分布时延网络控制系统的H_∞优化控制

研究了具有分布时延和扰动的网络控制系统H_∞优化控制问题.连续系统中的网络时延必须分开讨论.针对线性时不变被控对象,基于连续动态输出反馈控制器,建立了同时含有连续项和离散项的混合系统模型.利用Lyapunov-Krasovskii泛函,推导出系统可实现γ-次优H_∞镇定的充分条件.通过引入等式约束的方法将其转化为线性矩阵不等式,给出了系统H_∞次优控制器参数和扰动衰减度的求取方法,并将该系统H_∞最优控制问题转化为线性目标最小化问题.仿真算例说明了分析方法和结果是有效可行的.     

基于FHM模型的非线性网络化控制系统H_∞优化控制

研究了一类具有随机网络诱导时延,数据包丢失和扰动的网络化控制系统H_∞优化控制问题.针对一类非线性被控对象,基于状态反馈控制器,建立了网络化模糊双曲正切混杂系统模型.根据Lyqpunov稳定性理论,给出了系统可实现γ-次优H_∞镇定的充分条件和控制器优化参数的获取方法.仿真算例利用神经网络算法和线性矩阵不等式工具箱,完成了模型参数的辨识与最优扰动衰减度的求取,证明了分析方法和结果的有效性.     

广义连续系统极小极大控制反问题初探

将极小极大控制的反问题推广到广义系统.针对给定的性能指标,通过受限等价变换,得到判别广义连续系统控制器是极小极大控制器的充要条件及其等价的频率条件.最后针对广义系统H∞次优控制的反问题进行了讨论.     

线性二次高斯系统的奇异对策

本文讨论量测噪声协方差阵和控制作用加权阵可能退化时的二次高斯系统的二人零和随机对策,给出了统一适用的次优策略,当系统成为非退化时,次优策略就收敛到最优策略.     

半环的L-模糊理想的范畴性质

首先给出了半环的L-模糊理想同态的定义,在此基础上较系统地讨论了半环的L-模糊理想范畴的性质,证明了此范畴是半环范畴上的一个拓扑结构,并探讨了其中的等子、拉回和乘积等性质.另一方面,给出了半环的L-模糊理想范畴的逆系统的定义,建立了半环的L-模糊理想范畴中逆系统的逆极限结构.特别是在引入两个逆系统之间映射的基础上,得到了两个逆系统的逆极限之间的极限映射.     

对合Quantale范畴的逆系统

本文得到了对合Quantale范畴逆系统及逆极限的结构,引入了两个逆系统之间映射的概念,给出了逆极限之间的极限映射,并研究了它们的若干性质.     

非自治逆紧系统的拓扑压

主要研究了非自治逆紧系统上的拓扑压.给出了非自治逆紧系统上拓扑压的定义,得到了这种拓扑压关于集合Z的一些性质,并在同胚意义下,探讨了两个非自治逆紧系统上拓扑压的大小关系.     

一类参数不确定系统的输出反馈自适应稳定控制设计

研究一类n阶、相对阶为n-m(n为任意自然数,m≤n为非负整数)参数不确定系统的输出反馈自适应镇定控制设计问题.所给出的控制器适用于相对阶为n-m的n阶系统.通过引入一个新型的3n+2m阶动态补偿器,构造性地给出了输出反馈自适应镇定控制器的显式表达.所设计的控制器使得闭环系统所有信号有界或渐近稳定.     

平面运动n级倒立摆的鲁棒保成本模糊控制

针对参数不确定平面运动n级倒立摆系统,研究了鲁棒保成本模糊控制器设计问题。给出了将系统动力学模型转化为一种新型广义T-S模糊模型的方法,由此避免了求逆矩阵的过程。为获得保守性更低的系统稳定性设计条件,通过引入松弛变量矩阵来解耦Lyapunov函数中的系统矩阵,增加了设计条件的自由度。然后讨论了最优鲁棒保成本模糊控制器的设计条件,并将这些条件转换成了线性矩阵不等式(LMI)的凸优化问题。最后,在仿真实验中以平面运动二级倒立摆系统为例验证了本文方法的有效性。     

MP弱Core逆的性质和应用

基于Moore-Penrose逆和弱Core逆该文提出了Moore-Penrose弱Core逆(MPWC逆)的概念.分别从代数和几何角度对它进行刻画,给出了MPWC逆与非奇异加边矩阵之间的关系,应用Hartwig-Spindelb?ck分解和Core-EP分解给出了MPWC逆的性质、刻画及其扰动分析,最后给出了矩阵的MPWC逆是EP矩阵的等价条件.     

Inverse optimal control and construction of control Lyapunov functions

In this paper, the construction of CLFs for nonlinear systems and a new inverse optimal control law are presented. The construction of a CLF for an affine nonlinear system is reduced to the construction of a CLF for a simpler system, and a new L _g V type control law with respect to a CLF is provided. This control law is a generalization of Sontag’s formula and contains a design parameter. Tuning this parameter gives many suboptimal solutions for the optimization problem. Also, the gain margin and sector margin of the control law are calculated. Examples are provided to illustrate the main theoretical results of the paper. Translated from Sovremennaya Matematika i Ee Prilozheniya (Contemporary Mathematics and Its Applications), Vol. 61, Optimal Control, 2008.     

Suboptimal Regulators for Discrete-Time Jump Linear Systems with Time-Multiplied Performance Index

This paper addresses the suboptimal regulator design problem of a discrete-time jump linear system by using a time-multiplied performance index. For a given state feedback control with constant gain matrices, the time-multiplied performance index is derived and a necessary condition for such a control to minimize the time-multiplied cost is provided. A gradient-based algorithm to find a suboptimal control law is developed and some numerical results are given to show the usefulness of the theoretical results.     

Suboptimal Control of Time-Varying Neutral Control Systems

In this paper,the optimal control of time-varying neutral control systems is investigated by means of the Maxmium Principle of time-delay systems.optimal control law is given. Then sub-optimal control of time-varying neutral control systems is studied by using sensitivity approach. Algorithm for suboptimal control is given.     

Taylor expansions of the value function associated with a bilinear optimal control problem

A general bilinear optimal control problem subject to an infinite-dimensional state equation is considered. Polynomial approximations of the associated value function are derived around the steady state by repeated formal differentiation of the Hamilton–Jacobi–Bellman equation. The terms of the approximations are described by multilinear forms, which can be obtained as solutions to generalized Lyapunov equations with recursively defined right-hand sides. They form the basis for defining a suboptimal feedback law. The approximation properties of this feedback law are investigated. An application to the optimal control of a Fokker–Planck equation is also provided.     

On a multiscale analysis of an inverse problem of nonlinear transfer law identification in periodic microstructure

This paper is devoted to the multiscale analysis of a homogenization inverse problem of the heat exchange law identification, which is governed by parabolic equations with nonlinear transmission conditions in a periodic heterogeneous medium. The aim of this work is to transform this inverse problem with nonlinear transmission conditions into a new one governed by a less complex nonlinear parabolic equation, while preserving the same form and physical properties of the heat exchange law that it will be identified, based on periodic homogenization theory. For this, we reformulate first the encountered homogenization inverse problem to an optimal control one. Then, we study the well-posedness of the state problem using the Leray–Schauder topological degrees and we also check the existence of the solution for the obtained optimal control problem. Finally, using the periodic homogenization theory and priori estimates, with justified choise of test functions, we reduce our inverse problem to a less complex one in a homogeneous medium.     

Successive approximation procedure of optimal tracking control for nonlinear similar composite systems

The optimal tracking control (OTC) problem for a class of affine nonlinear composite systems with similar structure is considered. By using a modeling technique, the nonlinear similar composite system is first transformed into some quasi-decoupled subsystems. Then the high-order, strongly coupled, nonlinear two-point boundary value (TPBV) problem is transformed into a sequence of linear decoupled TPBV problems through a successive approximation procedure. The obtained OTC law consists of an accurate linear term and a nonlinear compensation term which is the limit of the adjoint vector sequence. A suboptimal tracking control law is obtained by truncating a finite iterative result of the adjoint vector sequence as its nonlinear compensation term.     

Decentralized control with overlapping information sets

A decentralized control scheme is proposed for linear systems composed of overlapping subsystems. By expanding the state space of the system, a higher-dimensional space is formed where the subsystems appear as disjoint. In the expanded space, standard optimization techniques can be used to formulate a suboptimal decentralized control law for the overall system. A suitable contraction of the obtained control law, which is compatible with the information constraints imposed by the overlapping subsystems, can be implemented in the original system. The application of the proposed decentralized control scheme is illustrated using a 19th order model for load-frequency control of a two-area interconnected power system.The research reported herein was supported in part by the US Department of Energy, Electric Energy Division, under Contract No. EC-77-S-03-1493, and in part by the National Science Foundation under Grant No. ECS-8011210.     

On specific optimal control of systems with different information sets

The problem of reduced-order, observer-based control of systems with controllers having different information about the system is considered. The set of necessary conditions for optimality is obtained, and then the breakdown of the separation principle is demonstrated. The necessary conditions are simplified, and it is shown that a suboptimal control law based on separation can be obtained to satisfy the necessary conditions.The author is indebted to Professor C. T. Leondes for his continuous support, guidance, and stimulating discussions.     

Adaptive Inverse Control Using a Gradient-Projection Optimization Technique

As an application of an optimization technique, a gradient-projection method is employed to derive an adaptive algorithm for updating the parameters of an inverse which is designed to cancel the effects of actuator uncertainties in a control system. The actuator uncertainty is parametrized by a set of unknown parameters which belong to a parameter region. A desirable inverse is implemented with adaptive estimates of the actuator parameters. Minimizing an estimation error, a gradient algorithm is used to update such parameter estimates. To ensure that the parameter estimates also belong to the parameter region, the adaptive update law is designed with parameter projection. With such an adaptive inverse, desired control system performance can be achieved despite the presence of the actuator uncertainties.