高超声速飞行器基于Back-stepping的离散控制器设计

!根据高超声速飞行器纵向模型的特点,提出了基于Back-stepping的离散控制器设计方法.首先通过合理的简化,将飞行器的模型转化为连续非线性系统的严格反馈形式;然后采用欧拉法得到其近似的离散模型,根据近似离散模型并结合Back-stepping和反馈线性化方法,设计了高超声速飞行器的离散控制器.利用高超声速飞行器的纵向模型对算法进行仿真验证,得到了较为满意的控制效果.

     

基于Backstepping的高超声速飞行器模糊自适应控制

提出了高超声速飞行器的模糊自适应控制方法.根据飞行器纵向模型的特点,分别设计了基于动态逆的速度控制器和基于Backstepping的高度控制器,模糊自适应系统用来在线辨识飞行器模型由于气动参数的变化而引起的不确定性,采用Lyapunov理论设计的自适应律保证了系统的稳定性与指令跟踪的精确性.仿真使用了高超声速飞行器的纵向模型对算法进行了验证,得到了较满意的控制效果.     

基于预测控制的高超声速飞行器容错控制器设计

针对存在升降舵面偏转角卡死故障的高超声速飞行器,提出一种基于预测控制的容错控制器设计方法.利用输入输出反馈线性化,对高超声速飞行器纵向模型进行变换;对于速度和高度的高阶导数以及故障项,设计扩张状态观测器在线观测;采用泰勒展开得到预测模型,建立连续预测控制器,分析证明闭环控制系统的稳定性和观测误差的有界性.仿真结果验证了所提方法的有效性.     

高超声速飞行器考虑数据丢失的预测控制研究

以高超声速飞行器纵向通道为研究对象,考虑飞行器控制系统中传感器–控制器以及控制器–执行器通道均存在数据丢失的问题,提出一种能有效处理丢包的预测控制方法。首先,对高超声速飞行器纵向通道非线性模型进行局部小扰动线性化,得到平衡点处线性化模型;接着,建立有数据丢失的系统动态模型,使用终端状态约束集和终端代价函数方法设计预测控制器并设计相关补偿策略,以实现高超声速飞行器输入指令的跟踪;最后,基于MATLAB和Truetime平台进行数值仿真。结果表明,所设计的预测控制器能保证系统在出现数据丢失时具有良好的跟踪性能和鲁棒性。     

基于QFT+DI的高超声速飞行器控制研究

在高超声速飞行器控制研究中,大空域大马赫数飞行造成的强耦合、高度非线性,影响控制系统的稳定性.为解决上述问题,在传统定量反馈控制理论的基础上,提出定量反馈和动态逆相结合的鲁棒控制方法,对高超声速飞行器纵向模态进行控制器设计.利用动态逆对系统非线性模型进行反馈线性化,将得到的线性模型作为控制的内环和定量反馈设计模板,设计外环系统的控制器和前置滤波器.通过计算机对设计的控制器进行仿真验证,结果表明,控制设计方法具有较好的控制效果,且比传统的设计方法在高频段具有更好的噪声抑制能力.     

基于神经网络补偿的高超声速飞行器滑模控制

针对吸气式高超声速飞行器的轨迹控制问题,提出了一种基于自适应径向基函数(RBF)神经网络的滑模控制方法.建立了高超声速飞行器的纵向动态模型;设计了滑模控制器,利用自适应RBF神经网络对系统不确定项进行在线逼近,对滑模控制器进行补偿;基于李雅普诺夫的稳定性分析证明了闭环系统的稳定性.仿真结果表明:控制系统能够实现对于高超声速飞行器给定指令的有效跟踪.     

高超声速飞行器最优PIF-LQR控制器设计

以一类通用高超声速飞行器的非线性纵向模型为研究对象,对其线性化后,应用LQR理论设计了一种多输人多输出的最优控制器.通过引入比例积分滤波器(PIF),有效地抑制模型参数变化所引起的扰动,实现飞行器对速度和高度变化指令精确跟踪.将所设计控制器应用于具有不确定参数的高超声速飞行器扰动模型,通过仿真对控制器的鲁棒性进行评估.仿真结果表明,尽管存在参数不确定性,所设计控制器能够满足高超声速飞行器在复杂飞行条件下的控制要求,具有较强的鲁棒性.     

高超声速飞行器的自抗扰控制器设计

由于采用机体一体化设计,吸气式高超声速飞行器的气动特性难以准确获知,建立的数学模型是极为不准确的;设计了一种自抗扰控制器(ADRC),分别设计了速度回路和高度回路的自抗扰控制器;仿真用高超声速飞行器的纵向模型对该控制器进行了验证,证明该控制方法能够有效地跟踪飞行器的高度和速度指令。     

高超声速飞行器神经网络动态逆姿态控制器设计

以一类通用高超声速飞行器纵向模型为研究对象,推导了飞行器纵向姿态运动方程。考虑受到飞行器附加攻角扰动以及气动参数不确定性的影响,对运动方程中速率变化快慢不同的攻角和俯仰速率分别引入精确线性化动态逆反馈,并利用神经网络对系统逆反馈误差进行补偿,有效地抑制了模型参数摄动,实现了对攻角指令的精确跟踪。将设计的控制器对高超声速非线性模型进行系统闭环仿真,仿真结果说明所设计的姿态控制器具有抗参数摄动的性能,能够满足高超声速飞行器复杂飞行条件下的姿态控制要求,具有较强的鲁棒性。     

高超声速飞行器调度双模预测控制方法

针对一类具有大包线飞行和快时变特性的高超声速飞行器,采用多个线性时变(LTV)多胞模型近似地描述高超声速飞行器的纵向非线性模型.对于各LTV多胞模型,离线设计局部双模预测控制器来显式地处理控制输入和状态的约束问题,并获得相应的稳定域.然后,构造各局部控制器间稳定的切换策略.仿真结果表明,所提方法在控制输入和状态满足给定的约束范围情况下,不仅能更快地跟踪较大范围的速度和高度的指令信号,而且大幅减少了在线计算时间.     

Adaptive discrete-time controller design with neural network for hypersonic flight vehicle via back-stepping

In this article, the adaptive neural controller in discrete time is investigated for the longitudinal dynamics of a generic hypersonic flight vehicle. The dynamics are decomposed into the altitude subsystem and the velocity subsystem. The altitude subsystem is transformed into the strict-feedback form from which the discrete-time model is derived by the first-order Taylor expansion. The virtual control is designed with nominal feedback and neural network (NN) approximation via back-stepping. Meanwhile, one adaptive NN controller is designed for the velocity subsystem. To avoid the circular construction problem in the practical control, the design of coefficients adopts the upper bound instead of the nominal value. Under the proposed controller, the semiglobal uniform ultimate boundedness stability is guaranteed. The square and step responses are presented in the simulation studies to show the effectiveness of the proposed control approach.     

Input-to-state-stability modular command filtered back-stepping attitude control of a generic reentry vehicle

A command filtered back-stepping attitude controller is exploited and analyzed to design a dynamic state-feedback controller for a generic Reentry Vehicle. A novel back-stepping control that obviates the need to compute analytic derivatives in the traditional back-stepping design is presented by combining command filtered back-stepping method, sliding-mode-based integral filters and inputto-state stability (ISS) analysis. The ISS-modular approach provides a simple and effective way for controlling non-linear Reentry Vehicle satisfying the strict-feedback form, simultaneously solving the problem of “explosion of complexity” in traditional back-stepping approach. The stability analysis of the closed-loop system and the convergence of the aerodynamic angles are verified based on the smallgain theorem. Numerical simulations are included to demonstrate the effectiveness of the proposed control scheme.     

Prescribed performance fuzzy back-stepping control of a flexible air-breathing hypersonic vehicle subject to input constraints

Journal of Intelligent Manufacturing - The design of prescribed performance fuzzy back-stepping tracking control for a flexible air-breathing hypersonic vehicle (FAHV) with actuator constraints is...     

Adaptive Fuzzy Back-stepping Control of a Flexible Air-breathing Hypersonic Vehicle Subject to Input Constraints

The design of an adaptive fuzzy back-stepping tracking control for flexible air-breathing hypersonic vehicle (FAHV) with actuator constraints is discussed. Fuzzy logic systems (FLSs) are applied to approximate the total uncertainty of FAHV model. The minimal-learning-parameter (MLP) algorithms are applied for FLSs to reduce the quantity of calculation. For the sake of preventing explosion of differentiation term in back-stepping control, the derivatives of virtual control laws can be calculated by sliding mode differentiators. Particularly, the novel auxiliary systems are explored to cope with the inputs saturation. Finally, reference trajectory tracking simulation shows the auxiliary systems have better performances than the existing algorithms when the inputs are saturated.     

高超声速飞行器输入受限反演鲁棒控制

针对执行机构受限条件下高超声速飞行器的控制问题,提出一种鲁棒反演控制设计 方法.设计一种新的辅助系统对跟踪误差和控制律进行补偿,保证执行器受限时跟踪误差的 有界性.为了避免传统反演方法中虚拟导数计算量膨胀问题,引入滑模微分器对虚拟 导数进行求解.为了增强系统的鲁棒性,基于改进反正切跟踪微分器(MATD)设计 一种新型干扰观测器,对系统的不确定项进行估计和补偿.最后,通过实例仿真验证了所提出控制器的有效性.     

A Singularly Perturbed System Approach to Adaptive Neural Back-stepping Control Design of Hypersonic Vehicles

This paper presents the design of neural adaptive flight control systems for the longitudinal dynamics of hypersonic vehicle. By considering the coupling between thrust and pitch moment, the proposed control strategy is derived from the solutions of a series of fast dynamical equations, which are designed based on the back-stepping control and singularly perturbed system approach. The RBF neural networks are employed to approximate the unknown hypersonic dynamics. Simulation results are included to show the effectiveness of the neural adaptive control method.     

Robust estimation-free prescribed performance back-stepping control of air-breathing hypersonic vehicles without affine models

This paper investigates the design of a novel estimation-free prescribed performance non-affine control strategy for the longitudinal dynamics of an air-breathing hypersonic vehicle (AHV) via back-stepping. The proposed control scheme is capable of guaranteeing tracking errors of velocity, altitude, flight-path angle, pitch angle and pitch rate with prescribed performance. By prescribed performance, we mean that the tracking error is limited to a predefined arbitrarily small residual set, with convergence rate no less than a certain constant, exhibiting maximum overshoot less than a given value. Unlike traditional back-stepping designs, there is no need of an affine model in this paper. Moreover, both the tedious analytic and numerical computations of time derivatives of virtual control laws are completely avoided. In contrast to estimation-based strategies, the presented estimation-free controller possesses much lower computational costs, while successfully eliminating the potential problem of parameter drifting. Owing to its independence on an accurate AHV model, the studied methodology exhibits excellent robustness against system uncertainties. Finally, simulation results from a fully nonlinear model clarify and verify the design.     

基于加幂积分方法的制导控制一体化设计

针对高超声速飞行器纵向制导控制一体化设计问题展开研究.首先建立纵向制导与控制系统一体化设计模型;然后结合非线性干扰观测器与加幂积分方法设计制导与控制一体化算法,并借助相关基础理论证明级联系统是全局有限时间稳定的.所提出的方法可以使制导与控制系统协调配合,更充分地利用飞行器控制能力.通过与反步滑模制导控制一体化设计方法进行对比仿真,验证了该方法有效且更具优势,并通过模拟外扰及参数拉偏情况下的仿真验证了所提出方法亦具备较强的鲁棒性.     

Back-stepping Robust Control for Flexible Air-breathing Hypersonic Vehicle via α-filter-based Uncertainty and Disturbance Estimator

In this paper, a robust control scheme using back-stepping and α-filter-based uncertainty and disturbance estimator (UDE) is designed for flexible air-breathing hypersonic vehicle (FAHV) in the presence of severe external disturbances. Firstly, with the aid of back-stepping technique, the longitudinal dynamics of FAHV is separated into velocity and altitude subsystems. Feedback linearization (FL) is utilized to design control laws for each subsystem. Secondly, to enhance robustness of the controller, α-filter-based UDEs are constructed to estimate the lumped disturbances, including aerodynamic parameter uncertainties, flexible effects and external disturbances. More importantly, the performance superiority of α-filter-based UDE is clarified via frequency response analysis and comparisons with an extended state observer (ESO). Furthermore, the issue of tuning design parameter α is analyzed. The stability of closed-loop system is proved by Lyapunov stability theory. Simulation results demonstrate the effectiveness of the proposed control strategy.

     

Using discrete models with continuous design packages

An algorithm is derived for converting a discrete state-space model to an equivalent continuous form. Both approximate and exact solutions are presented. A new discrete model is also derived, for which the continuous design will be valid. The principles are illustrated using an example of optimal control, and the discrete and continuous designs are compared. These techniques have particular relevance for computer aided design of feedback control systems.