参数不确定广义大系统的保性能分散控制

对一类范数有界时不变参数不确定的连续广义大系统和一个二次型性能指标,研究了其保性能分散控制问题.目的是设计一状态反馈分散控制器,使得对所有容许的不确定性,闭环系统不仅是鲁棒稳定的,而且性能指标有一上界.应用线性矩阵不等式方法,给出了一个用线性矩阵不等式表达的保性能分散控制器存在的充分条件;在此条件可解时,给出了保性能分散控制律的表达式.最后,举例说明了该方法的应用.     

基于T-S模型的倒立摆最优保性能模糊控制

对一类具有范数有界参数不确定性T-S模糊模型系统,采用状态反馈的并行分布补偿器(PDC)结构,基于线性矩阵不等式处理方法,研究了其最优保性能模糊控制律的设计问题.导出了保性能模糊控制律存在的条件,通过求解一个凸优化问题给出了最优保性能模糊控制律的设计方法,并用此方法设计了倒立摆系统的最优保性能模糊控制器.仿真实验验证了该设计方法的有效性.     

多时滞不确定系统的模糊保性能H∞控制

针对多时滞不确定系统,利用T-S模型进行建模,提出了模糊保性能H∞控制问题.基于Lyapunov稳定理论,得到一种通过模糊状态反馈实现的具有干扰抑制的保性能H∞控制.当被控对象存在多时滞和不确定性及外部干扰统计特性未知时,该模糊控制器能保证闭环系统稳定和一定的二次型性能指标上界,同时具有H∞下的干扰抑制作用.控制器的设计可通过转化为线性矩阵不等式的求解问题.数值仿真表明,该控制器具有所期望设计的性能.     

不确定非线性系统的时滞依赖保性能控制

针对一类基于T-S模糊模型表示的具有时变状态时滞和范数有界不确定性非线性系统,研究了时滞依赖保性能模糊控制器设计问题。对于用T-S模糊模型表示的非线性时滞系统,已知系统的时变时滞本身及其变化率的上界,采用并行分散补偿技术,通过选取合适的Lyapunov函数,推导了依赖时滞上界及变化率上界的时滞保性能模糊控制器存在的充分条件,进而通过建立和求解LMI（线性矩阵不等式）约束的凸优化问题,给出了保性能控制律的设计方法。数值算例表明了该方法的有效性。     

不确定性时滞大系统的分散鲁棒H_∞控制

研究一类具有状态时滞的内联不确定性动态大系统的分散鲁棒 H∞ 控制问题 .系统的不确定性参数满足范数有界条件 .得到了由无记忆状态反馈分散控制器使每一个子系统和整个大系统都可镇定且满足给定 H∞ 性能的充分条件 .所得结果与系统时滞的大小有关 ,并以线性矩阵不等式的形式给出     

不确定性时滞大系统的分散鲁棒H∞控制

研究一类具有状态时滞的内联不确定性动态大系统的分散鲁棒H∞控制问题.系统 的不确定性参数满足范数有界条件.得到了由无记忆状态反馈分散控制器使每一个子系统和 整个大系统都可镇定且满足给定H∞性能的充分条件.所得结果与系统时滞的大小有关,并 以线性矩阵不等式的形式给出.     

参数不确定T-S模糊交联系统的分散保成本控制

研究了参数不确定T-S模糊交联系统的分散保成本控制问题.对于给定系统所容许的所有不确定参数,设计了分散状态反馈保成本控制器,使得闭环系统不仅渐近稳定而且具有适当的性能指标上界.基于线性矩阵不等式(LM I)处理方法,给出了分散控制器存在的充分条件.仿真算例表明了所给方法的简易、有效.     

不确定性时滞系统H∞鲁棒保性能控制

在Riccati不等式的基础上,给出鲁棒保性能H∞不确定性时滞系统的定义.然后以性能指标上界最小为优化命题设计状态反馈控制器,使得闭环系统不仅对容许的参数不确定性以及时变时滞保持渐近稳定,而且具有给定H∞干扰衰减度以及保性能上界最优.数值实例及仿真结果表明了该方法的有效性和可行性.     

不确定关联大系统鲁棒分散可靠H∞控制

针对一类具有不确定性关联大系统,研究其鲁棒分散可靠状态反馈H∞控制器的设计方法.采用线性矩阵不等式的方法,给出了当控制器在规定范围内的一部分失效时控制器存在的充分条件,并能保证闭环系统可靠稳定和具有一定的H∞性能.仿真例子说明了方法的有效性.     

压水堆功率跟踪自适应保性能控制器设计

针对压水堆动态模型的高度非线性和不确定性特点,本文提出一种自适应保性能跟踪控制器(adaptive guaranteed cost control,AGCC)设计方法.首先以堆芯的点堆方程为基础,引入功率跟踪误差的积分项,构造反应堆的增广状态空间模型,再结合线性参数变化(linear parameter varying,LPV)理论,建立了堆芯系统的多胞LPV模型.该控制器的控制输入由状态反馈控制和不确定性补偿组成,结合保性能控制理论和多胞模型理论,求解线性矩阵不等式得到变增益状态反馈矩阵,确保闭环系统全局渐近稳定;利用李亚普诺夫稳定理论得到不确定性参数的自适应律,实现对系统不确定性的动态补偿.仿真结果表明,该控制器不仅对系统不确定项具有自适应性,而且有较好的负荷跟踪性能.     

Stability and stabilization of fuzzy large-scale systems

This paper investigates the stability and stabilization problems for the fuzzy large-scale system in which the system is composed of a number of Takagi-Sugeno fuzzy model subsystems with interconnections. The stability criterion of the fuzzy large-scale system without control is derived first. Instead of fuzzy parallel distributed compensation design, linear state feedback control is used in the stabilization work. Each local control is obtained from Riccati equation of the corresponding subsystem and the interconnection terms are used to determine the local feedback gain determination. Based on Lyapunov criterion, some conditions are derived under which the whole fuzzy large-scale system is stabilized asymptotically. A numerical example is given to illustrate the control design procedure and its effectiveness.     

离散模糊大系统的分散化PDC 控制器设计: LM I 方法

考虑了由N个相互关联的T—S子模糊系统组成的离散模糊大系统的分散镇定问题，给出了保证该模糊大系统闭环渐近稳定的分散化并行分布补偿(DPDC)控制器的设计方法，这些DPDC控制器的存在条件均以LMI的形式出现，LMI可通过MATLAB的LMI工具箱求解，因此提供了一条综合离散模糊大系统分散化PDC控制器的有效途径，仿真例子说明了所提出方法的有效性。     

基于线性矩阵不等式的参数不确定性关联模糊大系统的分散鲁棒镇定

讨论了参数不确定性关联模糊大系统的分散鲁棒镇定问题,所考虑的参数不确定性满足范数有界条件.基于李雅普诺夫稳定性理论及大系统分散控制理论,采用分散化PDC(parallel distributed compensation)控制器,给出了保证该关联模糊大系统闭环渐近稳定的LMI形式的充分条件,通过MATLAB软件中的LMI工具箱可求解出这些LMI中的控制器参数.仿真例子说明了所提方法的有效性.     

Decentralized PDC for large-scale T-S fuzzy systems

This paper studies the decentralized stabilization problem for a large-scale system. The considered large-scale system comprises of a number of subsystems and each subsystem is represented by a Takagi-Sugeno (T-S) fuzzy model. The interconnection between any two subsystems may be nonlinear and satisfies some matching condition. By the concept of parallel distributed compensation (PDC), the decentralized fuzzy control for each subsystem is synthesized, in which the control gain depends on the strength of interconnections, maximal number of the fired rules in each subsystem, and the common positive matrix P/sub i/. Based on Lyapunov criterion and Riccati-inequality, some sufficient conditions are derived and the common P/sub i/ is solved by linear matrix inequalities (LMI) so that the whole closed loop large-scale fuzzy system with the synthesized fuzzy control is asymptotically stable. Finally, a numerical example is given to illustrate the control synthesis and its effectiveness.     

Stability analysis of fuzzy large-scale systems with time delays in interconnections

The stability problem of fuzzy large-scale systems with time delays in interconnections is considered in this paper. The fuzzy large-scale system consists of J interconnected subsystems, which are represented by Takagi-Sugeno (T-S) fuzzy models. The stability conditions are derived using the Lyapunov-Krasovskii functional approach. This condition is independent of time delay and does not need the solution of a Lyapunov equation or Riccati equation. A stabilization approach for the delayed fuzzy large-scale systems through fuzzy state feedback-based controller is also presented in this paper. Finally, an example is given to demonstrate the result.     

分散混杂自适应模糊H∞控制器及其在自动化公路系统中的应用

本文针对一类SISO不确定非线性大系统,提出了一种混杂间接和直接自适应分散模糊H∞控制器.通过组合模糊系统和H∞跟踪技术开发的分散自适应模糊控制算法避免了控制设计中含有的符号函数.两种自适应模糊控制器的组合消除了它们各自均不能够同时融合被控对象知识与控制知识的局限.闭环大系统被证明是稳定的,且具有H∞跟踪性能.该算法应用于自动化公路系统中车辆的纵向跟随控制,仿真结果表明混杂自适应模糊H∞控制系统的跟踪性能更好而相应的控制幅值却更小.     

Robustness design of fuzzy control for nonlinear multiple time-delay large-scale systems via neural-network-based approach

The stabilization problem is considered in this correspondence for a nonlinear multiple time-delay large-scale system. First, the neural-network (NN) model is employed to approximate each subsystem. Then, a linear differential inclusion (LDI) state-space representation is established for the dynamics of each NN model. According to the LDI state-space representation, a robustness design of fuzzy control is proposed to overcome the effect of modeling errors between subsystems and NN models. Next, in terms of Lyapunov's direct method, a delay-dependent stability criterion is derived to guarantee the asymptotic stability of nonlinear multiple time-delay large-scale systems. Finally, based on this criterion and the decentralized control scheme, a set of fuzzy controllers is synthesized to stabilize the nonlinear multiple time-delay large-scale system.     

一类大系统的分散自适应模糊滑模控制

研究了一类具有未知函数控制增益的非线性大系统的分散模糊控制问题.基于滑模 控制原理和模糊集理论,提出了一种分散自适应模糊控制器的设计方法.通过理论分析,证明 了分散自适应模糊控制系统是全局稳定的,跟踪误差可收敛到零的一个领域内.     

Robust stabilization of nonlinear multiple time-delay large-scale systems via decentralized fuzzy control

To overcome the effect of modeling errors between nonlinear multiple time-delay subsystems and Takagi-Sugeno (T-S) fuzzy models with multiple time delays, a robustness design of fuzzy control is proposed in This work. In terms of Lyapunov's direct method, a delay-dependent stability criterion is hence derived to guarantee the asymptotic stability of nonlinear multiple time-delay large-scale systems. Based on this criterion and the decentralized control scheme, a set of model-based fuzzy controllers is then synthesized via the technique of parallel distributed compensation (PDC) to stabilize the nonlinear multiple time-delay large-scale system. Finally, a numerical example with simulations is given to demonstrate the concepts discussed throughout This work.     

Decentralised direct adaptive fuzzy control for a class of large-scale nonaffine nonlinear systems and application to AHS

A novel decentralised direct adaptive fuzzy controller design is presented for a class of large-scale nonaffine uncertain nonlinear systems in this article. By integrating a fuzzy logic system and H _∞ tracking technique, the designed controller is able to adaptively compensate for interconnections and disturbances with unknown bounds, but none of the control and adaptation laws contains a sign function so that control chattering can be shunned. The closed-loop large-scale system is guaranteed to be asymptotically stable and obtain good H _∞ tracking performance. The control approach developed is applied to the following control problem of a string of vehicles within an automated highway system (AHS) and simulation results verify its validity.