On absolute stability of Lur'e control systems with multiple non-linearities using linear matrix inequalities

Necessary and suffcient conditions for the existence of a Lyapunov function in the Lur ’ e form to guarantee the absolute stability of Lur’ e control systems with multiple non-linearities are discussed in this paper. It simplifies the existence problem to one of solving a set of linear matrix inequalities (LMIs). If those LMIs are feasible, free parameters in the Lyapunov function, such as the positive definite matrix and the coefficients of the integral terms, are given by the solution of the LMIs. Otherwise, this Lyapunov function does not exist. Some sufficient conditions are also obtained for the robust absolute stability of uncertain systems. A numerical example is provided to demonstrate the effectiveness of the proposed method.

Linear matrix inequality approach to exponential synchronization of a class of chaotic neural networks with time-varying delays

In this paper, a synchronization scheme for a class of chaotic neural networks with time-varying delays is presented. This class of chaotic neural networks covers several well-known neural networks, such as Hopfield neural networks, cellular neural networks, and bidirectional associative memory networks. The obtained criteria are expressed in terms of linear matrix inequalities, thus they can be efficiently verified. A comparison between our results and the previous results shows that our results are less restrictive.

On stabilization for a class of nonlinear stochastic time-delay systems:a matrix inequality approach

This paper treats the feedback stabilization of nonlinear stochastic time-delay systems with state and control-dependent noise. Some locally （globally） robustly stabilizable conditions are given in terms of matrix inequalities that are independent of the delay size. When it is applied to linear stochastic time-delay systems, sufficient conditions for the state-feedback stabilization are presented via linear matrix inequalities. Several previous results are extended to more general systems with both state and control-dependent noise, and easy computation algorithms are also given.

Optimal Nonlinear Robust Sliding Mode Control of an Excitation System Based on Mixed Linear H_(2)/H_(∞) Matrix Inequalities

In this paper,an optimal nonlinear robust sliding mode control(ONRSMC)based on mixed H_(2)/H_(∞)linear matrix inequalities(LMIs)is designed for the excitation system in a“one machine-infinite bus system”(OMIBS)to enhance system stability.Initially,the direct feedback linearization method is used to establish a mathematical model of the OMIBS incorporating uncertainties.ONRSMC is then designed for this model,employing the mixed H_(2)/H_(∞)LMIs.The chaos mapping-based adaptive salp swarm algorithm(CASSA)is introduced to fully optimize the parameters of the sliding mode control,ensuring optimal performance under a specified condition.CASSA demonstrates rapid convergence and reduced like-lihood of falling into local optima during optimization.Finally,ONRSMC is obtained through inverse transformation,exhibiting the advantages of simple structure,high reliability,and independence from the accuracy of system models.Four simulation scenarios are employed to validate the effectiveness and robustness of ONRSMC,including mechanical power variation,generator three-phase short circuit,transmission line short circuit,and generator parameter uncertainty.The results indicate that ONRSMC achieves optimal dynamic performance in various operating conditions,facilitating the stable operation of power systems following faults.

Linear matrix inequality approach for synchronization control of fuzzy cellular neural networks with mixed time delays

Fuzzy cellular neural networks （FCNNs） are special kinds of cellular neural networks （CNNs）. Each cell in an FCNN contains fuzzy operating abilities. The entire network is governed by cellular computing laws. The design of FCNNs is based on fuzzy local rules. In this paper, a linear matrix inequality （LMI） approach for synchronization control of FCNNs with mixed delays is investigated. Mixed delays include discrete time-varying delays and unbounded distributed delays. A dynamic control scheme is proposed to achieve the synchronization between a drive network and a response network. By constructing the Lyapunov-Krasovskii functional which contains a triple-integral term and the free-weighting matrices method an improved delay-dependent stability criterion is derived in terms of LMIs. The controller can be easily obtained by solving the derived LMIs. A numerical example and its simulations are presented to illustrate the effectiveness of the proposed method.

Linear matrix inequality approach for robust stability analysis for stochastic neural networks with time-varying delay

This paper studies the problem of linear matrix inequality （LMI） approach to robust stability analysis for stochastic neural networks with a time-varying delay. By developing a delay decomposition approach, the information of the delayed plant states can be taken into full consideration. Based on the new Lyapunov-Krasovskii functional, some inequality techniques and stochastic stability theory, new delay-dependent stability criteria are obtained in terms of LMIs. The proposed results prove the less conservatism, which are realized by choosing new Lyapunov matrices in the decomposed integral intervals. Finally, numerical examples are provided to demonstrate the less conservatism and effectiveness of the proposed LMI method.

Solution Building for Arbitrary System of Linear Inequalities in an Explicit Form

The known Fourier-Chernikov algorithm of linear inequality system convolution is complemented with an original procedure of all dependent (redundant) inequalities deletion. The concept of “almost dependent” inequalities is defined and an algorithm for further reducing the system by deletion of these is considered. The concluding algorithm makes it possible to hold actual-time convolution of a general inequality system containing up to 50 variables with the rigorous method of dependent inequalities deletion and up to 100 variables with the approximate method of one. The main application of such an approach consists in solving linear inequality system in an explicit form. These results are illustrated with a series of computer experiments.

Event-Triggered Finite-Time H∞ Filtering for Discrete-Time Nonlinear Stochastic Systems

This paper addresses the problem of event-triggered finite-time H∞ filter design for a class of discrete-time nonlinear stochastic systems with exogenous disturbances. The stochastic Lyapunov-Krasoviskii functional method is adopted to design a filter such that the filtering error system is stochastic finite-time stable (SFTS) and preserves a prescribed performance level according to the pre-defined event-triggered criteria. Based on stochastic differential equations theory, some sufficient conditions for the existence of H∞ filter are obtained for the suggested system by employing linear matrix inequality technique. Finally, the desired H∞ filter gain matrices can be expressed in an explicit form.

非线性广义时滞Markov跳变系统的H_(∞)滑模控制

研究了一类转移速率部分未知的广义时滞Markov跳变系统的H_(∞)滑模控制问题.首先,对广义时滞Markov跳变系统设计了积分型滑模面,并求出对应的等效控制器.之后,构建Lyapunov泛函,通过线性矩阵不等式方法得到闭环系统随机容许且满足H_(∞)性能γ的充分条件,运用加减项方法以及引入自由权矩阵,使得到的结果降低了保守性;对滑模力学分析,设计了滑模控制器,使得系统能在有限时间内到达滑模面上.最后,通过数值算例与仿真验证了理论的可行性.

基于LMI和扰动观测器的电动伺服系统RBF神经网络控制

为了提高电动伺服系统的加载力跟踪精度,基于线性矩阵不等式(LMI)设计扰动观测器和控制器。针对系统中的非线性因素,采用RBF神经网络逼近系统的数学模型;在建立系统跟踪目标模型的基础上,根据LMI设计扰动观测器对控制器进行多余力的补偿,利用李雅普诺夫函数证明扰动观测器和控制器的收敛;在MATLAB/Simulink中搭建仿真模型,分析扰动观测器和RBF神经网络在不同工况下对系统相应量的精准估计,且误差均满足所设定的性能指标,同时与PID控制相比较,证明所提控制策略的控制性能更优。

执行器随机失效系统鲁棒预测容错切换控制

针对一类具有执行器随机失效问题的离散线性系统,提出一种基于故障概率情况下的鲁棒预测容错切换控制方法。首先,将工业过程建立成新型多自由度状态空间模型,设计含有故障概率的容错控制器;其次,引入系统故障和其恢复时的随机概率,利用李雅普诺夫判据给出基于线性矩阵不等式形式的稳定性条件,再通过指数稳定的相关证明求解出不同执行器切换时的稳定条件,以保证系统故障时容错控制与无故障时常规控制间的切换;然后,控制器设计时还充分考虑了设定值变化时所产生的跟踪误差带来的影响。最后,通过仿真结果验证了所提方法的可行性。

具有执行器故障的网络控制系统的非脆弱保性能控制

对短时变时延下具有执行器故障的网络控制系统的鲁棒非脆弱保性能控制问题进行研究。为了降低保守性,利用标称点方法对系统进行建模,并结合时变故障矩阵与非脆弱控制,将连续时间系统模型转变为具有范数有界不确定参数的时变离散时间模型。根据Lyapunov对离散系统的稳定性判据以及线性矩阵不等式技术,给出保性能控制器的设计策略和相应的二次性能指标的求解方法,并通过算例验证了所提策略的正确性。

马尔可夫跳变系统的有限时间保性能控制

本文主要研究具有时变时滞的脉冲随机马尔可夫跳变系统的有限时间保性能控制问题.通过选取模态相关的李雅普诺夫泛函,使用线性矩阵不等式(LMI)、平均驻留时间方法等技术,得到一些新的充分条件来确保系统有限时间稳定,并给出了系统的上界性能指标.基于该充分条件设计了系统的状态反馈保性能控制器.最后,利用Matlab的LMI工具箱进行数据仿真,得到相应的均方轨迹图.数值例子结果表明,得到的仿真结果和理论结果一致,验证了文中理论的有效性.

奇异摄动区间变时滞不确定非线性系统稳定性分析

奇异摄动系统是实际问题中普遍存在的一类系统,为了使此类系统在应用上达到一定的稳定,利用矩阵分块方法对含有不确定结构的奇异摄动区间变时滞非线性系统稳定性进行分析,通过构造新的Lyapunov函数,基于Lyapunov稳定性理论,结合舒尔补引理、积分不等式和线性矩阵不等式等方法推出奇异摄动系统在摄动参数界内时滞相关的稳定性判据.

基于LMI和BP网络的非线性矩阵加权次优融合算法

为了解决互协方差未知的多传感器非线性系统融合估计问题,提出了一种改进的矩阵加权次优融合算法。利用舒尔补定理推导出线性最小方差意义下基于矩阵融合的最简约束条件。此约束条件可保证融合估计误差方差的正定性,以及所提出次优融合估计的一致性;基于线性矩阵不等式(LMI)提出了一种矩阵加权次优融合估计。考虑到LMI算法优化过程中存在的耗时问题,采用BP网络获取最优值;结合容积卡尔曼滤波算法(CKF),提出了基于LMI算法和BP网络的非线性矩阵加权次优融合算法。仿真分析结果证明了算法应用于非线性系统的有效性。

一类区间时滞忆阻神经网络的稳定性控制

鉴于忆阻神经网络(memristive neural network,MNN)的数学模型是一种右端不连续的微分方程,经典的微分方程理论不再适用。借助微分包含理论和Filippov解的框架,将这一神经网络系统转化为通常的神经网络系统,并建立了时滞一次函数形式的新型李雅普诺夫-克拉索夫斯基泛函(Lyapunov-Krasovskii functional,LKF),运用积分不等式和倒数凸组合不等式估计其导数,得到了该系统渐近稳定的充分条件并以线性矩阵不等式(linear matrix inequalities,LMIs)形式给出,便于用MATLAB软件验证。数值仿真结果表明,所得到结论正确且可实现。

一类时滞多智能体系统的事件触发容错控制

针对时变通讯时滞下,具有执行器故障和参数不确定性的多智能体系统事件触发一致性控制问题,利用线性矩阵不等式(LMI)技术,设计了反馈控制增益矩阵;为降低通信资源的消耗,设计了基于模型的动态事件触发机制;通过李雅普诺夫稳定性理论证明了容错控制算法在间歇通信的情况下实现了多智能体系统一致性,且排除了芝诺行为。

采用PSOGSA算法的水电机组调节系统非线性鲁棒控制研究

针对含有非线性与不确定性水电机组调节系统,本文提出并设计了基于粒子群与引力搜索混合算法(PSOGSA)的非线性鲁棒H2/H∞控制策略对其进行控制。首先,建立了水电机组调节系统整体精细化模型,其中水轮机模型采用BP神经网络描述予以描述。然后,采用输出反馈线性化理论以及线性矩阵不等式(LMI)设计非线性鲁棒H_(2)/H_(∞)控制器,以确保系统在存在扰动情况下具有良好的鲁棒H_(2)/H_(∞)性能。此外,为了使控制器的控制性能最优化,通过PSOGSA算法对控制器加权函数参数进行寻优操作。最后,通过仿真表明,本文提出的基于PSOGSA的非线性鲁棒H_(2)/H_(∞)控制策略在提高水电机组调节系统的稳定性以及抗干扰能力上具有优势,也为实际水电机组控制应用提供一种有效的参考方案。

基于事件触发的奇异semi-Markov跳变系统的有限时间控制

为减少不必要的网络资源传输,解决实际复杂系统的有限时间问题,提出一种基于事件触发方案的奇异semi-Markov跳变系统的有限时间控制器设计方法。在事件触发方案下,用系统的时滞表示网络诱导时延,闭环系统可以建模为一个时滞系统。首先,通过构造合适的Lyapunov-Krasovskii泛函,以严格矩阵不等式的形式给出充分条件,以使事件触发方案下的闭环系统正则、无脉冲、随机有限时间有界;然后,通过求解矩阵不等式,得到状态反馈控制器的参数;最后,给出一个数值示例,通过仿真证明所提方法的有效性。

船舶动力定位非线性模型预测控制

针对受船舶运动状态和推力饱和约束影响的动力定位控制问题,本文提出了一种非线性模型预测控制方法。依据多面体描述理论建立了船舶动力定位多面体描述偏差模型,针对上述模型采用线性矩阵不等式设计非线性模型预测控制器中的终端要素,并证明了闭环系统的稳定性。仿真结果表明:设计的控制器能在满足约束条件和受环境干扰条件下快速控制船舶稳定在计划航向和计划船位,验证了控制器的有效性和本质鲁棒性。