Stability analysis of the simplest Takagi-Sugeno fuzzy control system using circle criterion

A frequency-domain-based sufficient condition is derived to guarantee the globally asymptotic stability of the simplest Takagi-Sugeno (T-S) fuzzy control system by using the circle criterion. The analysis is performed in the frequency domain, and hence the condition is of great significance when the frequency-response method, which is widely used in the linear control theory and practice, is employed to synthesize the simplest T-S fuzzy controller. Besides, this sufficient condition is featured by a graphical interpretation, which makes the condition straightforward to be used. Comparisons are drawn between the performance of the simplest T-S fuzzy controller and that of the linear compensator. Two numerical examples are presented to demonstrate how this sufficient condition can be applied to both stable and unstable plants.     

汽车ASS与EPS分层协调控制

以提高汽车行驶平顺性和操纵稳定性为出发点,建立了汽车ASS与EPS的整车动力学模型,提出了一种汽车ASS与EPS集成控制的方法。分别设计了ASS子系统模糊控制器与EPS子系统模糊控制器,利用分层协调控制思想建立了上层协调控制器,对两个子系统进行协调控制。仿真结果表明：采用分层协调控制策略控制的ASS与EPS集成系统可使车身垂直加速度、车身俯仰角、横摆角速度、车身侧倾角等性能参数得到优化,汽车行驶平顺性和操纵稳定性有明显改善,提高了整车综合性能。

Abstract：

Vehicle dynamics model of ASS and EPS was built.A coordination control method of vehicle ASS and EPS was presented to improve vehicle ride performance and handling stability.ASS controller and EPS controller were designed separately.The controller of ASS was designed using fuzzy logic control theory and the EPS system was designed using fuzzy logic control theory too.In order to coordinate the two subsystems,an upper coordination controller was set up based on an idea of delaminating control.The simulation results show that layered coordination control strategy can optimize the vehicle performance parameters and the vehicle integrated performance is improved obviously.     

AN INTELLIGENT CONTROL SYSTEM BASED ONRECURRENT NEURAL FUZZY NETWORK AND ITS APPLICATION TO CSTR

In this paper, an intelligent control system based on recurrent neural fuzzy network is presented for complex, uncertain and nonlinear processes, in which a recurrent neural fuzzy network is used as controller (RNFNC) to control a process adaptively and a recurrent neural network based on recursive predictive error algorithm (RNNM) is utilized to estimate the gradient information ρy/ρu for optimizing the parameters of controller.Compared with many neural fuzzy control systems, it uses recurrent neural network to realize the fuzzy controller. Moreover, recursive predictive error algorithm (RPE) is im-plemented to construct RNNM on line. Lastly, in order to evaluate the performance of the proposed control system, the presented control system is applied to continuously stirred tank reactor (CSTR). Simulation comparisons, based on control effect and output error,with general fuzzy controller and feed-forward neural fuzzy network controller (FNFNC),are conducted. In addition, the rates of convergence of RNNM respectively using RPE algorithm and gradient learning algorithm are also compared. The results show that the proposed control system is better for controlling uncertain and nonlinear processes.     

A Novel Robust Adaptive Fuzzy Controller

For a class of continuous-time nonlinear system, a novel robust adaptive fuzzy controller is proposed by using of Lyapunov method. It is proven that the control algorithm is globally stable, the output tracking-error can convergence to a domain of zero under the assumptions. As a result, the system controlled has stronger robustness for disturbance and modeling error.     

ON THE DESIGN OF A NEURO-FUZZY CONTROLLER–APPLICATION TO THE CONTROL OF A BIOREACTOR

1. Introduction Fuzzy controllers have interesting characteristics, principally concerning their robustness and their ability to integrate human knowledge in the design of control systems, allowing to a human operator to interpret, at every time, the action of the control system. However, the synthesis of a fuzzy controller, giving the controlled process a specified behaviour, is not an evident thing. Indeed, tuning the parameters of a fuzzy controller is often achieved by a succession of tria…     

神经网络模糊预测优化控制在VAV系统中的应用

将神经网络、模糊控制与非线性预测优化控制结合起来,提出了神经网络模糊预测优化控制方法,采用前馈神经网络作为预测模型,利用贝叶斯正则化方法对模型进行了辨识,以自调整模糊控制器作为优化控制器,通过多步预测方式,系统的优化性能指标综合考虑温度偏差最小和能耗最小这两方面因素,应用该方法对制冷工况变风量空调系统的送风温度和回风温度（室内温度）进行了仿真控制研究。控制结果表明了该方法的有效性,控制效果良好,并且可以达到节省能耗的目的。

Abstract：

Artificial neural network,fuzzy control and nonlinear optimal predictive control were combined.The algorithm of neural network nonlinear fuzzy predictive optimal control was proposed.Feed-forward neural network was adopted as the predictive model of the cooling VAV system.The model was identified by the method of Bayesian regularization.The self-adjusting fuzzy controller was adopted as optimal controller.The algorithm was applied in the cooling VAV system with multi-step predictive method.Indoor temperature and supply air temperature was controlled aimed at minimum temperature deviation and minimum energy consumption by this scheme in Matlab.Simulation results illustrate the effectiveness of this technique,and in the meantime illustrate that this technique can save energy consumption.     

基于反馈线性化的船舶自动舵模糊滑模控制

针对船舶航向自动舵控制系统的非线性数学模型,应用基于微分几何的反馈线性化方法,将原非线性系统等价为完全可控型线性化模型,然后设计了滑模变结构控制器,并利用模糊控制器实现了滑模趋近律的参数自整定,设计方法简单可行。仿真结果表明,所设计的模糊滑模控制器能够快速准确地跟踪设定航向,并对参数摄动和外界风浪干扰具有很强的鲁棒性,同时消除了系统的抖振现象,优于传统的滑模控制。

Abstract：

According to the nonlinear model of ship autopilot system and using the feedback linearization procedure of differential geometry,an equivalent,fully controllable and linear model was derived via a homomorphism transformation and a fuzzy reaching law sliding mode controller was designed.The simulation results show that the controller designed here,which is superior to traditional sliding mode control,can track a desired course fast and accurately.It also exhibits strong robustness peculiarity to system uncertainties and avoiding chatting phenomenon.     

H_∞ Control for Nonlinear Stochastic Markov Systems with Time-Delay and Multiplicative Noise

This paper is concerned with the H_∞ control problem for a class of nonlinear stochastic Markov jump systems with time-delay and system state-, control input-and external disturbancedependent noise. Firstly, by solving a set of Hamilton-Jacobi inequalities(HJIs), the exponential mean square H_∞ controller design of delayed nonlinear stochastic Markov systems is presented. Secondly,by using fuzzy T-S model approach, the H_∞ controller can be designed via solving a set of linear matrix inequalities(LMIs) instead of HJIs. Finally, two numerical examples are provided to show the effectiveness of the proposed design methods.     

Fuzzy Static Output Feedback H_∞ Control for Nonlinear Systems Subject to Parameter Uncertainties

This study is concerned with the stabilization issue of nonlinear systems subject to parameter uncertainties. An interval type-2 T-S fuzzy model is used to represent the nonlinear systems subject to parameter uncertainties. An interval type-2 fuzzy static output feedback controller is designed to synthesize the interval type-2 T-S fuzzy systems. The membership-function-dependent stability conditions are derived by utilizing the information of upper and lower membership functions. The proposed stability conditions are presented in the form of linear matrix inequalities(LMIs). LMI-based stability conditions for interval type-2 fuzzy static output feedback H_∞ control synthesis are also developed.Several simulation examples are given to show the superiority of the proposed approach.     

Sampled-Data H∞ Dynamic Output Feedback Controller Design for Fuzzy Markovian Jump Systems

This paper considers the issue of H_∞ dynamic output feedback controller design for T-S fuzzy Markovian jump systems under time-varying sampling with known upper bound on the sampling intervals. The main aim is to realize sampled-data fuzzy dynamic output feedback control so as to demonstrate the stochastic stability and H_∞ performance index of the closed-loop sampled-data fuzzy Markovian jump systems. Then, by making the most of the information within the sampling interval,...     

基于模糊模型的无线网络控制系统故障检测

考虑控制器与被控对象之间采用无线传输,且被控对象为非线性模型的一类网络控制系统,对其进行故障检测。首先基于T-S模糊模型将对象线性化,利用模糊主导子系统规则,设计了模糊观测器,并得出了观测器误差方程。然后将误差方程等效为与无线传输跳数相关的离散切换系统,并证明了误差系统的稳定性。最后,通过仿真实例验证了所提方法的有效性。     

灰色预测模糊PID控制在汽温控制系统中的应用

将灰色预测、模糊控制与常规PID控制三者的设计思想融合起来，提出了一种灰色预测模糊PID控制算法。该算法首先通过建立模糊规则和进行模糊推理来确定PID控制器的参数，然后由PID控制律直接确定控制作用，再将灰色预测在线应用，用其预测结果代替被控对象测量值进行控制运算。仿真结果表明，利用该算法设计的汽温控制系统比应用模糊PID控制设计的汽温控制系统具有更加优良的性能。     

基于采样控制理论的飞行仿真转台控制研究

基于采样控制系统的直接设计理论，应用提升技术，提出了仿真转台伺服控制系统多速率H∞控制器的设计方法，解决了转台控制系统中由于被控对象的不确定性及应用离散控制器控制连续被控对象的近似等价影响系统动态性能提高的问题，最后文中给出了具体实例。     

自动变速车辆起步模糊神经网络控制策略仿真

自动变速器车辆的核心和难点是起步控制。针对传统模糊控制在其参数的模糊化过程中人为因素影响较大,获得较优控制参数困难等缺点,基于优秀驾驶员的起步操作经验,利用神经网络自适应学习功能优化模糊控制参数,设计了模糊神经网络控制策略。应用SIMULINK建立了起步模糊神经网络控制系统仿真模型。仿真实验表明,优化了模糊控制模型隶属函数,该控制策略可较好的解决自动变速车辆起步控制问题,为机械式自动变速车辆的开发设计提供了理论依据。     

单路口多相位交通信号模糊控制系统的设计

模糊控制不需建立被控对象的精确数学模型，特别适用于具有较大随机性的城市交通控制。此文同时考虑多相位和倒计时问题，讨论用模糊控制方法对单路口多相位的交通信号进行控制，提出以当前相，后继相的车辆等待长度决定相位信号配时，文中重点讨论该二维模糊控制器的设计，并给出该模型控制系统的总体设计方案，利用Mcs96提供的Watch Dog，结合软件设计保证系统的稳定性，提高系统的抗干扰能力，由于每一相位的配时是根据路口实时数据经模糊推理一次确定，该系统可用于安装倒计时器的路口，具有良好的实用性。     

空调房间温度预估模糊PID控制器的研究

针对中央空调房间温度被控对象的大滞后大惯性,设计了具有Smith预估的模糊自整定PID控制器。建立中央空调房间温度控制系统的数学模型,介绍预估模糊PID控制器结构,以及模糊控制规则的生成方法,并且对该控制方案进行了数字仿真。仿真结果表明:该方法调节精度较高,调节迅速,超调小,具有一定的可行性。     

基于控制历史的延迟系统模糊控制及其仿真

通过对传统模糊控制器的改进，提出一种新型的适合于变延迟系统的控制方法。在对控制对象动态特性的研究的基础上，将控制历史引入控制器的决策过程，设计出了一种基于控制历史的延迟系统模糊控制器。与传统的模糊控制器相比，该模糊控制器用历史的控制信息代替偏差变化率作为模糊控制的决策依据，能够及时地反映出被控制量的变动趋势。仿真结果表明，所提出的控制方法可以明显地改善对延迟对象的控制效果，并具有较强的适应性。     

基于数据挖掘的结构模糊控制规则生成与仿真分析

结合智能控制与现代控制理论,在结构控制领域,提出了直接从已有主动控制算法的成功数据样本中提取模糊控制规则的思想和方法。以顶层设置AMD控制装置的五层钢框架模型结构为例,阐述了提取模糊控制规则的步骤,仿真结果证实了所设计的二输入单输出模糊控制器的有效性和鲁棒性。通过76层Benchmark风振模型的模糊控制仿真分析,进一步验证了这种充分利用主动控制算法获取模糊规则思想的有效性。研究成果使结构振动模糊控制器的设计具有一定的依据,为AMD模糊控制走向工程应用奠定了理论基础。     

CMAC网络与模糊PD并行控制仿真研究

针对工业被控对象广泛存在的不确定性、非线性、时变性和多干扰等特性,提出了一种CMAC网络与模糊PD并行控制器的设计方法。该方法以模糊PD控制代替传统的PD控制实现反馈控制,并采用遗传算法整定模糊PD控制的量化因子,CMAC网络实现前馈控制。仿真结果表明,CMAC网络与模糊PD并行控制相对于PID控制与传统的CMAC网络与PD并行控制具有更强的鲁棒性与抗干扰能力。     

基于PSO的模糊控制及在孵化中的应用

针对孵化系统复杂的动态非线性特性,提出一种基于粒子群优化的模糊控制算法,该算法针对模糊控制器量化因子参数调节的困难,采用PSO的惯性系数的自适应调整机制,用以加速优化算法的收敛性和维持群体的多样性,以寻优模糊控制器量化因子参数,将该方法应用于孵化过程,较好的实现了温度、湿度和含氧量的稳定控制。仿真和实际运行结果表明了所提出的算法的有效性和优越性。