基于量子衍生涡流算法和T⁃S模糊推理模型的储层岩性识别

鉴于梯度下降法易陷入局部极值、普通群智能优化算法易早熟收敛,提出一种基于量子衍生涡流算法(Quantum Vortex Search Algorithm,QVSA)和T⁃S模糊推理模型的岩性识别方法,QVSA具有操作简单、收敛速度快、寻优能力强等优点,有助于T⁃S模糊推理模型获得最优参数配置,从而实现储层岩性的准确识别。首先利用具有全局搜索能力的QVSA优化T⁃S模糊推理模型的各种参数;然后利用主成分分析方法降低获取的地震属性维度;再利用优化的T⁃S模糊推理模型识别储层岩性。实验结果表明,利用反映储层特征的8个地震属性识别储层岩性时,所提方法的识别正确率达到92%,比普通BP网络方法高5.1%,同时查准率、查全率、F1分数等指标也较BP网络方法提升明显。

基于T-S模糊模型的桥吊系统积分状态反馈控制

小车定位精度和负载摆角大小是桥式起重机控制系统的两个重要性能指标。采用状态反馈控制时小车位移会存在稳态误差,为了消除稳态误差,提高系统的定位精度,给出基于T-S模糊模型的积分状态反馈控制方法。在桥式起重机非线性动力学模型基础上,建立增维T-S模糊模型,基于所建模型设计并行结构的积分状态反馈控制器,通过线性矩阵不等式计算反馈增益矩阵,并对闭环系统的稳定性进行分析。实验结果表明积分状态反馈控制不仅可以提高系统的定位精度,减少定位时间,而且有较好的抗干扰能力。

通信受限下T-S模糊网络控制系统L_(1)动态输出反馈控制

针对通信受限的非线性网络控制系统,为兼顾系统性能和节约利用网络资源,引入事件触发通信机制(ETCM),利用时延建模方法和并行分布补偿(PDC)技术,将连续控制系统建模为一个采样数据误差依赖的非线性网络化系统模型;构建保守性低的时滞依赖和模糊基依赖的Lyapunov-Krasovskii泛函,给出增广系统稳定性和鲁棒性结果,得到鲁棒控制器存在的充分条件,提出一种基于线性矩阵不等式(LMIs)的事件触发参数,以及全局模糊L 1动态输出反馈控制器参数的协同设计方法。采用永磁同步电动机模型仿真验证,结果表明该设计方法可减少网络资源占用,达到闭环控制系统的性能要求。

不确定非方广义分数阶T-S模糊系统的鲁棒镇定

针对不确定非方广义分数阶T-S模糊系统阶数为0<α<1的鲁棒镇定问题,提出了一个有效的判据.首先,利用一个新的T-S模糊动态补偿器,将不确定非方广义分数阶T-S模糊系统转化为一个增广的不确定方形广义分数阶T-S模糊系统.由于增广变量的引入,动态补偿器的设计问题可以等价为静态输出反馈控制器的设计问题.其次,设计一个分数阶导数反馈控制器对得到的增广系统进行正常化处理.然后,对正常化得到的不确定分数阶T-S模糊系统进行研究,得到一个系统渐近稳定的充分条件.该条件保守性小并且形式简洁.最后,通过一个数值算例和一个实际例子验证了本文所提出结论的正确性和有效性.

基于双边闭环函数的T-S模糊模型非线性系统网络跟踪控制

研究了一类基于T-S模糊模型的非线性系统网络跟踪控制问题。首先基于输入时滞法,建立了考虑网络诱导时滞和数据丢包的T-S模糊模型非线性系统跟踪误差模型;然后利用采样区间[t_(k),t_(k+1))信息,构建了一个新的双边闭环Lyapunov-Krasovskii(L-K)泛函,并使用新的L-K泛函和自由权矩阵积分不等式,得到了非线性网络系统H_(∞)跟踪控制的稳定性判据,以及控制器的设计方法。仿真结果表明,在相同网络条件下,所设计模糊控制器产生的跟踪误差相比已有文献结果明显更小;在相同的H_(∞)跟踪性能要求下,比已有文献具有更大的输入时滞上界,表明相较于现有方法,所提方法的保守性更低。

基于动态特性分析的涡扇发动机T-S模糊建模

面向具备强非线性特征的航空发动机这一复杂对象,考虑其在宽广包线内动态特性复杂难以用有限个线性模型描述,提出一种基于发动机动态特性分析建立全包线涡扇发动机数学模型的方法。基于全包线动态特性分析,设计动态特性表征参数λ。利用K均值聚类算法分析包线内发动机特性,依据聚类的中心点建立全包线T-S模糊状态空间模型。开展了模型精度仿真验证,仿真结果表明,基于航空发动机动态特性分析建立的全包线T-S模糊状态空间模型基本无稳态误差,且计算时间约为3ms。

T-S模糊网络化控制系统的事件触发控制

为解决遭受欺骗攻击的Takagi-Sugeno(简称T-S)模糊网络化控制系统的事件触发安全控制问题,针对T-S模糊网络化控制系统,采用改进型事件触发机制,在节省更多网络资源的同时使系统具有可靠的性能。采用不一致的方法解决了模糊系统的前提变量与模糊事件触发控制器的前提变量不一致的问题。采用伯努利过程建立了随机欺骗攻击模型。利用延迟系统方法,以线性矩阵不等式的形式给出了保证闭环系统达到随机渐进稳定同时满足给定的H∞性能的充分条件。利用实际案例验证了所提出的模糊控制方案的优越性与有效性。

Pendulation reduction on ship-mounted container crane via T-S fuzzy model

Ship-mounted container cranes are challenging industrial applications of nonlinear pendulum-like systems with oscillating disturbance which can cause them unstable.Since wave-induced ship motion causes the hoisted container to swing during the transfer operation,the swing motion may be dangerously large and the operation must be stopped.In order to reduce payload pendulation of ship-mounted crane,nonlinear dynamics of ship-mounted crane is derived and a control method using T-S fuzzy model is proposed.Simulation results are given to illustrate the validity of the proposed design method and pendulation of ship-mounted crane is reduced significantly.

Adaptive predictive functional control based on Takagi-Sugeno model and its application to pH process

In order to obtain accurate prediction model and compensate for the influence of model mismatch on the control performance of the system and avoid solving nonlinear programming problem,an adaptive fuzzy predictive functional control(AFPFC) scheme for multivariable nonlinear systems was proposed.Firstly,multivariable nonlinear systems were described based on Takagi-Sugeno(T-S) fuzzy models;assuming that the antecedent parameters of T-S models were kept,the consequent parameters were identified on-line by using the weighted recursive least square(WRLS) method.Secondly,the identified T-S models were linearized to be time-varying state space model at each sampling instant.Finally,by using linear predictive control technique the analysis solution of the optimal control law of AFPFC was established.The application results for pH neutralization process show that the absolute error between the identified T-S model output and the process output is smaller than 0.015;the tracking ability of the proposed AFPFC is superior to that of non-AFPFC(NAFPFC) for pH process without disturbances,the overshoot of the effluent pH value of AFPFC with disturbances is decreased by 50% compared with that of NAFPFC;when the process parameters of AFPFC vary with time the integrated absolute error(IAE) performance index still retains to be less than 200 compared with that of NAFPFC.

Controller Design for Electric Power Steering System Using T-S Fuzzy Model Approach

Pressure ripples in electric power steering （EPS） systems can be caused by the phase lag between the driver s steering torque and steer angle, the nonlinear frictions, and the disturbances from road and sensor noise especially during high-frequency maneuvers. This paper investigates the use of the robust fuzzy control method for actively reducing pressure ripples for EPS systems. Remarkable progress on steering maneuverability is achieved. The EPS dynamics is described with an eight-order nonlinear state-space model and approximated by a Takagi-Sugeno （T-S） fuzzy model with time-varying delays and external disturbances. A stabilization approach is then presented for nonlinear time-delay systems through fuzzy state feedback controller in parallel distributed compensation （PDC） structure. The closed-loop stability conditions of EPS system with the fuzzy controller are parameterized in terms of the linear matrix inequality （LMI） problem. Simulations and experiments using the proposed robust fuzzy controller and traditional PID controller have been carried out for EPS systems. Both the simulation and experiment results show that the proposed fuzzy controller can reduce the torque ripples and allow us to have a good steering feeling and stable driving.

Constrained predictive control based on T-S fuzzy model for nonlinear systems

A constrained generalized predictive control （GPC） algorithm based on the T-S fuzzy model is presented for the nonlinear system. First, a Takagi-Sugeno （T-S） fuzzy model based on the fuzzy cluster algorithm and the orthogonalleast square method is constructed to approach the nonlinear system. Since its consequence is linear, it can divide the nonlinear system into a number of linear or nearly linear subsystems. For this T-S fuzzy model, a GPC algorithm with input constraints is presented. This strategy takes into account all the constraints of the control signal and its increment, and does not require the calculation of the Diophantine equations. So it needs only a small computer memory and the computational speed is high. The simulation results show a good performance for the nonlinear systems.

Predictive functional control based on fuzzy T-S model for HVAC systems temperature control

In heating, ventilating and air-conditioning （HVAC） systems, there exist severe nonlinearity, time-varying nature, disturbances and uncertainties. A new predictive functional control based on Takagi-Sugeno （T-S） fuzzy model was proposed to control HVAC systems. The T-S fuzzy model of stabilized controlled process was obtained using the least squares method, then on the basis of global linear predictive model from T-S fuzzy model, the process was controlled by the predictive functional controller. Especially the feedback regulation part was developed to compensate uncertainties of fuzzy predictive model. Finally simulation test results in HVAC systems control applications showed that the proposed fuzzy model predictive functional control improves tracking effect and robustness. Compared with the conventional PID controller, this control strategy has the advantages of less overshoot and shorter setting time, etc.

Statistic PID Tracking Control for Non-Gaussian Stochastic Systems Based on T-S Fuzzy Model

A new robust proportional-integral-derivative （PID） tracking control framework is considered for stochastic systems with non-Gaussian variable based on B-spline neural network approximation and T-S fuzzy model identification. The tracked object is the statistical information of a given target probability density function （PDF）, rather than a deterministic signal. Following B-spline approximation to the integrated performance function, the concerned problem is transferred into the tracking of given weights. Different from the previous related works, the time delay T-S fuzzy models with the exogenous disturbances are applied to identify the nonlinear weighting dynamics. Meanwhile, the generalized PID controller structure and the improved convex linear matrix inequalities （LMI） algorithms are proposed to fulfil the tracking problem. Furthermore, in order to enhance the robust performance, the peak-to-peak measure index is applied to optimize the tracking performance. Simulations are given to demonstrate the efficiency of the proposed approach.

Modeling and Stability Analysis for Non-linear Network Control System Based on T-S Fuzzy Model

Based on the T-S fuzzy model,this paper presents a new model of non-linear network control system with stochastic transfer delay.Sufficient criterion is proposed to guarantee globally asymptotically stability of this two-levels T-S fuzzy model.Also a T-S fuzzy observer of NCS is designed base on this two-levels T-S fuzzy model.All these results present a new approach for networked control system analysis and design.

Robust Stabilization of Nonlinear Time Delay Discrete-time Systems Based on T-S Model

A robust stabilization problem is considered for time delay nonlinear discrete-time systems based on T-S fuzzy model. A necessary and sufficient condition for the existence of such controllers is given through Lyapunov stability theorem. And it is further shown that this condition is equivalent to the solvability of a certain linear matrix inequality, which can be solved easily by using the LMI toolbox of Matlab. At last, an illustrative example of truck-trailer is presented to show the feasibility and effectiveness of the proposed method.

Controller design of uncertain nonlinear systems based on T-S fuzzy model

A robust control for uncertain nonlinear systems based on T-S fuzzy model is discussed in this paper. First, a T-S fuzzy system is adopted to model the uncertain nonlinear systems. Then, for the system with input variables adopting standard fuzzy partitions, the efficient maximal overlapped-rules group （EMORG） is presented, and a new sufficient condition to check the stability of T-S fuzzy system with uncertainty is derived, which is expressed in terms of Linear Matrix Inequalities. The derived stability condition, which only requires a local common positive definite matrix in each EMORG, can reduce the conservatism and difficulty in existing stability conditions. Finally, a simulation example shows the proposed approach is effective.

Guaranteed Cost Control for Uncertain Nonlinear Time-Delay Neutral Systems Based on T-S Fuzzy Model

The problem of guaranteed cost fuzzy controller is studied for a class of nonlinear time-delay neutral sys-tems with norm-bounded uncertainty based on T-S model. The sufficient conditions are first derived for the existenceof guaranteed cost fuzzy controllers. These sufficient conditions are equivalent to a kind of linear matrix inequalities.Furthermore, a convex optimization problem with LMI constraints is formulated to design the optimal guaranteedcost controller.

Flatness predictive model based on T-S cloud reasoning network implemented by DSP

The accuracy of present flatness predictive method is limited and it just belongs to software simulation. In order to improve it, a novel flatness predictive model via T-S cloud reasoning network implemented by digital signal processor(DSP) is proposed. First, the combination of genetic algorithm(GA) and simulated annealing algorithm(SAA) is put forward, called GA-SA algorithm, which can make full use of the global search ability of GA and local search ability of SA. Later, based on T-S cloud reasoning neural network, flatness predictive model is designed in DSP. And it is applied to 900 HC reversible cold rolling mill. Experimental results demonstrate that the flatness predictive model via T-S cloud reasoning network can run on the hardware DSP TMS320 F2812 with high accuracy and robustness by using GA-SA algorithm to optimize the model parameter.

Fault tolerant synchronization of chaotic systems based on T-S fuzzy model with fuzzy sampled-data controller

In this paper the fault tolerant synchronization of two chaotic systems based on fuzzy model and sample data is investigated. The problem of fault tolerant synchronization is formulated to study the global asymptotical stability of the error system with the fuzzy sampled-data controller which contains a state feedback controller and a fault compensator. The synchronization can be achieved no matter whether the fault occurs or not. To investigate the stability of the error system and facilitate the design of the fuzzy sampled-data controller, a Takagi Sugeno （T-S） fuzzy model is employed to represent the chaotic system dynamics. To acquire good performance and produce a less conservative analysis result, a new parameter-dependent Lyapunov-Krasovksii functional and a relaxed stabilization technique are considered. The stability conditions based on linear matrix inequality are obtained to achieve the fault tolerant synchronization of the chaotic systems. Finally, a numerical simulation is shown to verify the results.

Effects of natural-cerebrolysin-containing serum on neurotoxicity and synaptogenesis in amyloid-beta 1-40-induced Alzheimer's disease in vitro models

BACKGROUND： Neuronal loss, synapse mutilation, and increasing malnourished axons are pathologically related to Alzheimer＇s disease. Microtubule-associated protein 2 （MAP2） is of importance for neuronal, axonal, and dendritic generation, extension, and stabilization, as well as for the regulation of synaptic plasticity. OBJECTIVE： To investigate the antagonistic effects of natural-cerebrolysin-containing serum on beta amyloid protein 1-40 （Aβ1-40）-induced neurotoxicity from the standpoints of cell proliferation, synaptogenesis, and cytoskeleton formation （MAP2 expression）. DESIGN, TIME AND SETTING： A paralleled, controlled, neural cell, and molecular biology experiment was performed at the Institute of Integrated Chinese and Western Medicine, Shenzhen Hospital, Southern Medical University between February 2006 and April 2008. MATERIALS： PC12 cells, derived from the rat central nervous system, were purchased from Shanghai Institute of Cell Biology, Chinese Academy of Sciences, China. A β1-40 was provided by Sigma, USA. Natural-cerebrolysin was provided by Shenzhen Institute of Integrated Chinese and Western Medicine, China. The natural-cerebrolysin was predominantly composed of Renshen （Radix Ginseng）, Tianma （Rhizoma Gastrodiae）, and Yixingye （Ginkgo Leaf） in a proportion of 1：2：2. Following conventional water extraction technology, an extract （1：20） was prepared. Each gram of extract equaled 20 grams of crude drug. In a total of 12 adult male New Zealand rabbits, six underwent intragastric administration of natural-cerebrolysin extract for 1 month to prepare natural-cerebrolysin-containing serum, and the remaining six rabbits received intragastric administration of physiological saline to prepare normal blank serum. METHODS： An AIzheimer＇s disease in vitro model was induced in PC12 cells using Aβ1-40. The cells were incubated with varying doses of natural-cerebrolysin-containing serum （2.5%, 5%, and 10%）. Normal blank serum-treated PC12 cells served as a blank control group. MAIN OUTCOME MEASURES： Through the use of inverted phase contrast microscope, cell morphology and neurite growth were observed, neurite length was measured, and the percentage of neurite-positive cells was calculated. Cell proliferation rate was determined by MTT assay, and MAP 2 expression was detected by fluorescent immunocytochemistry. RESULTS： Following Aβ1-40 treatments, some PC12 cells were apoptotic/dying, and only a few short neurites were observed. Following interventions with natural-cerebrolysin-containing serum, the PC12 cells proliferated, there was an increased number of neurites, and neurite length was enhanced. After middle- and high-dose natural-cerebrolysin treatments, the percentage of neurite-positive cells, as well as the average length of neurites, was significantly greater than the normal blank serum-treated PC12 cells （P 〈 0.05 or P 〈 0.01）. Compared with the blank control group, MAP2 expression in the Aβ1-40-treated PC12 cells was significantly inhibited, and the cell proliferation rate was significantly decreased （P 〈 0.01）. Following incubations with natural-cerebrolysin-containing serum, MAP2 expression and cell proliferation rate in the PC12 cells were significantly increased in a dose-dependent manner, compared with treatments with blank control serum （P 〈 0.05 or P 〈 0.01 ）. CONCLUSION： Natural-cerebrolysin exhibited antagonistic effects on neurotoxicity in Aβ1-40 induced Alzheimer＇s disease in vitro models. These effects were likely related to cell proliferation and the upregulation of intracellular MAP2 expression.