含不确定性混沌系统的模糊自适应同步

研究了含不确定性混沌系统的同步问题.基于Takagi-Sugeno(T-S)模糊动态模型，给出了一个新的自适应模糊同步控制设计方法.该方法同时适用于相同结构混沌系统的同步以及异构混沌系统的同步.为说明问题，给出了Lorenz混沌系统和Rossler混沌系统的同步控制设计和仿真结果. 关键词： 混沌系统 模糊控制 同步     

混沌系统的变论域模糊控制算法研究

混沌运动和混沌控制受到广泛关注,本文利用模糊控制实现混沌系统的控制.针对模糊控制器精度不高,模糊控制中规则数量与控制精度之间的矛盾,提出了一种基于蚁群优化算法的变论域模糊控制器.通过分析变论域模糊控制中的2类伸缩因子,指出其中1类并不能满足广义伸缩因子条件.提出利用蚁群优化算法对伸缩因子智能寻优的方法,在对蚁群算法改进的基础上,构成一种基于蚁群算法的变论域模糊控制,将设计的控制器用于Duffing混沌系统的控制.仿真结果表明,提出的控制算法在收敛速度和稳态性能上要略优于其他控制方式. 关键词： 混沌 变论域模糊控制 伸缩因子 蚁群优化算法     

连续时间耦合系统中时空混沌的自适应模糊控制

对模型未知的子系统是多维的连续时间耦合系统的时空混沌提出了自适应模糊控制方法.该方法综合了自适应模糊系统的逼近能力和滑模控制鲁棒性强的优点.并用Laypunov方法证明了该方法的稳定性.对模型未知的连续时间耦合Duffing振子时空混沌控制进行了仿真研究,仿真结果证明了所提方法的有效性 关键词： 时空混沌 自适应模糊控制 滑模控制     

混沌蚂蚁群算法设计T-S模糊系统

介绍了混沌蚂蚁群算法，然后利用该算法设计T-S模糊系统，给出了具体的设计过程，并成功地将其应用于非线性动态系统辨识和自适应模糊控制，最后给出了数值仿真，数值仿真表明该方法是有效的.     

混沌蚂蚁群算法设计T-S模糊系统

介绍了混沌蚂蚁群算法,然后利用该算法设计T-S模糊系统,给出了具体的设计过程,并成功地将其应用于非线性动态系统辨识和自适应模糊控制,最后给出了数值仿真,数值仿真表明该方法是有效的. 关键词： 混沌蚂蚁群算法 T-S模糊系统 非线性动态系统辨识 自适应控制     

基于backstepping方法的混沌系统自适应模糊控制

针对一类含未知非线性参量或外界干扰的混沌系统,基于backstepping提出一种新的自适应模糊控制方法,模糊逼近器参量可自动实时调整,任意给定初始参量,根据所给控制律表达式迭代得到控制器,能够成功地把混沌系统控制到预先给定的目标轨道.仿真结果验证了方法的有效性 关键词： 混沌系统 backstepping方法 自适应模糊控制     

基于模糊模型的不同结构的混沌系统同步

针对不同结构的混沌系统广义同步问题，提出了基于T-S 模糊模型的H_∞控制方法，利用Lyapunov方法和线性矩阵不等式技术,给出了误差闭环系统稳定的充分条件.仿真结果表明了方法的有效性. 关键词： H_∞控制')" href="#">H_∞控制 广义同步 混沌系统 模糊控制     

基于组合混沌遗传算法的认知无线电资源分配

提出了基于组合混沌遗传算法用于认知无线电资源分配,设计了相应的组合混沌序列发生器,并分别运用组合混沌遗传算法、粒子群优化算法、模拟退火算法及简单遗传算法对认知无线电资源分配问题进行了仿真分析.结果表明,组合混沌遗传算法具有收敛速度快、搜索空间广、全局收敛等优点.相比其他三种算法,基于组合混沌遗传算法进行资源分配提高了认知无线电系统的传输速率,降低了系统的发射功率及误码率,同时加快了收敛速度. 关键词： 组合混沌 认知无线电 遗传算法 资源分配     

基于遗传算法的统一混沌系统比例-积分-微分神经网络解耦控制研究

提出了采用改进的遗传算法优化比例-积分-微分(PID)神经网络解耦控制器的连接权值，从而实现PID控制器参数的优化及非线性多变量系统的解耦控制. 改进的遗传算法优于基本遗传算法，它使寻优过程中的计算量减少，计算效率提高，收敛速度加快. 将优化后的PID神经网络解耦控制器应用于统一混沌系统的控制中，仿真实验收到良好的控制效果，证明了PID控制器应用于统一混沌系统控制的有效性. 关键词： 混沌系统 遗传算法 比例-积分-微分 神经网络     

混沌系统的遗传神经网络控制

提出遗传神经网络控制混沌新方法.将小扰动技术和周期控制技术结合起来，用遗传算法训练神经网络，使之成为混沌控制器.对Henon映射和Logistic映射的仿真结果说明控制器能产生小扰动控制序列信号，将混沌振荡转变成规则运动状态.该方法无需了解动态系统数学模型，具有一定抗噪声干扰能力，可将它推广应用到其他混沌系统的控制中. 关键词： 遗传算法 神经网络 混沌 周期控制     

基于文化遗传算法的水浴温度模糊控制器设计

水浴温度控制普遍存在滞后、超调、波动性大等特点,且难以建立精确的数学模型,一般采用模糊控制器实现对水浴温度的控制,但模糊控制规则具有较强的主观性,其控制性能往往达不到客观要求。研究遗传算法在模糊控制规则寻优上的应用,将其和文化算法相结合,构成一种具有双层协同进化机制的文化遗传算法,采用专家经验的知识信息构成信仰空间作为指导方向,然后在群体空间中进行遗传算法操作,选取群体中适应度值较高的个体更新信仰空间,再通过迭代优化,得到适应度值高的个体,将其作为优化的模糊控制规则。仿真实验表明,优化后的规则在控制过程中的动态温度跟踪和稳态误差方面均具有较好的效果。     

Vibration suppression control of smart piezoelectric rotating truss structure by parallel neuro-fuzzy control with genetic algorithm tuning

The main goal of this paper is to develop a novel approach for vibration control on a piezoelectric rotating truss structure. This study will analyze the dynamics and control of a flexible structure system with multiple degrees of freedom, represented in this research as a clamped–free–free–free truss type plate rotated by motors. The controller has two separate feedback loops for tracking and damping, and the vibration suppression controller is independent of position tracking control. In addition to stabilizing the actual system, the proposed proportional–derivative (PD) control, based on genetic algorithm (GA) to seek the primary optimal control gain, must supplement a fuzzy control law to ensure a stable nonlinear system. This is done by using an intelligent fuzzy controller based on adaptive neuro-fuzzy inference system (ANFIS) with GA tuning to increase the efficiency of fuzzy control. The PD controller, in its assisting role, easily stabilized the linear system. The fuzzy controller rule base was then constructed based on PD performance-related knowledge. Experimental validation for such a structure demonstrates the effectiveness of the proposed controller. The broad range of problems discussed in this research will be found useful in civil, mechanical, and aerospace engineering, for flexible structures with multiple degree-of-freedom motion.     

T—S模糊控制器中各类算子对系统输出的影响

模糊控制系统的核心是模糊控制算法，对Takagi-Sugeno（以下简称T—S）模糊控制器，由于其合取算子与汇总算子选取的不同，使其算法区别很大。文中对几种常用T-S模糊控制算法的输出曲面及误差曲面进行比较，以确定这些算子变化对系统输出的影响。     

Fractional-order optimal fuzzy control for network delay

Fuzzy logic control has been used frequently in tuning network control system (NCS) due to its on-line dynamic static non-linear match and several remarkable fractional-order controllers have achieved satisfactory control performance when applied to NCS in present years, therefore, in this paper, a novel fractional fuzzy logic controller which combined the fractional algorithm and fuzzy logic control together has been proposed to deal with fixed and random network induced delays in closed-loop feedback systems. The comparisons of set-point tracking performances of fractional fuzzy logic PID controller (FFuzzyPID), conventional fuzzy logic PID controller(FuzzyPID), fractional optimal PID controller (FOPID), and optimal PID controller(OPID) on a representative plant with fixed and random network delays have been shown with simulations. The simulation results indicate that fractional fuzzy logic controller has higher capability to handle network delays compared with other controllers in most cases.     

Free vibration control of smart composite beams using particle swarm optimized self-tuning fuzzy logic controller

This paper deals with active free vibrations control of smart composite beams using particle-swarm optimized self-tuning fuzzy logic controller. In order to improve the performance and robustness of the fuzzy logic controller, this paper proposes integration of self-tuning method, where scaling factors of the input variables in the fuzzy logic controller are adjusted via peak observer, with optimization of membership functions using the particle swarm optimization algorithm. The Mamdani and zero-order Takagi–Sugeno–Kang fuzzy inference methods are employed. In order to overcome stability problem, at the same time keeping advantages of the proposed self-tuning fuzzy logic controller, this controller is combined with the LQR making composite controller. Several numerical studies are provided for the cantilever composite beam for both single mode and multimodal cases. In the multimodal case, a large-scale system is decomposed into smaller subsystems in a parallel structure. In order to represent the efficiency of the proposed controller, obtained results are compared with the corresponding results in the cases of the optimized fuzzy logic controllers with constant scaling factors and linear quadratic regulator.     

Robust adaptive fuzzy neural tracking control for a class of unknown chaotic systems

In this paper, an adaptive fuzzy neural controller (AFNC) for a class of unknown chaotic systems is proposed. The proposed AFNC is comprised of a fuzzy neural controller and a robust controller. The fuzzy neural controller including a fuzzy neural network identifier (FNNI) is the principal controller. The FNNI is used for online estimation of the controlled system dynamics by tuning the parameters of fuzzy neural network (FNN). The Gaussian function, a specific example of radial basis function, is adopted here as a membership function. So, the tuning parameters include the weighting factors in the consequent part and the means and variances of the Gaussian membership functions in the antecedent part of fuzzy implications. To tune the parameters online, the back-propagation (BP) algorithm is developed. The robust controller is used to guarantee the stability and to control the performance of the closed-loop adaptive system, which is achieved always. Finally, simulation results show that the AFNC can achieve favourable tracking performances.     

A simplified fuzzy model to mimic a nonlinear system, applied to aplasma source

This paper develops a fuzzy model to simulate the behaviour of a nonlinear system, in particular a plasma source, with a view to developing a control system for processing plasmas employing a helicon source. Genetic algorithms are employed to optimize fuzzy rules related to the parameters of the fuzzy model which contain a set of variable zeros and poles of the nonlinear system as well as its time delay. A practical application of the fuzzy model is to estimate the electron number density of a low-temperature plasma. Based on the membership functions of the input and output, a set of fuzzy rules by which the variable zeros and poles are identified is derived and optimized using a genetic algorithm. The principal reason for investigating the proposed fuzzy model is the subsequent computer-aided design of a fuzzy controller to control the nonlinear system. Two experimental results are presented to validate the fuzzy model method. One shows a computer simulation and the other predicts the real-time behaviour of the plasma source as its input parameters are varied     

嵌入式电子节能控制器的设计与实现

节能控制能够有效降低能耗,对保护环境等方面具有重要影响。但目前大多数电子节能控制器都是通过采用单片机技术和双向晶闸管过零触发交流调压电路对电子节能控制器进行设计。通过介绍电子的负荷特点和节能原理,分析电子节能控制器的硬件组成电路,并对电子节能控制器的主要软件程序的流程图进行设计,完成电子节能控制器设计。但这种方法节能控制效果较低,难以保证电子节能控制器性能,为此,提出一种基于模糊PID控制的嵌入式电子节能控制器设计与实现方法。首先通过对嵌入式电子节能控制器的处理器、电源电路、复位电路、系统时钟电路、JTAG接口电路、D/A转换电路、功放电路、双极性电源电路以及嵌入式电子节能控制器硬件PCB板器件布局等的设计,完成嵌入式电子节能控制器硬件设计。在此基础上,选用模糊PID控制方法对嵌入式电子节能控制器进行设计。通过分析模糊PID控制原理,介绍加入自调节因子的模糊PID控制的算法设计,以此确定输入输出隶属度函数,再利用模糊推理和模糊规则,得到电子节能控制器的模糊控制过程,从而完成嵌入式电子节能控制器的设计。实验证明,所提方法能够有效提高嵌入式电子节能控制器的节能控制效果,具有良好的使用价值。     

超声振动系统频率自动跟踪的模糊控制算法研究

针对大功率超声应用中,工作频带窄、频率跟踪速度要求快的特点,提出将模糊控制智能算法（Fuzzy Control）和数字频率直接合成技术（DDS）相结合的频率自动跟踪方案,依据经验归纳制定模糊控制器来快速跟踪工作频率,将驱动频率调节至谐振频率附近;随后只采用DDS进行频率的微调节,使得电源频率工作在超声换能器的谐振频率点上。最后,通过在MATLAB软件中对控制算法进行验证,表明本文算法具有可行性,能够实现对大功率超声波电源的频率自动跟踪。