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.     

Adaptive diagnosis and compensation for hypersonic flight vehicle with multisensor faults

This study proposes an improved adaptive fault diagnosis and compensation scheme for multisensor faults of hypersonic flight vehicles (HFVs). The faults are detected and isolated through a series of sensor output residuals and thresholds that consider observation error and disturbances. Via an adaptive augmented observer, the faults are estimated accurately and a time‐varying disturbance is handled by an additional differential part. Sensor faults are compensated on the basis of estimation results, and disturbances are considered in the fault‐tolerant control (FTC) design, thereby improving the tracking accuracy of the altitude and velocity and robustness with respect to external disturbances for HFVs. The stability of diagnosis and FTC is analyzed by Lyapunov theory. Numerical simulation results explain the validity of the proposed diagnosis and compensation methods.     

基于鲁棒自适应反步的可重复使用飞行器再入姿态控制

考虑惯性矩阵不确定和力矩扰动的影响, 设计再入可重复使用飞行器的鲁棒自适应反步姿态控制器. 首先, 设计虚拟控制时, 通过自适应实现对不确定项的未知边界的估计; 其次, 设计实际控制输入时, 为消除反步法的“计算爆炸”问题, 将虚拟控制导数作为不确定项, 引入鲁棒项消除不确定与力矩扰动的影响; 再次, 基于Lyapunov 理论证明了跟踪误差收敛到任意小邻域; 最后, 基于X-33 的六自由度模型仿真验证了所设计的控制策略的有效性.     

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.     

具有非线性不确定性的高超声速飞行器的 分布式鲁棒反步跟踪控制

本文针对具有外部干扰,参数摄动和非连续未知非线性气动影响的一般高超声速飞行器纵向动力学问题,设计了分布式鲁棒反步跟踪控制器.为了处理复杂的系统,将标准反步控制和信号补偿方法结合起来构成一个"简单"的鲁棒控制器.该方法不仅可以保证闭环系统半全局鲁棒跟踪性能,也可保证系统跟踪误差以期望的收敛速度收敛到期望的误差范围内.最后,带有非线性不确定性,外部干扰和参数扰动的仿真系统说明了该方法的有效性.     

Adaptive neural network tracking design for a class of uncertain nonlinear discrete-time systems with dead-zone

In this paper, the stability and control issues of a class of uncertain nonlinear discrete-time systems in the strict feedback form are investigated. The dead-zone input in the systems, whose property is non-symmetric and discretized, is investigated. The unknown functions in the systems are approximated by using the radial basis function neural networks （RBFNNs）. Backstepping design procedure is employed in the controller and the adaptation laws design. Lyapunov analysis method is utilized to prove the stability of the closed-loop system. A simulation example is given to illustrate the effectiveness of the proposed approach.     

基于反步法的高超音速飞机纵向逆飞行控制

针对高超音速飞机纵向运动的数学模型具有严重非线性、不稳定、多变量耦合以及不确定的气动参数等特点,采用非线性动态逆控制与反步法相结合的方法为其设计飞行控制系统．该系统以非线性动态逆控制作为控制内环,通过将非线性的多输入多输出系统进行精确线性化,解除了多变量之间的强耦合关系;并以反步法作为控制外环．保证系统的全局稳定以及抑制不确定参数的扰动．仿真研究表明．所提出的控制方法可以确保高超音速飞机的纵向稳定性．改善其飞行品质．     

Output feedback control of hypersonic vehicles based on neural network and high gain observer

In this paper,the output feedback control problem for a genetic hypersonic vehicle is considered under the restriction that only the vehicle's velocity and altitude are measurable. High gain observers (HGO) are utilized to provide estimation signals for unmeasurable derivatives of the vehicle's velocity and altitude. Neural network based feedforward function is designed to compensate for model uncertainties. The proposed control design require less knowledge of the hypersonic vehicle's dynamic model. A comp...     

Control variable parameterisation with penalty approach for hypersonic vehicle reentry optimisation

An efficient trajectory optimisation approach combining the classical control variable parameterisation (CVP) with a novel smooth technology and two penalty strategies is developed to solve the trajectory optimal control problems. Since it is difficult to deal with path constraints in CVP method, the novel smooth technology is firstly employed to transform the complex constraints into one smooth constraint. Then, two penalty strategies are proposed to tackle the converted path and terminal constraints to decrease the computational complexity and improve the constraints satisfaction. Finally, a nonlinear programming problem, which approximates the original trajectory optimisation problem, is obtained. Error analysis shows that the proposed method has good convergence property. A general hypersonic cruise vehicle trajectory optimisation example is employed to test the performance of the proposed method. Numerical results show that the path and terminal conditions are well satisfied and better trajectory profiles are obtained, showing the effectiveness of the proposed method.     

无人机编队飞行神经网络自适应逆控制器设计

针对无人机编队飞行时存在的气动耦合和外部干扰等影响因素,提出基于“长-僚机”模式的神经网络自适应逆控制器设计方法.详细推导了气动耦合影响,建立了完整的编队飞行非线性数学模型,设计了非线性动态逆控制律,提出了改进的 BP 神经网络算法,自适应地逼近和在线补偿动态逆误差,改善了控制效果,并针对队形变换提出了简单有效的设计思想.仿真表明,该控制器能有效实现编队队形的保持或变换,控制系统结构具有良好的扩充性.     

Sliding mode learning control for T-S fuzzy system and an application to hypersonic flight vehicle

Sliding mode-based learning control is presented for T-S fuzzy system. A T-S fuzzy model with both uncertainties and unmodeled dynamics is proposed firstly, in which the information of uncertainties and unmodeled dynamics are assumed to be unknown. Then, according to a given reference model, state-tracking error system is built. Respecting facts, the input matrices of the built T-S fuzzy model are different from each other. An extended state observer is built for estimating the unknown uncertainties and unmodeled dynamics, and a corresponding sliding surface is proposed. A learning controller is then presented for the closed loop system. Moreover, a numerical simulation result on hypersonic flight vehicles is considered to testify the controller's availability.     

TCSC鲁棒自适应控制器的非线性逆推设计

设计了一种应用于互联电网的TCSC鲁棒自适应调制控制器(RAMC).该控制器采用广域测量系统中的全局信号,旨在对各区域的惯量中心进行控制并保持同步运行.采用反推法推导了控制规律,并在2区域4机系统中进行了控制器性能检测.仿真结果显示,该控制器可有效阻尼互联电力系统间的机电振荡,与传统的控制器相比具有良好的性能.     

无人机编队飞行的分布式控制策略与控制器设计

针对一种小型无人机模型及其编队飞行的实际背景和限制条件,分析了编队飞行所必须涉及的队形保持、约束条件以及行为协调等关键性问题,进而引入分布式编队飞行控制策略并简要介绍了其优越性.根据分布式策略的层级概念,先后讨论了单机控制器的设计与上层的编队控制器的设计.最后分别进行了单机的FDC(flight dynamic and control)仿真和双机编队仿真.仿真结果表明,设计的控制器在执行效率和控制性能等方面具有突出的优势.     

退火递归神经网络极值搜索算法及其在无人机紧密编队飞行控制中的应用

针对无人机紧密编队飞行问题,以气动干扰引起的僚机俯仰角υω作为极值搜索变量,利用退火递归神经网络极值搜索算法,使僚机干扰俯仰角υω收敛至其极值,从而解决了无人机紧密编队飞行中僚机所需动力最小化的问题.将退火递归神经网络与极值搜索算法相结合,消除了传统极值搜索算法中控制量的来回切换问题和输出"颤动"现象,改善了系统的动态性能,同时简化了系统的稳定性分析.通过对无人机紧密匕行编队的仿真,验证了该算法的有效性.     

吸气式高超声速飞行器考虑控制约束的设计优化

为了能够获得性能卓越,可靠的吸气式高超声速飞行器(AHSV)设计,需要考虑其特殊动力学特性及控制系统的性能约束,研究面向控制系统设计的系统设计优化策略.首先,简要介绍了AHSV第1原理建模与参数化建模相关的问题与方法;其次,分析了AHSV系统模型与控制系统性能之间的约束耦合特性:系统模型的不稳定极点及其左特征向量与控制信号的饱和约束确定了反馈控制系统的零可控区域;系统模型右半平面(RHP)零极点对于反馈闭环系统灵敏度函数与补灵敏度函数具有峰值约束与带宽限制;最后,考虑系统模型与控制系统约束耦合的特性,根据选取的总体优化设计目标,介绍了系统本体与控制系统组合优化的相关策略.并简要分析了各优化策略的实用性及适用范围.对于AHSV考虑控制系统性能约束的优化设计提供了一种研究方法和设计思路.     

Observer-based synergetic adaptive neural network control for a class of discrete-time nonlinear systems with dead-zone

In this paper, the observer-based synergetic adaptive neural network control method is designed for a class of discrete-time systems with dead-zone. A macro-variable is introduced by a synergetic approach to control theory and neural networks are utilised to estimate unmeasured states and unknown functions in the system. Furthermore, by employing an adaptive design procedure and Lyapunov stability theory, the closed-loop system stability is guaranteed, and the desired system performance is achieved simultaneously. Finally, some simulation results are given to prove the validity of the developed control method.     

Robust controller design for uncertain delayed systems and its applications to hypersonic vehicles

The longitudinal dynamics of hypersonic flight vehicles involves strong nonlinearity and coupling, uncertainties including parametric uncertainties, unmodeled uncertainties, external disturbances, and time‐varying input and state time delays. In this paper, a robust controller design method is proposed for the longitudinal stabilization of these vehicles by the signal‐compensation‐based control idea. Theoretical analysis is given to prove the robustness properties of the designed closed‐loop control system subject to multiple time‐varying uncertainties and time‐varying input and state delays. Simulation results are performed to show the validness and advantages of the proposed robust control approach.     

高超声速飞行器非线性鲁棒控制律设计

高超声速飞行器具有模型非线性程度高、耦合程度强、参数不确定性大、抗干扰能力弱等特点,其自主控制具有较大的挑战.论文提出了一种基于鲁棒补偿技术和反馈线性化方法的非线性鲁棒控制方法.文中首先采用反馈线性化的方法对纵向模型进行输入输出线性化,实现速度和高度通道的解耦和非线性模型的线性化.针对得到的线性模型,设计包括标称控制器和鲁棒补偿器的线性控制器.基于极点配置原理,设计标称控制器使标称线性系统具有期望的输入输出特性,利用鲁棒补偿器来抑制参数不确定性和外界扰动对于闭环控制系统的影响.基于小增益定理,证明了闭环控制系统的鲁棒稳定性和鲁棒跟踪性能.相比于非线性回路成形控制方法,仿真结果表明了所设计非线性鲁棒控制算法的有效性和优越性.