非线性对象的两种多模态控制方案

本文考虑了利用非线性系统多个工作点上的线性化模型来设计多模态控制器的问题。构造了两种类型的多模态控制器，参数内插型和输出内插型多模态控制器。由于综合利用了非线性系统多个工作点上的静态与动态信息，与传统的基于单个线性化模型设计的控制器相比较，在较大的范围内具有较好的性能。仿真例子表明了本文所设计的多模态控制器的有效性。     

含ARMA噪声系统模型的参数辨识方法*

实际问题中，大量的动态系统控制问题可归结为含ＭＡ，ＡＲＭＡ噪声系统模型的参数辨识问题。本文提出ＲＭＡ，ＲＡＲＭＡ两种系统模型参数辨识的一种新方法，主要手段是构造和研究特殊的辅助线性模型。理论分析和实际计算表明，本文方法较传统表度有明显提高。     

非线性系统的多模态ARMAX模型——一种基于插值理论的模型

基于多元多项式插值理论,提出了非线性系统的多模态模型,分别考虑了非平衡和平衡两 种方式下的多模态模型.在大范围内这两种模型均有较好的精度,特别是平衡式多模态模型 具有便于现场辨识和工程应用的特点.仿真结果表明了这种模态模型的有效性.     

基于C-R模糊模型的广义预测控制算法

该文对非线性系统的建模采用Cao-Ress(C-R)模糊模型，并用卡尔曼滤波算法在线辨识模糊模型的结论参数，从而减少了参数辨识的数量和避免了矩阵的求逆运算，然后在每一个采样点对该系统进行局部动态线性化，根据得到的系统线性化模型对系统采取广义预测控制(GPC)方法得到当前的控制动作。仿真结果表明了该方法的有效性。     

基于辨识ARMA模型的野值剔除方法与卡尔曼滤波修正算法

本文基于ＡＲＭＡ模型，提出了一种新的野值剔除方法。文中首先建立了新息过程的ＡＲＭＡ模型，再应用递推增广最小二乘方法，在线辨识ＡＲＭＡ模型的参数，并通过模型参数变化的检验函数，来判定是否出现了野值。文中同时提出了野值剔除后卡尔曼滤波的修正算法。作为应用，我们对雷达半主动导引头寻的制导系统的野值情况进行了仿真。仿真结果表明，这种基于辨识ＡＲＭＡ模型的野值剔除方法与野值剔除后的卡尔曼滤波修正算法能有效地     

带白色观测噪声的ARMA模型参数的无偏估计

本文研究了如何利用受白色噪声污染的观测数据辨识ＡＲＭＡ（ｐ，ｑ）模型参数数据的问题，提出了一种递推辅助变量法．利用这种方法首先辨识出ＡＲ（ｐ）部分的参数及观测噪声的方差，然后根据所得的估计利用常用的Ｎｅｗｔｏｎ－Ｒａｐｈｓｏｎ方法确定ＭＡ（ｑ）部分的参数。     

利用双通道格形滤波器同时辨识ARMA模型的参数和阶次

本文重新构造了一种最小二乘通道格形滤波器，并将它用于同时辨识ＡＲＭＡ模型的参数和阶次。数值仿真结果表明，这种辨识算法具有计算量小、精度高等优点。     

基于Simulink/Stateflow的无人机多模态飞行控制律仿真

为了验证无人机多模态飞行控制律设计的正确性,采用Simulink/Stateflow建模仿真方法;以某小型无人机为研究对象,首先在小扰动线性化模型基础上,设计了纵向和侧向多模态控制系统结构,并给出了相应的控制律,然后根据传统的频域和根轨迹的方法确定了各个控制器参数,最后通过Simulink/Stateflow完成整个飞行剖面的仿真,结果表明该方法能直观简洁地实现多模态之间切换的控制逻辑,模态控制误差均满足国军标要求,验证了所设计的多模态控制系统的正确性.     

多模态预训练模型综述

预训练模型（PTM）通过利用复杂的预训练目标和大量的模型参数,可以有效地获得无标记数据中的丰富知识。而在多模态中,PTM的发展还处于初期。根据具体模态的不同,将目前大多数的多模态PTM分为图像-文本PTM和视频-文本PTM;根据数据融合方式的不同,还可将多模态PTM分为单流模型和双流模型两类。首先,总结了常见的预训练任务和验证实验所使用的下游任务;接着,梳理了目前多模态预训练领域的常见模型,并用表格列出各个模型的下游任务以及模型的性能和实验数据比较;然后,介绍了M6(Multi-Modality to Multi-Modality Multitask Megatransformer)模型、跨模态提示调优（CPT）模型、VideoBERT(VideoBidirectionalEncoderRepresentationsfrom Transformers)模型和AliceMind(Alibaba’s collection of encoder-decoders from Mind)模型在具体下游任务中的应用场景;最后,总结了多模态PTM相关工作面临的挑战以及未来可能的研究方向。     

多模态控制技术在机坪自动供油中的应用

本文介绍了多模态控制在自动供油系统中的应用，及ＣＡＣＳ－９０００系统中多模态控制策略。运行结果表明，控制系统响应速度快，稳态精度高。     

基于模糊ARMAX模型的模糊建模

提出了一种利用MGS(modified Gram-Schmidt)算法建立模糊ARMAX模型的方法, 给出了基于MGS算法的模型结构和参数辨识的一体化方法. 利用MGS正交变换对通过GK模糊聚类的聚类结果进行变换, 确定对模型贡献大的规则, 删除对模型贡献小的规则, 同时对模型中的参数进行估计. 本文提出的方法能够实现模糊模型的结构和参数的优化. 仿真结果表明, 本文提出的方法能够建立非线性系统的模糊ARMAX模型.     

一种变步长CMAC的沉降NARMAX模型*

为了提高氧化铝生产质量和降低能耗,本文分析了氧化铝沉降工艺中影响沉降过程的各种因素,采用小脑模型神经网络(CMAC)系统辨识的方法建立沉降系统的带外部输入的自回归滑移模型(ARMAX)。针对CMAC收敛性存在的问题,提出了基于变步长小脑模型神经网络(CMAC)算法,通过双曲正割函数优化学习步长,提高了小脑模型神经网络算法的收敛速度和计算精度,进而优化了沉降槽密度ARMAX模型。仿真实验表明,该算法的ARMAX模型可以对沉降过程中的槽内密度进行准确识别,指导氧化铝的沉降生产操作。     

Conditions when minimum variance control is the optimal experiment for identifying a minimum variance controller

It is well known that if we intend to use a minimum variance control strategy, which is designed based on a model obtained from an identification experiment, the best experiment which can be performed on the system to determine such a model (subject to output power constraints, or for some specific model structures) is to use the true minimum variance controller. This result has been derived under several circumstances, first using asymptotic (in model order) variance expressions but also more recently for ARMAX models of finite order. In this paper we re-approach this problem using a recently developed expression for the variance of parametric frequency function estimates. This allows a geometric analysis of the problem and the generalization of the aforementioned finite model order ARMAX results to general linear model structures.     

A counterexample to the uniqueness of the asymptotic estimate in ARMAX model identification via the correlation approach

This paper deals with the identifiability of an ARMAX system when the correlation approach is adopted. In general, identifiability depends on both the parametrization of the model class and on the informativeness of the data. Here, we focus on the latter aspect and, therefore, a full-order model class is considered. The main goal is to provide a counterexample to the uniqueness of the asymptotic estimate when a persistently exciting input is adopted. This shows the somehow counterintuitive fact that the identifiability of ARMAX systems within the correlation approach is related to the “color” of the input.     

基于ARMAX模型自适应预测函数控制

本文提出了基于ARMAX模型的自适应预测函数控制,该算法的特点是占用内存少,计算 速度快,并具有较强的鲁棒性．ARMAX模型参数是通过带遗忘因子的递推最小二乘算法在线辨 识得到．仿真结果表明,该控制算法比PID控制具有更好的控制品质．     

Time-varying parametric system multiresolution identification by wavelets

In this paper, the problem of time-varying parametric system identification by wavelets is discussed. Employing wavelet operator matrix representation, we propose a new multiresolution least squares (MLS) algorithm for time-varying AR (ARX) system identification and a multiresolution least mean squares (MLMS) algorithm for the refinement of parameter estimation. These techniques can achieve the optimal tradeoff between the over-fitted solution and the poorly represented identification. The main features of time-varying model parameters are extracted in a multiresolution way, which can be used to represent the smooth trends as well as track the rapidly changing components of time-varying parameters simultaneously and adaptively. Further, a noisy time-varying AR (ARX) model can also be identified by combining the total least squares algorithm with the MLS algorithm. Based on the proposed AR (ARX) model parameter estimation algorithm, a novel identification scheme for time-varying ARMA (ARMAX) system is presented. A higher-order time-varying AR (ARX) model is used to approximate the time-varying ARMA (ARMAX) system and thus obtain an initial parameter estimation. Then an iterative algorithm is applied to obtain the consistent and efficient estimates of the ARMA (ARMAX) system parameters. This ARMA (ARMAX) identification algorithm requires linear operations only and thus greatly saves the computational load. In order to determine the time-varying model order, some modified AIC and MDL criterions are developed based on the proposed wavelet identification schemes. Simulation results verify that our methods can track the rapidly changing of time-varying system parameters and attain the best balance between parsimonious modelling and accurate identification.     

A new neural network-based approach for self-tuning control of nonlinear SISO discrete-time systems

This article presents a new neural network-based approach for self-tuning control of nonlinear single-input single-output (SISO) discrete-time dynamic systems. According to the approach, a neural network ARMAX (NN-ARMAX) model of the system is identified and continuously updated, using an online training algorithm. Control design is accomplished by solving an optimal discrete-time linear quadratic tracking problem using an observer-type linear state-space Kalman innovation model, which is built from the parameters of a local linear version of the NN-ARMAX model. The state-feedback control law is implemented using the Kalman state, which is calculated without estimating the noise covariance properties. The proposed control approach is shown to be very effective and outperforms the self-tuning control approach based on a linear ARMAX model on two simulation examples.     

Optimal tracking design for stochastic fuzzy systems

In general, fuzzy control design for stochastic nonlinear systems is still difficult since the fuzzy bases are stochastic so as to increase the difficulty and complexity of the fuzzy tracking control design. In this study, a fuzzy stochastic moving-average model with control input (fuzzy ARMAX model) is introduced to describe nonlinear stochastic systems. From the fuzzy ARMAX model, a fuzzy one-step ahead prediction model is developed. Based on a fuzzy one-step ahead prediction stochastic model, optimal design algorithms are proposed to achieve the optimal tracking of nonlinear stochastic systems. In this study, the minimum variance tracking control, generalized minimum variance tracking control, and the optimal model reference tracking control are developed for stochastic fuzzy systems. We construct some basic stability conditions for general stochastic fuzzy systems and use these conditions to verify the stability of the fuzzy tracking control systems. Finally, two simulation examples are given to indicate the performance of the proposed methods.     

Parameter identification of linear structural dynamic systems

The parameter estimations of linear multi-degree-of-freedom structural dynamic systems are carried out in time domain. Methods for estimating the system parameters and the modal parameters are presented. The equation of motion is transformed into the state space equation of the observable canonical form, and then into the auto-regressive and moving average model with auxiliary stochastic input (ARMAX) model to process the measurement data contaminated by the system noise as well as the output noise. The parameters of the ARMAX model are estimated by using the sequential prediction error method. Then, the parameters of equation of motion are recovered thereafter. In order to verify the accuracy of the estimation method, analytical simulation studies are performed for a model with two degrees of freedom on the basis of simulated data under various noise conditions. It is shown that the presented methods yield good estimates even under large noise conditions. The method is also applied to the identification of the modal parameters of a building model based on the experimental data.