多变量线性系统鲁棒非奇异自适应控制的改进

为解决多变量线性系统间接自适应控制的奇异性或不可控制性问题,在文[1]的基础上提出了一种新的修正模型辨识参数方法,该参数修正策略使系统辨识模型在适应过程中保持能控性,此修正算法较文[1]计算是大为减少,进一步提高了控制逄法的时性。     

基于动态遗忘因子递推最小二乘算法的船舶航向模型辨识

为提高遗忘因子递推最小二乘（RLS）算法辨识船舶航向运动数学模型参数的快速性和鲁棒性,在分析遗忘因子大小对算法特性影响的基础上,提出一种基于模糊控制的动态遗忘因子RLS算法。该算法从理论模型输出与实际模型输出之间的残差入手来构造评估参数辨识误差大小的评价函数,并将评价函数及其变化率作为模糊控制器的输入,利用模糊控制器结合制定的规则表进行模糊推理并计算遗忘因子的修正量,从而实现遗忘因子的动态调整。仿真结果表明,与恒定遗忘因子RLS算法的对比,该算法能够根据参数辨识误差实时调整遗忘因子的大小,使算法在模型参数平稳时有更高的辨识精度,在模型参数突变时有更快的收敛速度,验证了所提算法的优越性。     

基于PPSO的精馏塔模型参数闭环辨识

针对粒子群优化算法(particle swarm optimization,PSO)在高维空间复杂曲面寻优时易于陷入局部最小值的问题,分组扰动粒子群优化算法(partially-perturbed particle swarm optimization,PPSO)结合问题特征,采用启发式规则,实施参数分组扰动策略,对PSO算法进行改进,从而增大了跳出局部极小的可能性。本文主要研究PPSO在精馏塔模型参数闭环辨识上的应用,分别针对模型参数可辨识性,参数的不同分组,鲁棒性进行分析验证;并在其他精馏塔模型上进行了相应的验证。仿真实验表明,PPSO辨识算法比序列近似法等其它辨识算法具有更高的辨识精度,并且具有较强的鲁棒性;在其他精馏塔模型参数辨识上PPSO算法也同样取得了很好的辨识精度。实验结果证明了PPSO算法在精馏塔模型参数闭环辨识上的可行性和有效性。     

一种非线性模型的在线辨识方法

提出一种用于非线性模型在线辨识的模糊算法。该算法将非线性输入输出系统用时变线性系统模型来拟和。并把此非线性系统模型表示成模糊模型的形式,用在线调节模糊模型的方法来辨识时变线性模型的相关参数。在以往的模糊辨识方法中,均未给出在线调整非线性系统的模糊辨识算法。将递推模糊聚类方法与卡尔曼滤波法用于在线调整模糊模型参数,仿真算例表明了此算法的有效性与良好的实用价值。     

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     

统计机器学习中参数可辨识性研究及其关键问题

参数可辨识性研究在统计机器学习中具有重要的理论意义和应用价值.参数可辨识性是关于模型参数能否被惟一确定的性质.在包含物理参数的学习模型中,可辨识性不仅是物理参数获得正确估计的前提条件,更重要的是,它反映了学习机器中由参数决定的物理特征.为扩展到未来类人智能机器研究的考察视角,我们将学习模型纳入"知识与数据共同驱动模型"的框架中讨论.在此框架下,我们提出两个关键问题.第一是参数可辨识性准则问题.该问题考察与可辨识性密切相关的各种判断准则,其中知识驱动子模型与数据驱动子模型的耦合方式为参数可辨识性问题提供了新的研究空间.第二是参数可辨识性与机器学习理论和应用相关联的研究.该研究包括可辨识性对参数估计、模型选择、学习算法、学习动态过程、奇异学习理论、贝叶斯推断等内容的深刻影响.     

一种新型的过程模型参数辨识方法

针对模型参数辨识问题,提出了一种基于菌群优化（BSFO）算法的模型参数辨识方法。通过将辨识参数设置为群体细菌在参数空闸的位置,并模拟细菌群体觅食的动态行为来实现对参数的寻优,有效地提高了参数辨识的精腰和效率。对火电厂热工过程参数辨识的仿真研究验证了本文算法的有效性,结果表明,菌群优化算法能够实现对过程模型参数的有效辨识,仿真结果令人满意。     

基于模糊模型的非线性离散时间系统辨识:算法与性能分析

讨论使用模糊系统方法辨识非线性离散时间系统时,模糊系统模型的构造、逼近性 质以及模型参数的自适应调整算法.研究了该辨识方案的有关性能,对模糊模型的参数误差 和辨识误差进行了分析,并给出了模糊模型参数的估计值收敛到其真实值所需的持续激励条 件.     

一种新型的非线性系统模型参数辨识方法

针对传统模型参数辨识方法和遗传算法用于模型参数辨识时的缺点,提出了一种基于微粒群优化(PSO)算法的模型参数辨识方法,利用PSO算法强大的优化能力,通过对算法的改进,将过程模型的每个参数作为微粒群体中的一个微粒,利用微粒群体在参数空间进行高效并行的搜索来获得过程模型的最佳参数值,可有效提高参数辨识的精度和效率.     

基于改进粒子群算法的超级电容参数辨识

针对超级电容的模型参数辨识不准确问题,首先分析了超级电容单体的储能原理和性能特点,将二分支等效模型作为超级电容的模型,然后使用最小二乘算法和改进粒子群算法对模型参数进行辨识,最后通过仿真和实验比较两种算法辨识效果,证实该文所提出的改进粒子群算法更能准确地辨识出超级电容模型参数。     

Constrained max-min controllability

The max-min controllability concept of Heymann, Pachter, and Stern [1] is examined for a more general class of problems. Both strong and weak max-min controllability are characterized. It is shown that when magnitude constraints on the control are present, the two max-min controllability concepts are not the same. This differs from the result for problems without control magnitude constraints where it has been shown that the two controllability concepts are identical.     

多智能体系统能控性研究进展

能控性问题是多智能体协调控制领域中一个基本又十分重要的研究课题.本文对多智能体系统能控性问题的研究现状进行综述.介绍了多智能体能控性领域的基本问题和特点,并结合智能体自身动力学与邻居交互协议,从拓扑结构角度对该领域当前的研究热点和前沿进行分析阐述.进一步,对结构能控性的研究成果进行归纳总结,并对能观测性、可镇定性和复杂网络能控性等相关问题进行阐述.最后给出了仍需解决的问题和可能的研究方向.     

The Bang‐bang principle of time optimal controls for the Kuramoto–Sivashinsky–KdV equation with internal control

This paper is concerned with the time optimal control problem governed by the internal controlled Kuramoto–Sivashinsky–Korteweg‐de Vries equation, which describes many physical processes in motion of turbulence and other unstable process systems. We prove the existence of optimal controls with the help of the Carleman inequality, which has been widely used to obtain the local controllability or null controllability of parabolic differential systems. More precisely, with the help of the Carleman inequality, we obtain a relationship between the null controllability and time optimal control problem. Moreover, we give the bang‐bang principle for an optimal control of our original problem by using the one of approximate problems. This method is new for time optimal control problems. The bang‐bang principle established here seems also to be new for fourth‐order parabolic differential equations. Copyright © 2015 John Wiley & Sons, Ltd.     

Controllability and Optimization of Pseudohyperbolic Systems

The problems of singular optimization and controllability of systems described by the boundary-value problems for pseudohyperbolic equations are studied. The existence and uniqueness theorems for solutions to basic boundary-value problems for pseudohyperolic equations in the nonclassic Sobolev-like spaces under wide assumptions as to right-hand sides are proved. Based on these results, final impulse-point controllability of the systems and existence of optimal control are studied.     

A study of controllability and time-optimal control of a robotmodel with drive train compliances and actuator dynamics

The problems of robot controllability and time-optimal control where drive train compliances and actuator dynamics are incorporated in the mathematical model are considered. Conditions that ensure the existence of a time-optimal control, are demonstrated, and controllability of the augmented model (robot and actuator) in open-and-closed-loop form is established. A procedure is described for the derivation of easily computable functional inequalities which represent upper bounds on the norm of the augmented system's time response     

Impulsive semilinear heat equation with delay in control and in state

In this paper, we prove the interior approximate controllability of the impulsive semilinear heat equation with delay in control and in state by proving first that the linear heat equation with delay in control is approximately controllable. After that, we add impulses and a nonlinear perturbation with delay in state, and using Rothe's fixed point theorem, we prove that the interior approximate controllability of the impulsive semilinear system. Finally, we present some open problems and a possible general framework to study the controllability of impulsive semilinear diffusion process in Hilbert spaces with delay in control and in state.     

Approximate controllability of fractional stochastic evolution equations

A class of dynamic control systems described by nonlinear fractional stochastic differential equations in Hilbert spaces is considered. Using fixed point technique, fractional calculations, stochastic analysis technique and methods adopted directly from deterministic control problems, a new set of sufficient conditions for approximate controllability of fractional stochastic differential equations is formulated and proved. In particular, we discuss the approximate controllability of nonlinear fractional stochastic control system under the assumptions that the corresponding linear system is approximately controllable. The results in this paper are generalization and continuation of the recent results on this issue. An example is provided to show the application of our result. Finally as a remark, the compactness of semigroup is not assumed and subsequently the conditions are obtained for exact controllability result.     

Graph-theoretic approach to controllability and localizability of decentralized control systems

The controllability and the localizability problems are considered under the decentralized information structure using some concepts from graph theory. First of all, the information structure graph is introduced for a decentralized control system based upon local output controllability, matrices between stations when a local output feedback law is applied, and some invariant properties of the graph with respect to local output feedback laws are driven. Then by using these results, necessary and sufficient conditions of controllability under decentralized information structure are obtained. Also obtained are several conditions for localizability, of decentralized control systems, which is newly introduced in the present paper as an extended concept of a class of disturbance localization and decoupling for centralized systems.     

An application of implicit function theorems to controllability problems of nonstationary discrete-time systems

Pshenichnyi's implicit function theorem for multivalued mappings is applied to controllability problems for some classes of nonstationary discrete-time systems. New necessary and sufficient conditions of local controllability are obtained for linear discrete-time systems with control constraints.Translated from Kibernetika, No. 4, pp. 55–58, July–August, 1990.     

Pinning control and controllability of complex dynamical networks

In this article, the notion of pinning control for directed networks of dynamical systems is introduced, where the nodes could be either single-input single-output (SISO) or multi-input multi-output (MIMO) dynamical systems, and could be non-identical and nonlinear in general but will be specified to be identical linear time-invariant (LTI) systems here in the study of network controllability. Both state and structural controllability problems will be discussed, illustrating how the network topology, node-system dynamics, external control inputs and inner dynamical interactions altogether affect the controllability of a general complex network of LTI systems, with necessary and sufficient conditions presented for both SISO and MIMO settings. To that end, the controllability of a special temporally switching directed network of linear time-varying (LTV) node systems will be addressed, leaving some more general networks and challenging issues to the end for research outlook.