一类离散切换模糊系统的稳定性

针对离散模糊系统,提出一类离散切换模糊系统的稳定性问题.使用切换技术及单Lyapunov函数、多Lyapunov函数方法构造出连续状态反馈控制器,使得相应的闭环系统渐近稳定,同时设计可以实现系统全局渐近稳定的切换律.模型中的每个切换系统的子系统是离散模糊系统,取常用的平行分布补偿PDC控制器,主要条件以凸组合和矩阵不等式的形式给出,具有较强的可解性.计算机仿真结果表明设计方法的可行性与有效性.     

不确定离散时间切换系统的鲁棒保性能控制

本文基于平均滞留时间和多Lyapunov函数方法,研究了一类线性不确定线性离散切换系统的保性能鲁棒控制问题,以线性矩阵不等式的形式设计了状态反馈保性能控制器使得相应的闭环系统对所有允许的不确定性是全局指数稳定的,并得到一个加权性能上界.仿真结果表明了该方法的有效性.     

输入饱和系统的离散增益调度控制及其在在轨交会中的应用

基于参量Lyapunov方法和不变集理论,针对具有输入饱和非线性约束的线性系统,提出了一种离散增益调度控制方法. 通过逐渐 增大代表闭环系统收敛速率参数的值,所提出的离散增益调度控制方法逐步加快闭环系统的收敛速度,达到改善闭环系统 动态性能的目的. 如果开环系统是非指数不稳定的,则所提出的离散增益调度控制器可实现半全局镇定;反之可实现局部镇定,并均可保证闭环系统的指数稳定性. 最后,将 所提出的方法应用于空间合作目标在轨交会控制系统的控制器设计,并直接在原始非线 性系统模型上进行仿真,结果验证了所提方法的有效性.     

基于向量Lyapunov函数方法的模糊控制系统稳定性分析和设计

采用模糊动态模型对连续时间非线性系统进行模糊控制,对闭环模糊系统的稳定性进行分析,并给出系统化的控制器设计程序,在一系列局部模型通过模糊隶属函数连接得到的连续的全局模型中,全面考虑其它关联子系统对标称线性系统的摄动,并利用向量Lyapunov函数的概念和方法,得到了闭环模糊系统稳定的充分条件;仿真例子验证了该设计方法的正确性。     

广义可镇定连续时延系统的通用连续性控制器设计

讨论确保闭环线性时延系统在Lyapunov意义下全局一致稳定的通用线性控制器 的设计问题.文中给出的通用镇定控制器适用于有限、无限和时变的点型、分布型及混合型等 各类时延系统.     

离散Hopfield双向联想记忆神经网络的稳定性分析

首先将离散Hopfield双向联想记忆神经网络转化成一个特殊的离散Hopfield网络 模型.在此基础上,对离散Hopfield双向联想记忆神经网络的全局渐近稳定性和全局指数稳 定性进行了新的分析.证明了神经网络连接权矩阵在给定的约束条件下有唯一的而且是渐近 稳定的平衡点.利用Lyapunov方程正对角解的存在性得到了几个判定平衡点为全局渐近稳 定和全局指数稳定的充分条件.这些条件可以用于设计全局渐近稳定和全局指数稳定的神经 网络.所做的分析扩展了以前的稳定性结果.     

广义双线性系统的终端滑模控制

研究广义双线性系统的终端滑模变结构控制问题.基于Lyapunov稳定性理论,运用Lyapunov函数方法,给出广义双系统的终端滑动模超出面,设计相应的终端滑模变结构控制器,使得闭环系统渐进稳定,实现滑动模运动,保证系统状态在有限时间内到达平衡点,得到广义双线性系统全局稳定的充分条件.所给的可行性算例,说明这一方法的有效性与可行性.     

脉冲时滞Hopfield神经网络的全局指数稳定性

研究一类具有脉冲控制的时滞Hopfideld神经网络的全局指数稳定性,通过Lyapunov-Krasovskii稳定性理论和Halanay不等式等方法,构造合适的Lyapunov泛函,利用不等式技巧得到了确保时滞神经网络在脉冲控制下全局指数稳定的一个充分条件,保证了Hofidd神经网络在脉冲控制下的全局指数稳定,并估计了系统的指数收敛率.为了便于计算和验证结论的有效性,给出一个简化的充分条件.最后通过数值实例的实验仿真证实了结论的有效性、可行性.     

考虑摩擦力影响精密伺服系统的鲁棒自适应控制

针对具有摩擦力扰动的精密伺服系统提出了一种鲁棒自适应控制方法.首先,对基于 bristle模型的动态摩擦力模型进行了线性参数化,该线性参数化过程包含了对stribeck效应的 线性参数化处理;然后,基于构造的Lyapunov函数设计全局渐近稳定自适应控制律,并对闭环 系统的跟踪性能进行了严格的理论分析.仿真实验验证了算法的有效性.     

带有混合时滞的切换系统的耗散性分析与控制

本文研究了一类具有不稳定子系统和混合时滞的切换系统的耗散性和指数镇定问题. 首先, 为了消除不稳 定子系统给这类时滞系统带来的不利影响, 采用了一种新颖的切换信号设计方法–将模态依赖平均驻留时间的慢切 换和快切换方法相结合, 并通过利用Lyapunov相关理论, 给出了全局指数稳定的充分条件. 然后, 利用耗散性理 论、多重Lyapunov-Krasovskii泛函技术、积分不等式、与Schur补引理等方法, 以线性矩阵不等式的形式给出了耗散 性能的相关判据, 使闭环系统实现全局指数稳定性的同时具有严格耗散性能. 进一步, 在给定扰动衰减水平的前提 下, 通过求解一些严格的LMI条件, 建立了一组可行控制器. 最后, 通过仿真实例验证了该方法的有效性.     

变周期采样系统指数稳定的新条件

本文研究了线性采样系统在变周期采样下的指数稳定性问题.基于离散时间Lyapunov理论,构造了一个新的类Lyapunov泛函.该泛函不仅是时变的,还增加了对状态二次项的积分,而且不要求在采样区间内正定.利用这一新的类Lyapunov泛函,本文首先针对一类非线性采样系统提出了指数稳定性定理,再结合改进的Wirtinger积分不等式,导出了使变周期线性采样系统指数稳定以及渐近稳定的线性矩阵不等式条件.最后举例说明了所得稳定性结果比现存的某些文献报道的结果保守性较小.     

A novel stability analysis of linear systems under asynchronous samplings

This article proposes a novel approach to assess the stability of continuous linear systems with sampled-data inputs. The method, which is based on the discrete-time Lyapunov theorem, provides easy tractable stability conditions for the continuous-time model. Sufficient conditions for asymptotic and exponential stability are provided dealing with synchronous and asynchronous samplings and uncertain systems. An additional stability analysis is provided for the cases of multiple sampling periods and packet losses. Several examples show the efficiency of the method.     

Exponential stability of impulsive systems with application to uncertain sampled-data systems

We establish exponential stability of nonlinear time-varying impulsive systems by employing Lyapunov functions with discontinuity at the impulse times. Our stability conditions have the property that when specialized to linear impulsive systems, the stability tests can be formulated as Linear Matrix Inequalities (LMIs). Then we consider LTI uncertain sampled-data systems in which there are two sources of uncertainty: the values of the process parameters can be unknown while satisfying a polytopic condition and the sampling intervals can be uncertain and variable. We model such systems as linear impulsive systems and we apply our theorem to the analysis and state-feedback stabilization. We find a positive constant which determines an upper bound on the sampling intervals for which the stability of the closed loop is guaranteed. The control design LMIs also provide controller gains that can be used to stabilize the process. We also consider sampled-data systems with constant sampling intervals and provide results that are less conservative than the ones obtained for variable sampling intervals.     

Robust sampled-data control of a class of semilinear parabolic systems

We develop sampled-data controllers for parabolic systems governed by uncertain semilinear diffusion equations with distributed control on a finite interval. Such systems are stabilizable by linear infinite-dimensional state-feedback controllers. For a realistic design, finite-dimensional realizations can be applied leading to local stability results. Here we suggest a sampled-data controller design, where the sampled-data (in time) measurements of the state are taken in a finite number of fixed sampling points in the spatial domain. It is assumed that the sampling intervals in time and in space are bounded. Our sampled-data static output feedback enters the equation through a finite number of shape functions (which are localized in the space) multiplied by the corresponding state measurements. It is piecewise-constant in time and it may possess an additional time-delay. The suggested controller can be implemented by a finite number of stationary sensors (providing discrete state measurements) and actuators and by zero-order hold devices. A direct Lyapunov method for the stability analysis of the resulting closed-loop system is developed, which is based on the application of Wirtinger’s and Halanay’s inequalities. Sufficient conditions for the exponential stabilization are derived in terms of Linear Matrix Inequalities (LMIs). By solving these LMIs, upper bounds on the sampling intervals that preserve the exponential stability and on the resulting decay rate can be found. The dual problem of observer design under sampled-data measurements is formulated, where the same LMIs can be used to verify the exponential stability of the error dynamics.     

Robustly Exponential Stability Analysis for Discrete-Time Stochastic Neural Networks with Interval Time-Varying Delays

This paper deals with the problems of the global exponential stability and stabilization for a class of uncertain discrete-time stochastic neural networks with interval time-varying delay. By using the linear matrix inequality method and the free-weighting matrix technique, we construct a new Lyapunov–Krasovskii functional and establish new sufficient conditions to guarantee that the uncertain discrete-time stochastic neural networks with interval time-varying delay are globally exponential stable in the mean square. Furthermore, we extend our consideration to the stabilization problem for a class of discrete-time stochastic neural networks. Based on the state feedback control law, some novel delay-dependent criteria of the robust exponential stabilization for a class of discrete-time stochastic neural networks with interval time-varying delay are established. The controller gains are designed to ensure the global robust exponential stability of the closed-loop systems. Finally, numerical examples illustrate the effectiveness of the theoretical results we have obtained.     

Piecewise-affine Lyapunov functions for discrete-time linear systems with saturating controls

This paper is concerned with piecewise-affine (PWA) functions as Lyapunov function candidates for stability analysis of time-invariant discrete-time linear systems with saturating closed-loop control inputs. Using a PWA model of saturating closed-loop system, new necessary and sufficient conditions for a PWA function be a Lyapunov function are presented. Based on linear programming formulation of these conditions, an effective algorithm is proposed for construction of such Lyapunov functions for estimation of the region of local asymptotic stability. Compared to piecewise-linear functions, like Minkowski functions, PWA functions are more adequate to capture the dynamical effects of saturation nonlinearities, giving strictly less conservative results. The complexity of the proposed approach is polynomial in state dimension and exponential in saturating control dimension, being hence appropriate for problems with large state dimension but with few saturating inputs.     

Robust reliable sampled-data control for switched systems with application to flight control

This paper addresses the robust reliable stabilisation problem for a class of uncertain switched systems with random delays and norm bounded uncertainties. The main aim of this paper is to obtain the reliable robust sampled-data control design which involves random time delay with an appropriate gain control matrix for achieving the robust exponential stabilisation for uncertain switched system against actuator failures. In particular, the involved delays are assumed to be randomly time-varying which obeys certain mutually uncorrelated Bernoulli distributed white noise sequences. By constructing an appropriate Lyapunov–Krasovskii functional (LKF) and employing an average-dwell time approach, a new set of criteria is derived for ensuring the robust exponential stability of the closed-loop switched system. More precisely, the Schur complement and Jensen's integral inequality are used in derivation of stabilisation criteria. By considering the relationship among the random time-varying delay and its lower and upper bounds, a new set of sufficient condition is established for the existence of reliable robust sampled-data control in terms of solution to linear matrix inequalities (LMIs). Finally, an illustrative example based on the F-18 aircraft model is provided to show the effectiveness of the proposed design procedures.     

Exponential sampled-data control for T–S fuzzy systems: application to Chua's circuit

ABSTRACT

This study deals with the chaotic phenomenon of nonlinear Chua's circuit for power generator systems. Takagi–Sugeno (T–S) fuzzy model of a nonlinear system is established. By constructing a suitable Lyapunov functional, exponential stability conditions are obtained for fuzzy systems. Based on the sampled-data control theory, extreme sensitivity is visualised in the state trajectory depending on the initial conditions and sampled-data fuzzy controllers are designed in the form of linear matrix inequality (LMI). Finally, some numerical simulation results are shown that the sampled-data fuzzy control system adopts a well-designed methodology.     

Stability and stabilization of linear sampled-data systems with multi-rate samplers and time driven zero order holds

In multi-rate sampled-data systems, a continuous-time plant is controlled by a discrete-time controller which is located in the feedback loop between sensors with different sampling rates and actuators with different refresh rates. The main contribution of this paper is to propose sufficient Krasovskii-based stability and stabilization criteria for linear sampled-data systems, with multi-rate samplers and time driven zero order holds. For stability analysis, it is assumed that an exponentially stabilizing controller is already designed in continuous-time and is implemented as a discrete-time controller. For each sensor (or actuator), the problem of finding an upper bound on the lowest sampling frequency (or refresh rate) that guarantees exponential stability is cast as an optimization problem in terms of linear matrix inequalities (LMIs). Furthermore, sufficient conditions for controller synthesis are formulated as LMIs. It is shown through examples that choosing the right sensors (or actuators) with adequate sampling frequencies (or refresh rates) has a considerable impact on stability of the closed-loop system.     

Growth conditions for exponential stability of time-varying perturbed systems

In this paper, we derive some sufficient conditions for practical uniform exponential stability of time-varying perturbed systems based on Lyapunov techniques, whose dynamics are in general unbounded in time, in the sense that the solutions are uniform stable and converge to a small neighbourhood of the origin. Under quite general assumptions, we first present a new converse stability theorem for a large class of time-varying systems, which will be used to prove certain stability properties of nonlinear systems with perturbation. Therefore, a new Lyapunov function is presented that guarantees practical uniform exponential stability of perturbed systems. Furthermore, some illustrative examples are presented.