拒绝服务攻击下的分布式弹性负荷频率控制

开放通信网络的大量应用,给电网带来了潜在安全隐患.文章研究了周期性拒绝服务攻击下的弹性负荷频率控制器的设计问题.给出了一种可检测的拒绝服务攻击模型,推导了网络攻击下的分布式负荷频率控制时滞切换系统模型.基于李亚普洛夫理论,分析了切换系统的稳定性,并进行了弹性控制器设计,所设计控制器对负荷扰动和网络攻击具备一定的鲁棒性.最后,在Matlab/Simulink平台搭建了两区域电力系统模型,验证了所提方法的有效性.     

Robust Frequency Control in an Autonomous Microgrid: A Two-Stage Adaptive Fuzzy Approach

Highly intermittent power from renewable energy sources (RES) along with load and system perturbations in an autonomous microgrid (MG), results in large frequency fluctuations. Conventional controllers like PI controllers to be unable to provide acceptable performance over a wide range of operating conditions. To overcome this problem, present paper introduces a novel two-stage adaptive fuzzy logic based PI controller for frequency control of MG. In this proposed controller, particle swarm optimization (PSO) and grey wolf optimization (GWO) are used to optimize the membership functions (MFs) and rule base of fuzzy logic based PI controller. The proposed controller is examined on an MG test system, the robustness and performance of the proposed controller is tested in presence of different disturbance scenarios and parametric uncertainties. Finally, the superiority of the proposed controller is shown by comparing the results with various controllers available in literature like PSO tuned fuzzy logic based PI controller, fuzzy logic-based PI controller and also with the conventional PI controller.     

区间二型模糊PI/PD控制器设计与结构分析

针对模糊控制器内部工作机理未知的问题,提出一种区间二型(IT2)模糊PI/PD控制器,并推导了该IT2模糊控制器的解析结构。该模糊控制器采用Mamdani型规则形式,为每个输入变量仅分配两个IT2模糊集合,具有规则数量少、规则不确定性影响小、控制输出平滑等优点。根据IT2模糊控制器的降型特点,将整个输入空间划分为15个分区,进而推导出在每个分区上控制输出的数学表达式,证明出该IT2模糊控制器等效为具有变增益的非线性比例积分(PI)或比例微分(PD)控制器。最后,在仿真和实验中与相对应的一型模糊控制器和传统PI控制器的控制效果进行对比,验证了该IT2模糊PI/PD控制器的有效性。     

Retracted: State estimation of Takagi-Sugeno fuzzy system in networked control system with stochastic time delays under unknown attacks

This article concerns the event-triggered fuzzy filter design for Takagi-Sugeno (T-S) fuzzy systems subject to deception attacks under the stochastic multiple time-varying delays. A sequence of random variables, which are mutually independent but obey the Bernoulli distribution, is introduced to account for the randomly occurring communication delays. In order to efficiently utilize limited network communication bandwidth resources, the event-triggering scheme is adopted. A fuzzy filter with the attacked input signal is presented. Moreover, due to communication delays caused by event-triggering schemes and transmission, the filter adopts non-synchronous premise variables with the system. Then, by utilizing a model transformation technique, the fuzzy systems are developed. Furthermore, using the piecewise Lyapunov functional method technique, the resulting criterion provides sufficient conditions to ensure that fuzzy systems under deception attacks are stochastically stable with an H_∞ performance. Accordingly, the conditions for the co-design of the fuzzy filter and event-triggering schemes are given. Finally, numerical simulation with the industrial process provided to verify the proposed event-triggered design.     

A self-tuning fuzzy PI controller for TCSC to improve power system stability

In this paper, a self-tuning fuzzy PI controller (STFPIC) is proposed for thyristor-controlled series capacitor (TCSC) to improve power system dynamic performance. In a STFPIC controller, the output-scaling factor is adjusted on-line by an updating factor (α). The value of α is determined from a fuzzy rule-base defined on error (e) and change of error (Δe) of the controlled variable. The proposed self-tuning controller is designed using a very simple control rule-base and the most natural and unbiased membership functions (MFs) (symmetric triangles with equal base and 50% overlap with neighboring MFs). The comparative performances of the proposed STFPIC and the standard fuzzy PI controller (FPIC) have been investigated on two multi-machine power systems (namely, 4 machine, 2 area system and 10 machine 39 bus system) through detailed non-linear simulation studies using MATLAB/SIMULINK. From the simulation studies it has been found out that for damping oscillations, the performance of the proposed STFPIC is better than that obtained by the standard FPIC. Moreover, the proposed STFPIC as well as the FPIC have been found to be quite effective in damping oscillations over a wide range of operating conditions and are quite effective in enhancing the power carrying capability of the power system significantly.     

离散区间2-D时滞系统的二次稳定与镇定

针对实际系统中广泛存在的时滞和不确定现象，依据Lyapunov理论，采用线性矩阵不等式技术，研究了基于第二类Fomasini-Marchesini模型的离散区间2-D时滞系统的二次稳定和镇定，给出了系统二次稳定和镇定的充分条件。结论以线性矩阵不等式的形式描述，通过求解一个相应的线性矩阵不等式，可判断系统是否二次稳定和二次镇定，并得到系统二次镇定的状态反馈控制律。数值算例表明，该方法用于设计离散区间2-D时滞系统的二次镇定控制律是有效的。     

A self-organizing knn fuzzy controller and its neural network structure

This paper introduces a fuzzy controller that can be designed without specific information on the membership functions and the fuzzy rules. We show how the membership values of crisp inputs can be determined by K-nearest-neighbour (KNN) distance measures applied to the centres of the input clusters. Based on this new type of membership values, we introduce a KNN defuzzification method that allows the direct estimation of the crisp output of the given input data. the proposed computational model requires a clustering (self-organizing) process. We employ a simple clustering method that can adaptively allocate new clusters as more data become available to the controller. We prove that the resulting controller can uniformly approximate any real and continuous function to any desirable accuracy on a compact set. For hardware implementations we develop a neural network structure of the proposed fuzzy controller and compare it with other types of neural networks. It is shown that the three-layer sigmoid neural network and the Gaussian radial basis function (GRBF) network are special cases of this structure. A learning algorithm for the new structure is provided. the performance of the proposed controller is considered through three application studies: a controller design for truck backer-upper control, the prediction of the S&P 500 index, and the prediction of the Mackey-Glass time series.     

基于遗传算法的窄间距隶属函数在模糊逻辑电力系统稳定器设计中的应用

提出了一种简单、高效的模糊逻辑电力系统稳定器的设计方案,其特点是采用了窄间距隶属函数(SSMFs).SSMFs是设计模糊逻辑控制器隶属函数的一种新方法.采用遗传算法选择SSMFs模糊逻辑控制器的最佳参数值,包括间距系数、重叠系数和增益系数等.通过单机无穷大系统验证所提方案的正确性.结果表明,所提出的SSMFs模糊逻辑电力系统稳定器在发电机负载和励磁增益大范围变化情况下,改善了电力系统的稳定性.     

一类切换T-S型模糊系统二次镇定保成本控制

针对一类不确定切换T-S型模糊系统,对保成本鲁棒控制问题进行了研究。利用单Lyapunov函数法,给出了混杂状态反馈保成本控制器的设计方案,使得闭环系统对所有允许的不确定性,在所设计的混杂状态反馈控制器下是二次稳定的,应用线性矩阵不等式的可解性给出闭环系统二次稳定的充分条件,同时给出了二次型成本函数的一个上界。模型中的每个切换系统的子系统是不确定模糊系统,取常用的平行分布补偿PDC控制器,主要条件以凸组合的形式给出,具有较强的可解性。仿真结果验证了所设计方法的有效性。     

直线同步电动机磁悬浮系统区间二型模糊控制的研究

针对可控励磁直线同步电动机（CELSM）磁悬浮系统,提出一种区间二型模糊控制策略改善系统性能。根据CELSM磁悬浮系统的结构特点及控制原理,建立其数学模型。设计区间二型模糊控制器实现对CELSM磁悬浮系统的控制,采用磁悬浮平台气隙高度的误差及其变化率作为控制器输入,用三角形隶属度函数,建立9条模糊控制规则进行模糊推理,最后用Nie-Tan（NT）降阶算法得到系统输出。根据控制系统结构,在MATLAB软件中搭建仿真模型,经仿真验证,采用区间二型模糊控制的CELSM磁悬浮系统具有起动性能好、抗干扰能力强的优点。     

Adaptive control design for MIMO switched nonlinear systems with full state constraints

This paper presents an adaptive fuzzy control approach of multiple‐input–multiple‐output (MIMO) switched uncertain systems, which involve time‐varying full state constraints (TFSCs) and unknown disturbances. In the design procedure, the fuzzy logic systems are adopted to approximate the unknown functions in the systems. The adaptive fuzzy controller is set up by backstepping technique. According to the tangent barrier Lyapunov function (BLF‐Tan), a novel adaptive MIMO switched nonlinear control algorithm is designed. Under the rule of arbitrary switchings and the proposed control laws, it is demonstrated that all signals in the resulted system are semiglobally uniformly ultimately bounded (SGUUB) and the tracking error converges to a small neighborhood of zero with TFSCs. Furthermore, the simulation example validates the effectiveness of presented control strategy.     

基于事件触发控制的时滞电力系统负荷频率控制

区域电网间存在较高的数据传输,使有限的通信和计算资源变得拥塞。为降低区域电网间的通信负担,提出基于事件触发控制的时滞电力系统负荷频率控制(LFC)方法。针对具有通信延迟的LFC系统,建立基于事件触发控制的时滞LFC动态模型。进而采用多求和不等式,提出具有事件触发控制器的时滞相关LFC系统Lyapunov稳定分析判据;在此基础上,推导了基于事件触发通信和输出反馈的负荷频率控制器协同设计方案,以保证电力系统频率稳定性的同时提高数据传输效率。仿真结果表明,所提方法能够有效减小互联系统频率和联络线功率振荡,保证系统的时滞稳定性并减少网络通信的冗余传输。     

Robust decentralized load frequency control of interconnected power system with Generation Rate Constraint using Type-2 fuzzy approach

The Load Frequency Control (LFC) problem has been a major subject in electrical power system design/operation. LFC is becoming more significant recently with increasing size, changing structure and complexity in interconnected power systems. In practice LFC systems use simple Proportional Integral (PI) controllers. As the PI control parameters are usually tuned, based on classical approaches. Moreover, they have fixed gains; hence are incapable of obtaining good dynamic performance for a wide range of operating conditions and various load changes, in multi-area power system. Literature shows that fuzzy logic controller, one of the most useful approaches, for utilizing expert knowledge, is adaptive in nature and is applied successfully for power system stabilization control. This paper proposes a Type-2 (T2) fuzzy approach for load frequency control of two-area interconnected reheat thermal power system with the consideration of Generation Rate Constraint (GRC). The performance of the Type-2 (T2) controller is compared with conventional controller and Type-1 (T1) fuzzy controller with regard to Generation Rate Constraint (GRC). The system parametric uncertainties are verified by changing parameters by 40% simultaneously from their typical values.     

基于改进学习算法的模糊神经网络控制系统

针对一类复杂非线性系统，提出一种模糊神经网络(FNN)控制方案。系统中采用模糊神经网络控制器和神经网络辨识控制器相结合的结构，介绍一种改进的学习算法，对学习公式进行推导，利用改进的遗传算法来优化已经获得的隶属度函数，并结合误差补偿以提高控制精度。同时将混沌机制引入常规BP算法，利用混沌机制固有的全局游动，逃出权值优化过程中存在的局部极小点，解决了网络训练易陷入局部极小点的问题。用该方法对某非线性动态系统进行辨识和控制，仿真结果表明控制精度和实时性优于常规模糊控制器。     

基于自整定模糊PID控制器的SRM调速系统

以数字信号处理器为主控芯片,建立了开关磁阻电动机调速(SRD)控制系统,设计了自整定模糊PID控制器,实现了开关磁阻电动机转速的实时控制.实验证明,自整定模糊PID控制器兼有模糊控制器和PID控制器的优点,能有效减小系统超调,缩短系统调节时间,提高系统稳态精度,它是一种有效的控制算法,具有较高的应用价值.     

基于神经网络的模糊控制在开关磁阻电机中的应用研究

把神经网络与模糊逻辑结合起来,利用神经网络的学习控制算法调节模糊逻辑隶属函数,通过对开 关磁阻电机运行特性的分析,提出了一种可应用于开关磁阻电机驱动系统的智能控制方法,理论和仿真结果均证明了这种基于神经网络模糊控制方法在开关磁阻电机驱动系统中应用的可行性和可靠性。     

Adaptive TS‐FNN control for a class of uncertain multi‐time‐delay systems: The exponentially stable sliding mode‐based approach

This paper presents an adaptive Takagi–Sugeno fuzzy neural network (TS‐FNN) control for a class of multiple time‐delay uncertain nonlinear systems. First, we develop a sliding surface guaranteed to achieve exponential stability while considering mismatched uncertainty and unknown delays. This exponential stability result based on a novel Lyapunov–Krasovskii method is an improvement when compared with traditional schemes where only asymptotic stability is achieved. The stability analysis is transformed into a linear matrix inequalities problem independent of time delays. Then, a sliding mode control‐based TS‐FNN control scheme is proposed to achieve asymptotic stability for the controlled system. Since the TS‐FNN combines TS fuzzy rules and a neural network structure, fewer numbers of fuzzy rules and tuning parameters are used compared with the traditional pure TS fuzzy approach. Moreover, all the fuzzy membership functions are tuned on‐line even in the presence of input uncertainty. Finally, simulation results show the control performance of the proposed scheme. Copyright © 2008 John Wiley & Sons, Ltd.     

可控串补自适应模糊阻尼控制策略研究

早期的模糊控制器设计,主要依赖于专家经验,而且只是采用近似推理的方法。针对可控串补非线性系统,设计了一种直接自适应模糊控制器。建立模糊控制器逼近误差和控制器参数之间的线性关系,用Lyapunov稳定性理论设计参数的自适应律,不仅调节模糊规则结论参数,同时调节隶属函数的参数。将该控制策略应用于含可控串补电力系统中,提出可控串补自适应模糊阻尼控制器的设计方法。仿真表明该控制器对系统发电机摇摆具有良好的阻尼作用,提高了系统的稳定性。     

工业用电加热炉的智能温度控制系统设计

工业用电加热炉作为一类大惯性、大时滞和参数时变的强非线性系统,其温度控制问题一直工业过程控制中的难题。提出一种新型的基于TSK模糊理论的模糊神经网络PID控制器,采用实数编码混沌量子遗传算法优化模糊神经网络的隶属函数参数和模糊TSK增益,具有较快的收敛速度和更强的优化能力。分析加热炉温度控制系统的原理和结构,阐述基于TSK模糊理论的模糊神经网络PID控制器的设计过程以及实数编码量子遗传算法的实现流程。通过工业用电加热炉的温度控制仿真和试验,验证了所提出的算法具有更好的动态性能、更高的稳态精度和更强的抗干扰能力。