摘要
近空间飞行器(Nearspace Vehicles NSVs)是各国正在大力发展的新型航空航天飞行器,具有极其重要的军事价值。它们在运行中表现出的多任务、多工作模式、大范围高速机动等特点使得控制系统设计成为一项极具挑战的研究课题。围绕这一基础科学问题,本文在近空间飞行器建模与分析、受扰动非线性系统控制两个方面开展了较为深入的研究,主要研究成果如下: 1、根据我们实验室已有的研究成果及国内外公开发表的文献资料建立起NSV高超声速飞行条件下6自由度数学模型。该模型包含完整的动力学方程和运动学方程,其中气动力系数和力矩系数是迎角、马赫数及气动舵面偏角的函数,发动机模型为吸气式超声速燃烧冲压发动机。然后对其开环性能进行分析,结果表明整个模型能够体现出NSV复杂的非线性以及快速时变性等特点,具有一定的代表性,可以满足未来NSV先进制导和控制等问题的理论研究和仿真验证需要。
2、研究了NSV纵向控制,首先利用输入-输出反馈线性化方法将NSV纵向运动模型转化为仿射非线性模型,并根据飞行器的状态变量特性,进一步将其转化为严格反馈多输入多输出(Multi-Input/Multi-Output MIMO)非线性模型,并基于回馈递推方法设计了高超声速飞行的纵向控制系统,对其进行的纵向运动仿真结果表明了方法的有效性。
针对NSV高超声速飞行时,气动参数变化剧烈且容易受到外界干扰的特点,提出了基于全调节径向基神经网络(Fully Tuned Radial Basis Function Neural Network FTRBFNN)的积分回馈递推方法,并基于Lyapunov定理给出稳定性的严格证明。FTRBFNN抗干扰能力强,而且控制律中增加的误差积分项可以有效消除系统的跟踪静差,因而控制精度高。最后对NSV进行的纵向运动仿真结果表明,在干扰变化较大的情况下,控制系统仍具有较好的鲁棒性。
3、提出了基于动态面控制的变增益自适应回馈递推控制方法。首先利用动态面控制简化回馈递推控制器设计,然后在参数自适应律中引入S函数对径向基神经网络(Radial Basis Function Neural Network RBFNN)的学习率进行动态调节,消除系统在自适应初始阶段的抖振现象。基于Lyapunov定理证明了闭环系统稳定,跟踪误差指数收敛到任意小的有界紧集内。对NSV的纵向运动仿真结果表明,该方法在降低控制律复杂性的同时,仍具有良好的过渡过程动态特性。
4、提出了基于模糊系统的快速自适应回馈递推方法。利用模糊系统对系统的复合干扰进行在线辨识,利用回馈递推方法进行了控制系统设计,该方法在线调整自适应参数仅为子系统的个数,减轻了系统的在线计算负担。基于Lyapunov定理和小增益定理证明了闭环系统稳定,系统的跟踪误差指数收敛到任意小的有界紧集内。对NSV进行的高超声速条件下6自由度协调转弯仿真结果表明该方法在简化的自适应律和控制律下仍具有良好的跟踪性能。
5、结合干扰观测器技术,提出了基于模糊干扰观测器的自适应回馈递推方法。该方法充分利用被控系统的有用信息,参数自适应律根据系统的跟踪误差和观测器误差进行在线调整,从而实现了对NSV不确定更为有效的逼近,获得了更好的鲁棒性。
设计了一种模糊神经干扰观测器(Fuzzy Neural Network Disturbance Observer FNNDO),提出了基于FNNDO的自适应回馈递推方法。由于FNNDO可以在线调整模糊规则,因而对未知非线性不确定的辨识精度更高,从而可以达到更高的跟踪精度。最后用该方法设计了NSV姿态控制系统,并在高超声速的条件下进行了6自由度姿态控制仿真,结果表明了方法的有效性。
The current research in developing next generation flying vehicles is focused on nearspace vehicles (NSVs) which have very important martial values. The control systems of the ASVs pose several challenges due to their multi-mission profiles, large attitude maneuvers and complicated flight conditions. In this dissertation, two relative problems, i.e. modeling and perturbed uncertain nonlinear control system design, are studied. The main results in this paper are as follows:
1. Based on the research contributions of our lab and available material, the simulation model of a NSV is presented, which includes the kinetic equations and motion equations. Aerodynamic force and moment coefficients are given as functions of angle of attack, Mach number and control surface deflections. The propulsion system is a hypersonic air-breathing engine. Open-loop dynamics and stability characteristics demonstrate that the proposed model can be used to allow research, refinement and evaluation of advanced guidance and control methods.
2. Longitudinal control for NSV is researched. Firstly, the nonaffine nonlinear NSV model of longitudinal motion is transformed into the affine nonlinear system by using the input-output feedback linearization approach. Then the transformed model is further transformed into strict feedback multi-input/multi-output (MIMO) nonlinear system. Control system of longitudinal motion is designed by backstepping approach, and the simulation results of longitudinal motion demonstrate the effect of control approach.
With the assumption that NSV suffers the violent changes of aerodynamic paameters and the outside disturbance in hypersonic condition, we present the integrator backstepping approach based on fully tuned radial basis function neural network (FTRBFNN). The strict proof of the approach’s stability is provided simutanously. FTRBFNN has excellent ability of restaining disturbance, and the integrator term in backstepping approach eliminate the static traking error efficiently. As a result, the controller has high control precision. Finally, the simulation results of longitudinal motion show that the control system has good robustness with greatly disturbance.
3. We present an approach of adaptive dynamic surface control with variable gain. Firstly, the dynamic surface control is introduced in the backstepping approach to simplify the controller design. Due to the chattering problem appears in the initial stage of the adaptive dynamic surface approach, the learing rate of radial basis function neural networks are regulated dynamically on line, which eliminates the chatting problem in the initial stage of the adaptive process. It has been proved that the tracking error converge to an arbitrary small compact set in finite time by Lyapunov stability theorem. Finally, the simulatin results of longitudinal motion show that the presented approach can reduce the complexcity of control law while still retain good performance of transient process.
4. We present a fast adaptive backstepping approach based on fuzzy systems. The total disturbance is identified on line by fuzzy systems, and the control law is deduced by backstepping approach. The adaptive parameters on line can be reduced to the number of subsystems. As a result, the burden computation is alleviated greatly. The stability of the closed loop system is proved by Lyapunov stabiliy theorem and small gain approach, and the tracking error converges to an arbitrary small compact set exponentially. Finally, in hypersonic condition, the coordinated-turned maneuver with 6 degree of freedom of NSV is used in experiment. The simulation results show tht the approach still has good tracking performance with simplified control law and adaptive law.
5. Combining with the technology of disturbance observer, this paper presents an adaptive backstepping approach based on fuzzy disturbance observer. The apprach utilize the useful information of the controlled plant and the adaptive law of parameters can be regulated according to the integrated error composed of the tracking error and the error of disturbance observer. As a result, the method realized the accurate approximation and control, so the control performance is improved.
A new fuzzy neural network disturbance observer (FNNDO) is presented in this paper, which can further improve the ability of retaining disturbance, and a control algorithem of bakcsteppping approach based on FNNDO is presented. The simulation results show that the FNNDO can regulate the fuzzy rules on line, so it can achieve a higher tracking precision. Finally, the attitude control system of NSV is designed by this control algorithem. The simulation results show that the the control algorithem has higher convergence speed and higher tracking precision than the backstepping approach based on FDO.
引文
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