terms in Taylor series, an adaptive bound estimation is investigated where a simple adaptive algorithm is utilized to estimate the bound of uncertainty. Furthermore, the simulated and experimental results due to periodic reference trajectories demonstrate that the dynamic behaviors of the proposed control systems are robust with regard to uncertainties.     

相对失控翻滚目标悬停的自适应模糊滑模控制

研究了含有系统不确定性和外部干扰的追踪器相对失控翻滚目标悬停的六自由度耦合控制问题。首先,在追踪器本体坐标系中建立了非线性的六自由度耦合的一体化动力学模型,将悬停控制问题转化为相对位置和相对姿态控制问题。再基于模糊逼近原理设计了一种自适应的模糊滑模控制器,该控制器能够有效克服系统的模型不确定性和外部干扰的影响,并能消除传统的抖振问题。由Lyapunov方法导出了模糊自适应律并证明了闭环系统的稳定性。数值仿真验证了所提的自适应模糊滑模控制器的有效性。