航空发动机性能退化缓解控制技术

开展了航空发动机性能退化缓解控制技术的相关研究.性能退化缓解控制通过内环控制转速,外环控制推力,实现了对航空发动机的控制.该控制方式既有传统控制方式的优点,又能像直接推力控制那样实现对推力的直接控制,达到提高发动机控制自主性的目的.为了验证性能退化缓解控制的有效性,以某型双轴混排涡扇发动机为对象进行了控制系统设计及仿真验证.仿真结果表明了性能退化缓解控制技术的有效性和可行性.      

航空发动机推力衰退缓解的神经网络控制

针对航空发动机气路部件性能退化导致的推力下降问题,提出一种基于变增量线性规划(LP)优化 神经网络控制方法用于航空发动机推力衰退缓解控制。该方法通过内环控制转速和发动机压比,外环修正发动机指令信号以缓解发动机推力衰退。其中内环非线性自回归滑动平均(NARMA-L2)转速控制器由神经网络训练得到;外环指令修正回路利用变增量LP优化方法调整发动机指令信号。以某型小涵道比涡扇发动机为对象进行仿真验证,结果表明,在4组仿真条件下,设计的控制方法在保证性能退化的发动机不超限的条件下使推力衰退至少缓解了46.5%,验证了该方法的有效性。     

涡扇发动机分路式多变量PI解耦控制

根据基于模型的智能数字发动机控制的特点,针对某型涡扇发动机,以直接推力控制模式为例,研究了涡扇发动机分路式多变量PI解耦控制.并采用了零点配置技术保证解耦控制器的稳定性,通过稳态解耦矩阵对涡扇发动机进行解耦达到分路控制的目的.利用SISO系统PI控制器设计方法确定每个环路的最优PI控制器.仿真结果表明,该算法具有较好的动态和静态解耦性能.      

一种基于模型的涡扇发动机容错控制策略

针对民用涡扇发动机,研究了一种基于模型的航空发动机容错控制策略.在发动机原有闭环控制系统的基础上,该策略对发动机各控制量进行容错控制修正,达到缓和甚至消除故障对控制系统影响的目的.针对民用涡扇发动机常见的高压压气机和高压涡轮性能退化故障,提出了以恢复发动机运行性能为目的的性能指标,通过遗传算法离线计算得到容错控制计划表,并利用插值计算实现在线容错控制.经仿真验证,该策略控制性能良好、实现简单.结果表明:在确保各温度限制不超标的情况下,受影响较大的增压级喘振裕度恢复至故障发生前的水平,同时风扇与高压压气机喘振裕度的降幅不超过5%,推力的降幅不超过3%.      

基于模态切换的航空发动机容错控制

综合了航空发动机控制和故障诊断方法,设计了基于模态切换的任务级和发动机级模式的容错控制系统.任务级模式在发动机部件故障时,通过切换控制策略和控制模式达到恢复或降低发动机性能的要求;发动机级模式在控制回路失效时,根据故障情况切换到容错控制回路,从而保证发动机继续正常工作.数字仿真结果表明:在稳态或加速过程中出现部件故障时,容错控制系统都能够100%恢复发动机的推力;在发动机中间、慢车和节流状态下,当压气机转速控制回路失效时,容错控制系统能够在3s内平稳切换到风扇转速控制回路.      

民用涡扇发动机起动过程改进的分段组合控制计划研究与试验

针对某型民用涡扇发动机,将n-dot闭环控制算法应用于涡扇发动机起动过程,设计了涡扇发动机起动过程开环与闭环分段组合的控制计划,仿真表明:涡扇发动机起动过程采用n-dot闭环控制算法在初始阶段会出现参数摆动的情况.为此,基于油气比作控制变量进行消喘的原理,对n-dot闭环控制算法进行了改进,以消除起动初始阶段的参数摆动.试验结果表明:改进后的分段组合的控制计划能够有效抑制参数摆动现象;并且,对于不同大气条件的起动测试,起动过渡态过程高压转子转速的数值差距不大于1.0%,延时不超过0.9s;而对于不同飞行状态的起动测试,起动过渡态过程高压转子转速的数值差距不大于1.0%,延时不超过1.2s,具有良好的稳定性和重复性.      

基于UIO的航空发动机执行机构故障诊断

为了提高航空发动机诊断过程中对噪声干扰和模型参数变化的鲁棒性,应用UIO(Unknown Input Ob-server)理论估计发动机动态系统的工作状态,通过干扰正交投影弱化外界干扰对状态估计的影响,处理了航空发动机执行机构的故障诊断问题。对航空发动机执行机构设计一组UIO观测器,其中每个UIO用于监测估计对应执行机构的故障偏移量,计算系统输出理论估计值与发动机实际测量信号间的残差数据,分析残差队列的幅值特性,实现航空发动机执行机构系统的故障检测和隔离。某型涡扇发动机上的实验结果表明,与Kalman滤波算法相比,UIO诊断方法更能鲁棒地检测和隔离出执行机构故障。     

涡扇发动机性能退化缓解控制与推力设定

为了补偿性能退化发动机的推力损失,减轻飞行员工作负担,提高推进系统的自动化程度,开展了涡扇发动机性能退化缓解控制(EPDMC)研究。针对某型涡扇发动机部件级模型设计了具备稳态控制、加/减速过渡态控制和极限保护等功能的基准控制器;在此基础上设计了外环推力控制回路,给出1种在多参数约束下的推力设定方法,并设计了合理的切换逻辑确保内外环控制器能协调工作。MATLAB/Simulink下的仿真结果表明:该智能改进控制系统架构可以在保证发动机安全工作的前提下,通过合理地设定期望推力,最大程度地补偿推力损失,维持油门杆角度和推力的对应关系近似不变。     

基于核方法的航空发动机推力估计器设计

鉴于实现航空发动机的直接推力控制需要高精度及高可靠性的推力估计器,基于核方法,提出了结合全局核k-means聚类与鲁棒最小二乘支持向量回归机的推力估计器设计方案,通过核诱导的隐性映射将原始输入数据映射到特征空间,使数据样本特征信息被提取并放大,具有更好的可分性.在每个聚类内设计推力子估计器,用鲁棒代价函数代替最小二乘代价函数,增强了推力估计器的整体鲁棒性.通过对涡扇发动机的仿真试验表明,本推力估计器设计方法能够满足直接推力控制需要,与其它方法相比,在估计精度及鲁棒性上存在一定优势.     

基于神经网络逆控制的发动机直接推力控制

首次将动态神经网络逆控制用于航空发动机直接推力控制。为了有效消除由于神经网络逆模型构造误差(即神经网络逆模型不可能完全逼近航空发动机的逆模型)而产生的稳态误差和解决航空发动机推力不易测量的困难,分别设计了积分补偿器和推力估计器,从而实现航空发动机直接推力控制。飞行包线内数字仿真结果表明,此控制方案具有良好的动静态性能、精度高、跟踪快。     

用于涡轴发动机传感器故障诊断的未知输入观测器干扰矩阵估算

针对处理涡轴发动机控制系统传感器故障诊断问题时有着良好性能的未知输入观测器展开研究,分析了与系统建模误差相关的干扰矩阵对未知输入观测器观测出的系统状态准确度的影响,提出了一种基于全阶状态观测器的涡轴发动机系统模型干扰矩阵的估算方法,该方法对某一固定工况下的干扰矩阵估算十分准确,使得根据此干扰矩阵设计出的未知输入观测器可以完全不受系统建模误差的影响.在此基础上,又给出了一种适应于更广泛工况范围的干扰矩阵近似方法,使得变工况下的未知输入观测器一样不受建模误差的影响.仿真结果表明:基于该方法估算出的干扰分布矩阵可以保证观测器具有良好的性能,在变工况下对发动机输出量估计残差的二范数不大于2.      

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.     

基于性能退化缓解的双发推力匹配控制

为了解决退化程度不同而导致的双发推力不匹配问题,首先介绍基于发动机性能退化缓解控制的双发推力匹配控制系统(在发动机基准控制系统的基础上添加外围控制回路实现对推力的间接控制,并通过设定双发共同遵循的期望推力减少由于发动机退化程度不同造成的双发推力差异),之后对该系统所涉及的推力设定、参数估计、转速指令修正等关键技术进行研究,最后在MATLAB/Simulink环境下进行仿真验证,结果表明:该系统可以在发动机参数限制范围内合理地设定双发共同遵循的期望推力,减少推力差异,满足自动协调双发推力匹配的设计要求。     

涡扇发动机进口畸变条件下的优化控制规律

提出了一种满足涡扇发动机不同进口畸变要求的控制规律.在涡扇发动机低进口畸变条件下通过提高增压比来提高性能,在高进口畸变条件下降低增压比并辅助导叶角调节以提高发动机裕度.根据不同的进口畸变条件下的裕度要求,提出了带边界缓冲区的差分进化算法(DEBZ),在包线内优化获取发动机增压比及导叶角控制规律.结合模型、控制器和控制规律完成涡扇发动机全包线仿真.仿真结果表明:采用该控制规律在畸变指数为2的条件下,涡扇发动机推力性能能够提高8%以上,而在畸变指数为8的条件下,涡扇发动机的可用裕度可以满足稳定要求,同时推力性能能够提高1%以上.      

Non-affine parameter dependent LPV model and LMI based adaptive control for turbofan engines

The precise control of turbofan engines thrust is an important guarantee for an aircraft to obtain good flight performance and a challenge due to complex nonlinear dynamics of engines and time-varying parameters. The main difficulties lie in the following two aspects. Firstly, it is hard to obtain an accurate kinetic model for the turbofan engine. Secondly, some model parameters often change in different flight conditions and states and even fluctuate sharply in some cases. These variable parameters bring huge challenge for the turbofan engine control. To solve the turbofan engine control problem, this paper presents a non-affine parameter-dependent Linear Parameter Varying(LPV) model-based adaptive control approach. In this approach, polynomial-based LPV modeling method is firstly employed to obtain the basis matrices, and then the Radial Basis Function Neural Networks(RBFNN) is introduced for the online estimation of the non-affine model parameters to improve the simulation performance. LPV model-based Linear Matrix Inequality(LMI) control method is applied to derive the control law. A robust control term is introduced to fix the estimation error of the nonlinear time-varying model parameters for better control performance. Finally, the Lyapunov stability analysis is performed to ensure the asymptotical convergence of the closed loop system. The simulation results show that the states of the engine can change smoothly and the thrust of the engine can accurately follow the desired trajectory, indicating that the proposed control approach is effective. The contribution of this work lies in the combination of linear system control and nonlinear system control methods to design an effective controller for the turbofan engine and to provide a new way for turbofan engine control research.     

A novel model-based multivariable framework for aircraft gas turbine engine limit protection control

Control technologies are innovated to satisfy increasingly complicated control demands of gas turbine engines. In terms of limit protection control, a novel model-based multivariable limit protection control method, which is achieved by adaptive command reconstruction and multiple-control loop selection and switch logic, is proposed in this paper to address the problem of balancing smaller thrust loss and safe operations by comparing with widely-used Min-Max logic. Five different combination modes of control loops, which represent the online control loop of last time instant and that of current time instant, is analyzed. Different command reconstructions are designed for these modes, which is based on static gain conversion of amplitude beyond limits by using an onboard model. The double-prediction based control loop selection and switch logic is developed to choose a control loop appropriately by comparing converted amplitude beyond limits regardless of one or more parameters tending to exceed limits. The proposed method is implemented in a twin-spool turbofan engine to achieve limit protection with direct thrust control, and the loss of thrust is improved by about 30% in comparison with the loss of thrust caused by Min-Max logic when limit protection control is activated, which demonstrates the effectiveness of the proposed method.