量子微粒群在波阻抗反演中的应用

求解的局部性和计算速度慢是目前非线性波阻抗反演存在的主要缺陷。在与微粒群算法进行比较的基础上,研究了量子微粒群反演算法。数值实验结果表明,与微粒群反演相比,量子微粒群波阻抗反演在收敛速度和避免陷入局部极小等方面有着一定的优势,适合于非线性反演问题,该方法也适用于其他领域非线性最优化问题的求解,具有较强的普适性。     

Set membership inversion and robust control from data of nonlinear systems

A set membership method for right inversion of nonlinear systems from data is proposed in the paper. Both the cases where the system to invert is known or unknown and therefore identified from data are addressed. The method does not require the invertibility of the regression function describing the system and ensures tight bounds on the inversion error. In the case of unknown system, the method allows the derivation of a robust right‐inverse, guaranteeing the inversion error bound for all the systems belonging to the uncertainty set which can be defined from the available prior and experimental information. Based on such a set membership inversion, two methods for robust control of nonlinear systems from data are introduced: nonlinear feed‐forward control (NFFC) and nonlinear internal model control (NIMC). Both the design methods ensure robust stability and bounded tracking errors for all the systems belonging to the involved uncertainty set. Two applicative examples of robust control from data are presented: NFFC control of semi‐active suspension systems and NIMC control of vehicle lateral dynamics.Copyright © 2013 John Wiley & Sons, Ltd.     

地震波阻抗参数混沌控制反演

分析了地震非线性反演的混沌现象, 并且从测井资料出发, 以非线性动力学不动点理论及混沌控制理论为基础, 提出了混沌控制反演方法, 数值实验和应用实例表明, 该方法能有效地控制非线性反演系统在反演选代过程中出现的混沌现象, 得到较真实可靠的反演结果.     

一种复杂非线性系统的智能变结构控制方法

采用一种智能结构方法实现复杂非线性系统的控制,给出了设计原则,并用第四代歼击机飞行控制系统设计一个复杂实例来说明方法的有效性,实例系统中包含了两种控制律：增益调参和非线性动态逆控制律,两种控制律都包括了对发动机推力矢量喷管的控制。文中采用了一种基于状态的控制律切换算法进行两类控制律的切换,考虑了变结构系统切换瞬态问题和强迫振荡问题,并给出了简便的解决方法。     

A subspace trust-region method for seismic migration inversion

In seismic exploration, regularized migration inversion of seismic data usually requires solving a weighted least-squares problem with constrains. It is well known that directly solving this problem using some decomposition techniques is very time-consuming, which makes it less possible for practical use. For iterative methods, previous research is mainly on solving the inverse model in a full space. In this paper, a robust subspace method is applied to seismic migration inversion with Gaussian beam representations of Green's function. The problem is first formulated by incorporating regularizing constraints, and then, it is changed from full space to subspace and solved by a trust-region method. To test the potential of the application of the developed method, synthetic data simulations are performed. The results show that this method is very promising for ill-posed seismic migration inversion problems.     

Inversion of multilayer neural network with modelling error compensation

The main contribution of this paper is to develop a method, using the Newton-Raphson me/hod, to search for the unknown part of the inputs of a multilayer neural network with given outputs and known inputs. To use the Newton-Raphson method, a method of expressing a jacobian by neural network parameters is developed first. A locally linearized relation between inputs and outputs of neural network is then derived. With this, iterative Newton-Raphson searches are performed until satisfactory results are obtained. The method shows rapid convergence, compared with previous approaches. While deriving the inverse of the neural network, some types of optimality, which are problem dependent, are resolved. Although the method shows fast convergence, this type of solution yields some inversion error due to the neural network modelling error. The second contribution of this paper is to propose a novel structure which can eliminate the inversion error caused by the neural network modelling error. The proposed method has a simple structure, but shows good performance as it has a feedforward structure and other beneficial features. Through computer experiments, the proposed methods show good performances in solving inverse kinematics of redundant robots and controlling nonlinear plant.     

多变量非线性飞行控制系统的神经网络动态逆控制方法

针对多变量非线性飞行控制系统,从理论上对其逆系统的解析形式进行了详细推导,根据神经网络逼近逆系统的原理分析,提出了一种由静态神经网络和积分器组成的动态神经网络,构造了多变量非线性飞行控制系统的神经网络动态逆控制系统,并利用动力伞飞行控制系统进行了仿真验证,结果表明这种控制方法完全满足控制要求,具有较好的抗干扰能力和鲁棒性能。     

A homotopy method for exact output tracking of some non‐minimum phase nonlinear control systems

This paper presents a method for non‐causal exact dynamic inversion for a class of non‐minimum phase nonlinear systems, which seems to be an alternative to those existing in the literature. This method is based on a homotopy procedure that allows to find a ‘small’ periodic solution of a desired equation by a continuous deformation of a known periodic solution of a simpler auxiliary system. This method allows to face the exact output tracking problem for some non‐minimum phase systems that are well known in the literature, such as the inverted pendulum, the motorcycle and the CTOL aircraft. Copyright © 2008 John Wiley & Sons, Ltd.     

免疫遗传算法及其在波阻抗反演中的应用*

提出一种改进的免疫遗传算法（IGA）并用于波阻抗反演。IGA设计了基于抗体激励度和抗体浓度的免疫选择算子及加速全局收敛的非一致性变异算子,提出了抗体规模自适应调整机制,IGA能够改善传统非线性反演方法易早熟和局部收敛等弊端。通过理论模型试算表明,IGA具有较高的反演精度和反演效率;阳泉二矿实际资料反演所得剖面的纵向分辨率明显高于实际地震剖面,弱反射波的连续性和可检测性明显提高,表明改进的免疫遗传算法适用于煤炭地震波阻抗反演。     

Stable inverse control for nonminimum-phase nonlinear processes

Since most of the chemical processes happen to be nonlinear, nonlinear controllers with feedback linearization have been recently designed which can directly cancel the nonlinearities and establish a linear input/output map. However, this I/O linearization algorithm requires the control with stable inversion and state feedback. In the present study, a stable inversion technique for nonminimum-phase systems is developed under appropriate assumptions, which includes several steps such as finding the bounded control and bounded state trajectories for tracking the desired signal. In this design, the output tracking error as well as the effect of measurable disturbances can be asymptotically eliminated through generating a feedforward control. Finally, the proposed control methodology is applied to a chemical process, viz. a Van de Vusse reaction in an isothermal or adiabatic stirred-tank reactor, and its performance is evaluated.     

基于非线性动态逆的姿态跟踪控制

当遇到非线性系统时可以利用传统的控制策略将其转化为线性系统进行处理,但是当系统的非线性度很高的时候,传统的控制策略就无能为力了,为了在飞行控制领域解决上述问题,引入了动态逆控制方法。首先确定一种非线性动态逆控制规律在飞行器上的应用,同时考虑到了惯性参数的不确定性和气动力矩的不确定性,实际是一种非线性的时变系统,为了改善全局稳定性,可以通过六自由度的仿真保证系统稳定,证明控制规律的有效性。     

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。     

不确定非仿射系统的引入动态逆的自抗扰控制器设计

针对一类不确定非仿射严反馈非线性系统, 提出一种引入动态逆的线性自抗扰控制器设计方法. 首先, 利 用微分同胚映射将严反馈非线性系统变换为积分串联型系统, 然后针对积分串联型系统设计线性自抗扰控制器. 提出的线性自抗扰控制器将闭环系统划分为3个时间尺度, 其中线性扩张状态观测器位于最快的时间尺度上, 用来 估计系统的状态和总和扰动, 动态逆位于次快的时间尺度上用以求解非仿射情况下的控制律, 系统动态位于最慢的 时间尺度上. 利用奇异摄动理论分析了闭环系统的稳定性和性能. 提出的自抗扰控制设计方法同样适用于控制增 益不确定的仿射非线性系统. 仿真和实验结果验证了提出的线性自抗扰控制器的可行性.     

A new functional expansion for nonlinear systems

In this paper, we shall introduce a new functional series expansion of the output of a general nonlinear system as power series in powers of derivatives of the input components. We shall illustrate the usefulness of this series by considering two important problems namely those of “realization” and systems inversion.     

大型飞机神经网络重构控制

介绍了一种通过神经网络在线进行飞行控制重构的方案.本文以非线性动态逆方法为基本控制律,通过设计的自适应神经网络,实时补偿因操纵面故障而引起的逆误差.最后的仿真结果表明,该方案是可行的.     

基于BP-GA算法的微震源反演研究

针对传统非线性反演方法易陷入局部最优,收敛速度慢等问题,研究了一种基于BP神经网络和遗传算法的微震源定位反演方法。重点论述了BP-GA混合算法的理论基础与模型建立,并结合实例进行了3层介质模型微震源定位反演仿真。     

高超音速飞行器鲁棒控制研究

针对高超音速飞行器具有高度非线性、输入输出之间强耦合以及参数不确定等特点,提出了基于随机鲁棒设计的动态逆控制;这一控制方案基于系统控制需求,利用蒙特卡罗仿真方法建立目标函数,并通过遗传算法最优化系统设计参数,该控制方案将动态逆控制的非线性解耦控制能力与随机鲁棒设计的强鲁棒性结合,保证了飞行的纵向稳定性,改善了其控制性能;基于某常规高超音速飞行器纵向模型进行仿真验证,结果表明该方案能够满足系统控制需求且具有强鲁棒性。     

Laplace变换的代数数值反演方法

１．引 言 在过去的几十年中,以 Ｌａｐｌａｃｅ变换理论为基础的运算法［１］在自动控制领域中成功地简化并解决了许多实际问题．运算法中最困难的就是从变像（即 Ｌａｎｌａｃｅ变换）找出原函数即反演）．虽然有许多变换表中由变像可直接查出相应的反演,但变换表远远不能包括所有实际中遇到的问题,而且在一般情况下,反演的表达式是一个复积分［２］： 这个积分在很多情况下是十分复杂以致难以算出,这时我们只能通过近似的方法求出反演的近似值,即求数值反演． Ｌａｐｌａｃｅ变换的数值反演是个病态问题．尽管在九十年代以前许多学…     

基于自组织模糊CMAC网络的非线性系统鲁棒自适应跟踪控制

基于自组织模糊CMAC(SOFCMAC)神经网络,提出了一种非线性模型参考神经网络 增广逆系统鲁棒自适应跟踪控制方法.该方法的特点是通过S0FCMAC神经网络在线修正由 于建模误差、不确定因素等引起的非线性系统逆误差,使得系统输出准确跟踪参考模型输出. SOFCMAC的权值调整规律由Lyapunov稳定性理论导出.文中证明了非线性闭环系统的稳定 性.仿真例子表明了本文方法的有效性.     

Guidance and stable and unstable manifolds with input

The theory in this paper is motivated by the problem of aircraft guidance in automatic flight control, in which plant models are nonlinear. This leads us to consider the stable inversion problem, which in turn can be cast as the problem of finding a bounded solution of a time-varying nonlinear system subject to a bounded input. Under the appropriate assumptions there is a unique bounded (for all time) continuous solution to this time-varying nonlinear system in response to the bounded (for all time) input. We show that there is a (local) stable manifold containing all bounded continuous solutions for non-negative time, and that all such solutions converge to the aforementioned unique solution as time goes to infinity. Likewise there is a (local) unstable manifold containing all bounded solutions for non-positive time and these converge to the unique solution as time goes to minus infinity. In fact, the unique solution (cross the time axis) is the intersection of the stable and unstable manifolds. Copyright © 1999 John Wiley & Sons, Ltd.