状态反馈脉冲控制相互干扰Leslie系统的定性分析

建构一类具有相互干扰和脉冲状态反馈控制的Leslie捕食与被捕食系统,并对系统进行定性分析.首先利用微分方程的稳定性理论获得无脉冲系统正平衡点的全局渐近稳定性;其次,对具有脉冲状态反馈控制系统,利用半连续动力系统的几何理论和后继函数的方法,获得系统阶1周期解的存在性、唯一性和轨道渐近稳定性,并利用数值模拟验证了主要结论.最后给出主要结论.     

基于T-S模型的Lurie混沌系统的模糊控制

针对Lurie混沌控制系统,进行了T-S模糊建模和模糊控制器设计,从而实现了Lurie混沌系统的稳定.在用T-S模糊模型精确重构Lurie系统结构的基础上,利用反馈同步思想,基于并行分布补偿(PDC)技术,得到了简单且易实现的控制器.仿真结果验证了该控制方法的有效性.     

一类带有连续投放和脉冲控制的害虫管理SI数学模型

研究一类具有连续投放和脉冲控制的害虫管理SI数学模型,证明了连续投放系统正平衡点的全局渐近稳定性,讨论了脉冲控制系统的持续性,并对所得结论进行了数值模拟.     

基于观测器的一类模糊时滞系统的容错控制

针对一类带有传感器故障的模糊时滞系统,提出了一种实现对系统的状态和传感器故障估计的观测嚣设计方法.在此基础上,给出了模糊容错控制方法及保证模糊控制系统的渐近稳定充分条件.应用广义Lyapunov函数和线性矩阵不等式方法,证明了模糊闭环时滞系统的渐近稳定性.仿真结果进一步验证了所提出的方法和条件的有效性.     

非线性网络控制系统的保性能控制

针对网络诱导时延小于一个采样周期的非线性网络控制系统,研究了系统的稳定性和保性能控制问题.对于T-S模糊模型描述的非线性被控对象,将时延的不确定性转化为系统参数的不确定性,从而将这一类非线性网络控制系统建模为具有参数不确定性的离散T-S模糊模型.基于建立的模型,提出了存在稳定保性能控制器的充分条件,并得出了相应的线性矩阵不等式(LMI)形式.最后通过对永磁同步电动机混沌系统进行控制和仿真研究,验证了所提出方法的有效性.     

脉冲控制系统的渐近稳定性分析

由于脉冲控制系统响应速度快,有较强的鲁棒性和抗干扰能力等特点,因而被广泛研究.但当前对脉冲量为线性函数时的系统稳定性研究较多,而对脉冲量为其它形式时的系统稳定性研究较少.本文对脉冲控制系统的脉冲量是非线性向量值函数,线性与非线性和的向量值函数以及可变的线性向量值函数这三种情形的系统稳定性进行研究,给出了它们稳定性的充分条件.最后,给出数值示例说明所得结果.     

具有脉冲和无限时滞的模糊细胞神经网络的全局指数稳定性

本文讨论了具有脉冲和无限时滞的模糊细胞神经网络的全局指数稳定性.通过建立一个脉冲时滞%积分微分不等式,以及模糊逻辑算子与M-矩阵的性质,不仅得到了系统全局指数稳定的充分条件,而且也给出了指数收敛速度.最后,所给的例子充分验证了文中所给出的充分条件的有效性.     

一类模糊互联系统的新时滞依赖稳定性条件

针对一类连续模糊定常时滞互联系统,提出了一种模糊分散控制器的控制方法,给出了新的时滞依赖稳定性条件.应用Lyapunov函数法,自由权重矩阵,及线性矩阵不等式(LMIs)方法证明了模糊分散控制系统稳定.仿真结果进一步验证了所提出模糊分散控制方法的有效性.     

一类不确定非线性系统的执行器故障模糊容错控制

针对一类状态不完全可测的不确定非线性系统,研究了带有执行器故障的容错控制问题.采用 T-S模型对非线性系统进行模糊建模,利用并行分布补偿(PDC)算法设计了状态现潮器和基于状态现 潮器的客错控制,给出了保证该模糊容错控制系统稳定的充分条件.根据李雅普诺夫稳定性理论和线性 矩阵不等式(LMI),证明了所提出的模糊容错控制方法不但使得模糊控制系统渐近稳定,而且能够取得 H∞性能指标.计算机仿真结果进一步验证了所提出方法的正确性.     

新的基于Type-2模糊系统的直接自适应模糊控制

在Type-1模糊系统的直接自适应控制基础上,将规则前件、后件改为Type-2模糊集合,建立Type-2模糊系统的直接自适应模糊控制器,给出了直接自适应控制器的设计方法,讨论了直接自适应控制系统的稳定性,研究了直接自适应控制系统的收敛性,针对一类非线性系统给出了仿真。     

Fuzzy Impulsive Control and Synchronization of General Chaotic System

In this paper, by utilizing impulsive control theory and T-S fuzzy model, the fuzzy impulsive control and synchronization of general chaotic system are proposed. Some less conservative and more general conditions are obtained to guarantee the globally asymptotical stability for the impulsive control and synchronization of general chaotic system based on T-S fuzzy model. Moreover, some criteria of globally exponential stability of chaotic system are also derived. Finally, some numerical simulations are given to demonstrate the effectiveness of the proposed control method.     

Indirect adaptive fuzzy observer and controller design based on interval type-2 T-S fuzzy model

This paper presents the design scheme of the indirect adaptive fuzzy observer and controller based on the interval type-2 (IT2) T-S fuzzy model. The nonlinear systems can be well approximated by IT2 T-S fuzzy model, in which the fuzzy rules’ antecedents are interval type-2 fuzzy sets and consequents are linear state equations. The proposed IT2 T-S fuzzy model is a combination of IT2 fuzzy system and T-S fuzzy model, and also inherits the benefits of type-2 fuzzy logic systems, which is able to directly handle uncertainties and can minimize the effects of uncertainties in rule-based fuzzy system. These characteristics can improve the accuracy of the system modeling and reduce the number of system rules. The proposed method using feedback control, adaptive laws, and on-line object parameters are adjusted to ensure observation error bounded. In addition, using Lyapunov synthesis approach and Lipschitz condition, the stability analysis is conducted. The simulation results show that the proposed method can handle unpredicted disturbance and data uncertainties very well in advantage of the effectiveness of observation and control.     

基于模糊双线性模型的非线性系统的跟踪控制

研究一种基于T-S模糊双线性系统的跟踪控制器设计及稳定性分析.使用分布并行补偿法(PDC)设计了模糊控制器,得到模糊双线性系统跟踪控制渐近稳定的充分条件,仿真结果验证了该方法改进了闭环系统的性能.     

Fuzzy impulsive control of chaotic systems based on TS fuzzy model

Combining Takagi–Sugeno (TS) fuzzy model and impulsive control, a new approach to control chaotic systems, namely fuzzy impulsive control, is proposed in this paper. The rigorous stability analysis of the proposed method is given. The effectiveness of the approach is tested on Chua’s circuit, Chen’s system and Rössler’s system.     

Type-2 T–S fuzzy impulsive control of nonlinear systems

A novel impulsive control approach based on interval Type-2 T–S fuzzy model has been presented for nonlinear systems in this paper. This approach makes up for the drawback of Type-1 fuzzy impulsive control, which cannot fully handle the uncertainties in describing the complex nonlinear systems by Type-1 fuzzy membership functions and cannot give rigorous fuzzy rules. Further more, this approach uses the “broad band” effect of the Type-2 membership functions to solve the noise of training data and exterior disturbance of the Type-1 fuzzy impulsive control. By using Lyapunov theory and Lipschitz condition, which is combined with integrated approaches such as comparison methods and linear matrix inequalities, the Type-2 fuzzy impulsive controller is designed and the general asymptotical stability analysis of the systems is given. Finally, the simulation of the inverted pendulum model demonstrates the validity and superiority of the proposed method by easily determining the membership functions and choosing minimum number of fuzzy rules and the method can handle random disturbance and data uncertainties very well.     

Improved stability criteria for time-varying delayed T-S fuzzy systems via delay partitioning approach

This paper focuses on the stability analysis for uncertain Takagi-Sugeno (T-S) fuzzy systems with interval time-varying delay. The uncertainties of system parameter matrices are assumed to be time-varying and norm-bounded. Some new Lyapunov-Krasovskii functionals (LKFs) are constructed by nonuniformly dividing the whole delay interval into multiple segments and choosing different Lyapunov functionals to different segments in the LKFs. By employing these LKFs, some new delay-derivative-dependent stability criteria are established for the nominal and uncertain T-S fuzzy systems in a convex way. These stability criteria are derived that depend on both the upper and lower bounds of the time derivative of the delay. By employing the new delay partitioning approach, the obtained stability criteria are stated in terms of linear matrix inequality (LMI). They are equivalent or less conservative while involving less decision variables than the existing results. Finally, numerical examples are given to illustrate the effectiveness and reduced conservatism of the proposed results.     

Reliable control for nonlinear systems with stochastic actuators fault and random delays through a T-S fuzzy model approach

This paper considers the reliable control design for T-S fuzzy systems with probabilistic actuators faults and random time-varying delays. The faults of each actuator occurs randomly and its failure rates are governed by a set of unrelated random variables satisfying certain probabilistic distribution. In terms of the probabilistic failures of each actuator and time-varying random delays, new fault model is proposed. Based on the new fuzzy model, reliable controller is designed and sufficient conditions for the exponentially mean square stability (EMSS) of T-S fuzzy systems are derived by using Lyapunov functional method and linear matrix inequality (LMI) technique. It should be noted that the obtained criteria depend on not only the size of the delay, but also the probability distribution of it. Finally, a numerical example is given to show the effectiveness of the proposed method.     

模糊Delta算子系统的鲁棒镇定

研究一类基于Delta算子描述的T-S模糊模型状态反馈镇定设计问题。首先将全局模糊模型按隶属函数划分成若干子空间,并被表示成不确定系统的形式;采用分段Lyapunov函数法,得到鲁棒稳定化控制律存在的充分条件．该条件被进一步等价表示成一组线性矩阵不等式的可解性问题。克服了以往设计法中需要求解一公共正定矩阵P的不足,也无需求解繁琐的Riccati方程。所得结果可将连续和离散模糊系统的有关结论统一到Delta算子框架内。     

Online identification of Takagi–Sugeno fuzzy models based on self-adaptive hierarchical particle swarm optimization algorithm

This paper presents an approach for online learning of Takagi–Sugeno (T-S) fuzzy models. A novel learning algorithm based on a Hierarchical Particle Swarm Optimization (HPSO) is introduced to automatically extract all fuzzy logic system (FLS)’s parameters of a T–S fuzzy model. During online operation, both the consequent parameters of the T–S fuzzy model and the PSO inertia weight are continually updated when new data becomes available. By applying this concept to the learning algorithm, a new type T–S fuzzy modeling approach is constructed where the proposed HPSO algorithm includes an adaptive procedure and becomes a self-adaptive HPSO (S-AHPSO) algorithm usable in real-time processes. To improve the computational time of the proposed HPSO, particles positions are initialized by using an efficient unsupervised fuzzy clustering algorithm (UFCA). The UFCA combines the K-nearest neighbour and fuzzy C-means methods into a fuzzy modeling method for partitioning of the input–output data and identifying the antecedent parameters of the fuzzy system, enhancing the HPSO’s tuning. The approach is applied to identify the dynamical behavior of the dissolved oxygen concentration in an activated sludge reactor within a wastewater treatment plant. The results show that the proposed approach can identify nonlinear systems satisfactorily, and reveal superior performance of the proposed methods when compared with other state of the art methods. Moreover, the methodologies proposed in this paper can be involved in wider applications in a number of fields such as model predictive control, direct controller design, unsupervised clustering, motion detection, and robotics.