无记忆弱非线性系统的时域建模方法─Volterra级数的应用

提出了一种应用Ｖｏｌｔｅｒｒａ级数给一类无记忆弱非线系统的时域建模的方法．为系统Ｖｏｌｔｅｒｒａ核的辨识奠定了理论基础．     

A sequential quadratic programming-based algorithm for optimization of gas networks

British Gas uses a complex, heavily looped network of pipes and controllable units (compressors and regulators) to transmit gas from coastal supply terminals to regional demand points. Computer algorithms are required for efficient management of the system. This paper describes an algorithm for optimal control over periods of up to a day. The problem is large scale and highly nonlinear in both objective function and constraints. The method is based on Sequential Quadratic Programming and takes account of the structure of the pipeflow equations by means of a reduced gradient technique which eliminates most of the variables from the quadratic subproblems. The latter involve only simple bound constraints, which are handled efficiently by a conjugate gradient-active set algorithm. Trust region techniques permit use of the exact Hessian, preserving sparsity. More general constraints are handled at an outer level by a truncated augmented Lagrangian method. Results are included for some realistic problems. The algorithm is generally applicable to problems with a control structure.     

Identification environment and robust forecasting for nonlinear time series

In this paper, the methods of time series for nonlinearity are briefly surveyed, with particular attention paid to a new test design based on a neural network specification. The proposed integrated expert system contains two main components: an identification environment and a robust forecasting design. The identification environment can be viewed as a integrated dynamic design in which cognitive capabilities arise as a direct consequence of their self-organizational properties. The integrated framework used for discussing the similarities and differences in the nonlinear time series behavior is presented. Moreover, its performance in prediction proves to be superior than the former work. For the investigation of robust forecasting, we perform a simulation study to demonstrate the applicability and the forecasting performance.     

A globally stable discrete-time controller for current-fed induction motors

In this short paper we present a discrete-time field oriented controller (FOC) for current-fed induction motors which insures global asymptotic speed regulation as well as rotor flux norm tracking. To the best of our knowledge, this is the first time such a result is rigorously established for a controller implemented in discrete-time. To insure global stability a condition on the reference for the rotor flux norm, which is time-varying, is imposed. This condition disappears as the sampling period goes to zero, hence allowing for independent speed regulation and rotor flux norm tracking. One important feature of our scheme is that, compared with the first-difference approximation of the classical indirect FOC, the additional computational burden is negligible. It is also shown that the result can easily be extended to the case of tracking time-varying references in speed or position.     

Identification of delayed system using instrumental variable method

System identification uses system inputs and outputs to raise mathematical models.Various techniques of system identification exist that offer a nominal model and an uncertainty bound.Many practical systems such as thermal processes & chemical processes have inbuilt time delay.If the time delay used in the system model for controller design does not concur with the actual process time delay,a closed-loop system may be unstable or demonstrate unacceptable transient response characteristics so here the time delay is assumed to be time-invariant. This paper proposes on-line identification of delayed complex/uncertain systems using instrumental variable(Ⅳ) method.Parametric uncertainty has been considered which may be represented by variations of certain system parameters over some possible range.This method allows consistent estimation when the system parameters are associated with the noise terms,as the IV methods(IVM’s)usually make no assumption on the noise correlation configuration.The faster convergence of the parameters including noise terms has been proved in this paper.Iterative prefiltering(IP)method has also been used for the identification of the delayed uncertain system and the graphical results given in this paper demonstrate that the convergence results are inferior to the instrumental variable method.     

基于多模型半参数非线性回归模型的外弹道节省参数估计

双星定位系统的应用是外弹道估计中的一种发展趋势。文章给出基于双星、光雷的联合测量模型,建立基于半参数回归的外弹道节省参数估计模型;给出基于半参数回归的外弹道节省参数估计算法及计算步骤,并从理论上分析了基于半参数回归的外弹道节省参数估计算法能有效地消除非线性因素对参数估计性能的影响,其估计精度优于经典的节省参数建模方法,并通过仿真得到：该算法确实有效。     

模糊自适应的高超飞行器控制与干扰

针对具有超大飞行马赫数、超宽飞行包络、飞行环境复杂等特点的高超声速飞行器,提出了基于模糊自适应的高超飞行器高度控制方法,分别设计了高度和速度控制器.针对所设计的控制器,分析了气动干扰力矩、测量噪声、舵机干扰、气动参数不确定性和飞行器模型参数不确定性对飞行器高度控制和速度控制效果的影响,然后又分析了综合考虑以上干扰和不确定性对飞行器高度控制和速度控制效果的影响.仿真结果表明,对各种干扰和不确定性,所设计的控制器速度和高度控制有较好的跟踪性和鲁棒性,达到了满意的效果.     

Adaptive fuzzy decentralized control for nonlinear large-scale systems based on high-gain observer

An adaptive fuzzy decentralized backstepping output-feedback control approach is proposed for a class of nonlinear large-scale systems with completely unknown functions,the interconnections mismatched in control inputs,and without the measurements of the states.Fuzzy logic systems are employed to approximate the unknown nonlinear functions,and an adaptive high-gain observer is developed to estimate the unmeasured states.Using the designed high-gain observer,and combining the fuzzy adaptive control theory with backstepping approach,an adaptive fuzzy decentralized backstepping output-feedback control scheme is developed.It is proved that the proposed control approach can guarantee that all the signals of the closed-loop system are semi-globally uniformly ultimately bounded(SUUB),and that the observer errors and the tracking errors converge to a small neighborhood of the origin by appropriate choice of the design parameters.Finally,a simulation example is provided to show the eectiveness of the proposed approach.     

基于去调频技术的斜视聚束SAR成像方法

针对具有方位谱混叠现象的斜视聚束式合成孔径雷达(SAR)成像问题,本文研究了一种基于去调频技术的成像方法.文中首先分析了斜视聚束SAR中的方位谱混叠现象,从理论上找出了斜视角影响方位粗聚焦的根本原因.基于分析结果,引入了回波信号非线性相位校正预处理法,彻底消除了斜视角的影响,从而得到正确的粗聚焦结果.然后基于斜视成像模型,推导了预处理回波信号的方位粗聚焦和精聚焦处理过程.在精聚焦的方位滤波处理中,针对非线性相位校正预处理导致的场景聚焦深度限制问题,提出了一种改进的非线性调频变标(NLCS)算法,实现了回波信号的高精度方位压缩.仿真实验结果证明了文中理论分析的正确性和所提方法的有效性.