网络时延的模糊逻辑补偿方法的研究

当网络应用到控制系统中时,网络将引起时延,从而对闭环网络控制系统产生一些不利的影响,比如系统性能下降,系统不稳定等。本文介绍了通过在已有的PI控制器的基础上,再增加一个模糊逻辑补偿器来补偿网络控制系统中网络所引起的时延,其优点是不需要再重新设计已有的PI控制器,而只是简单地将模糊逻辑控制器的输出作为一个参数来调节PI控制器所提供的控制信号。文中采用了MATLAB/SIMULINK仿真,仿真结果表明了该方法的有效性。     

网络时延的模糊逻辑补偿方法的研究

当网络应用到控制系统中时，网络将引起时延，从而对闭环网络控制系统产生一些不利的影响，比如系统性能下降，系统不稳定等。本文介绍了通过在已有的PI控制器的基础上，再增加一个模糊逻辑补偿器来补偿网络控制系统中网络所引起的时延，其优点是不需要再重新设计已有的PI控制器，而只是简单地将模糊逻辑控制器的输出作为一个参数来调节PI控制器所提供的控制信号。文中采用了MATLAB／SIMULINK仿真，仿真结果表明了该方法的有效性。     

网络控制系统模糊PI时滞补偿算法研究

针对网络控制系统中普遍存在的时滞问题,将模糊逻辑补偿算法引入传统PI控制器的设计,以消除闭环网络控制系统中由时滞引起的控制性能下降、系统不稳定等不利影响.模糊PI时滞补偿算法中,无需更改传统PI控制器的设计,以模糊补偿器调制PI控制器的输出.以实时性要求较高的无刷直流电机为应用实例,仿真结果证明了该控制算法的有效性和可行性,该方法可使具有时滞特性的远程网络控制系统保持良好的动、静态特性与较强的抗干扰能力.     

模糊逻辑补偿的网络PI控制系统及稳定性分析

针对现代工业过程中常用的网络PI控制系统, 分析了网络所诱导的时滞对PI控制系统性能的影响, 设计了一个模糊逻辑补偿器来实现对网络所诱导的时滞的补偿. 利用Hermite-Biehler引理在准多项式稳定性问题上的推广, 研究了具有模糊逻辑补偿的网络PI控制系统的稳定性问题, 给出了使闭环系统稳定的模糊逻辑调节参数的取值范围. 仿真结果验证了理论分析结果的有效性.     

网络控制中基于FSMC观测器的时延补偿

针对网络控制系统中因存在通讯时延、网络诱导噪声及数据丢失等而可能引起系统性能降低或不稳定的问题，利用模糊滑模控制理论，在时延存在的情况下，基于观测器建立不确定网络控制系统模型；并利用预估方法对网络控制系统的时延进行补偿，从而保证系统的稳定。设计模糊滑模控制器（FSMC）来抑制网络控制系统中的诱导噪声及滑模面上的“抖动”，以及采用预估补偿策略处理网络中的时滞和数据包丢失等，可有效保证系统的稳定。仿真实例表明了该算法的合理性、有效性。     

基于时延动态补偿的网络控制器的设计

在网络控制系统中由于网络带宽的限制,不可避免地使网络系统产生网络诱导时延,从而导致系统性能下降甚至不稳定。文章针对延时不确定使得Smith预估补偿控制效果差的问题,提出了新的Smith补偿控制算法,根据控制器反馈信息对网络延时进行动态补偿,并与模糊PI控制方法相结合,构成Smith预估模糊PI控制器,使得闭环控制系统即使在模型失配的情况下,仍具有较高的稳定性、较强的鲁棒性。仿真结果表明,该方法可行有效。     

基于模糊自适应PI改进的Smith预估器在网络化控制系统中的应用研究

网络化控制系统（NCS）中不可避免的网络诱导时延使传统的控制器难以达到较好的控制效果,针对网络化控制系统中前向传输时延无法精确测量的特点,提出了一种有效的控制方法。仿真结果表明,采用Smith预估控制与模糊自适应PI控制算法相结合的方法,能有效地增强网络化控制系统控制规律的自适应性,显著改善和提高系统的抗干扰性和鲁棒性能。     

非线性时延网络控制系统的模糊建模与控制

针对时变网络诱导时延小于一个采样周期的非线性时延网络控制系统,讨论系统的稳定性及控制器的设计方法.利用基于“IF-THEN”规则的模糊模型近似系统中的非线性,将时延的不确定性转化为系统参数的不确定性,从而将此类非线性网络控制系统建模为一类具有参数不确定性的离散Takagi-Sugeno（T-S）模糊模型.基于建立的模型,利用Lyapunov方法和线性矩阵不等式方法,分析了系统的稳定性及模糊状态反馈控制器的设计方法,最后通过仿真实例验证了所提出方法的有效性.     

基于自适应模糊神经网络控制器的网络控制系统

网络控制系统中存在着时延、丢包、网络干扰等问题。针对网络控制系统中存在恶化系统的控制性能,甚至导致系统不稳定的因素,提出了一种基于自适应模糊神经网络控制器的网络控制系统,它能根据系统的实际输出与期望输出误差,利用自适应模糊控制和神经网络自学习的原理进行控制参数的自行调整,以符合控制系统的实际要求,同时,分析了网络延时,丢包率及网络干扰因素对系统性能的影响。利用TrueTime工具箱建立了包含自适应模糊神经网络控制器的网络控制系统的仿真模型,并将其分别与基于常规PID控制器的网络控制系统和基于模糊参数PID控制器的网络控制系统进行了比较。实验结果表明,在相同的网络环境下,基于自适应模糊神经网络控制器的网络控制系统的控制效果比基于常规的PID控制器和基于模糊参数PID控制器的要好,且具有较好的抗干扰能力和鲁棒性能。     

时戳PI广义预测在网络控制系统中的应用

网络随机时延的存在使得控制系统的性能受到很大的负面影响,甚至引起系统的不稳定,为此提出了基于时戳的PI型广义预测控制算法.在给出了系统环路时延的获取方法的基础上,运用PI型广义预测算法求得系统的未来信息,并将此信息发送到执行器,在执行器端给出了网络补偿器的设计.之后对控制系统的稳定性进行了分析,得到了闭环网络控制系统稳定的条件.最后利用TrueTime工具箱进行仿真研究,验证了该算法的有效性.     

A novel intelligent controller for combating stiction in pneumatic control valves

Pneumatic control valve introduces limit cycles in process variables due to stiction nonlinearity. In this paper a novel stiction combating intelligent controller (SCIC) based on fuzzy logic has been proposed. The proposed technique reduces the complexity of the overall control scheme as it does not require any additional compensator. The SCIC controller is a variable gain fuzzy Proportional Integral (PI) controller making use of Takagi-Sugeno (TS) scheme. The performance of the SCIC controller has been investigated and compared with conventional PI controller on a laboratory scale flow process. SCIC controller outperformed PI controller and provided promising performance with lesser aggressive stem movement.     

FPGA‐based adaptive dynamic sliding‐mode neural control for a brushless DC motor

In the adaptive neural control design, since the number of hidden neurons is finite for real‐time applications, the approximation errors introduced by the neural network cannot be inevitable. To ensure the stability of the adaptive neural control system, a switching compensator is designed to dispel the approximation error. However, it will lead to substantial chattering in the control effort. In this paper, an adaptive dynamic sliding‐mode neural control (ADSNC) system composed of a neural controller and a fuzzy compensator is proposed to tackle this problem. The neural controller, using a radial basis function neural network, is the main controller and the fuzzy compensator is designed to eliminate the approximation error introduced by the neural controller. Moreover, a proportional‐integral‐type adaptation learning algorithm is developed based on the Lyapunov function; thus not only the system stability can be guaranteed but also the convergence of the tracking error and controller parameters can speed up. Finally, the proposed ADSNC system is implemented based on a field programmable gate array chip for low‐cost and high‐performance industrial applications and is applied to control a brushless DC (BLDC) motor to show its effectiveness. The experimental results demonstrate the proposed ADSNC scheme can achieve favorable control performance without encountering chattering phenomena. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Design of an adaptive self-organizing fuzzy neural network controller for uncertain nonlinear chaotic systems

Though the control performances of the fuzzy neural network controller are acceptable in many previous published papers, the applications are only parameter learning in which the parameters of fuzzy rules are adjusted but the number of fuzzy rules should be determined by some trials. In this paper, a Takagi–Sugeno-Kang (TSK)-type self-organizing fuzzy neural network (TSK-SOFNN) is studied. The learning algorithm of the proposed TSK-SOFNN not only automatically generates and prunes the fuzzy rules of TSK-SOFNN but also adjusts the parameters of existing fuzzy rules in TSK-SOFNN. Then, an adaptive self-organizing fuzzy neural network controller (ASOFNNC) system composed of a neural controller and a smooth compensator is proposed. The neural controller using the TSK-SOFNN is designed to approximate an ideal controller, and the smooth compensator is designed to dispel the approximation error between the ideal controller and the neural controller. Moreover, a proportional-integral (PI) type parameter tuning mechanism is derived based on the Lyapunov stability theory, thus not only the system stability can be achieved but also the convergence of tracking error can be speeded up. Finally, the proposed ASOFNNC system is applied to a chaotic system. The simulation results verify the system stabilization, favorable tracking performance, and no chattering phenomena can be achieved using the proposed ASOFNNC system.     

Structure and stability analysis of a Takagi–Sugeno fuzzy PI controller with application to tissue hyperthermia therapy

 In this paper, we first reveal the analytical structure of a simple Takagi–Sugeno (TS) fuzzy PI controller relative to the linear PI controller. The fuzzy controller consists of two linear input fuzzy sets, four TS fuzzy rules with linear consequent, Zadeh fuzzy logic AND and the centroid defuzzifier. We prove that the fuzzy controller is actually a nonlinear PI controller with the gains changing with process output. Utilizing the well-known small Gain Theorem in control theory, we then derive sufficient conditions for global stability of the fuzzy control systems involving the TS fuzzy PI controller. Finally, as an application demonstration, we apply the fuzzy PI controller to control issue temperature, in computer simulation, during hyperthermia therapy. The relationship between heat energy and tissue temperature is represented by a linear time-varying model with a time delay. The sufficient conditions for global stability are used to design a stable fuzzy control system. Our simulation results show that the fuzzy PI control system achieves satisfactory temperature control performance. The control system is robust and stable even when the model parameters are changed suddenly and significantly.     

无刷直流电机的模糊神经网络控制设计

在无刷直流电机(BLDCM)的控制上,传统PID等控制方法存在或多或少的不足.在模糊PID控制的基础上提出了一种模糊神经网络PI控制器的设计方法.该方法结合了模糊逻辑与神经网络,使得模糊控制器模拟了人的控制功能,不仅对环境变化有较强的适应能力,还拥有自学习能力.相比模糊PID控制,其具有计算量小、稳定性强等特点.对BLDCM进行建模与分析;在BLDCM数学模型的基础上,分别设计模糊PID控制器和模糊神经网络PI控制器;对设计的控制器进行仿真验证并分析.实验结果表明,模糊神经网络PI控制具有跟踪性能好、超调小、响应快、脉动小等优点,其动静态特性均优于模糊PID控制.     

基于电加热炉温度系统的Smith-FNNC-PI复合控制器研究

针对大滞后的电加热炉温度控制系统,研究了一种由Smith预估控制、模糊神经网络控制(FNNC)与传统PI相结合的复合控制器。该控制器将模糊控制具有的较强逻辑推理功能、神经网络具有的较强自学习能力以及传统PI控制的优点融为一体。仿真结果表明该复合式控制器具有良好的稳定性和鲁棒性,对于大时间滞后的电加热炉温控系统是一种实用而简便的控制方法。     

Fuzzy predictive PI control for processes with large time delays

This paper presents the design, tuning and performance analysis of a new predictive fuzzy controller structure for higher order plants with large time delays. The designed controller consists of a fuzzy proportional-integral (PI) part and a fuzzy predictor. The fuzzy predictive PI controller combines the advantages of fuzzy control while maintaining the simplicity and robustness of a conventional PI controller. The dynamics of the prediction term are adaptive to the system's time delay. The prediction term has two parts: a fuzzy predictor that uses the system time delay as an input for calculating the prediction horizon and an exponential term that uses the prediction horizon as its positive power. The prediction term also introduces phase lead into the system which compensates for the phase lag due to the time delay in the plant, thereby stabilizing the closed-loop configuration. The performance of the proposed controller is compared with the responses of the conventional predictive PI controller, showing many advantages of the new design over its conventional counterpart.