时变滞后系统的一种自校正混合模糊PID控制

普通模糊控制不能对时变滞后系统进行有效控制 ,甚至使系统失去稳定 .在 W.L.Bialkowski 1 983年提出的混合模糊 PID控制器的基础上 ,提出了一种自补偿混合模糊 PID控制器 ,并在此基础上提出了一种对积分系数 KI进行自校正的算法 .经 MATLAB仿真验证 ,该算法具有良好的控制品质 ,适应对象参数大范围变化的时滞系统 ,且易于工程实现 .     

变论域模糊控制器及其在拖车倒车中的应用

建立变论域模糊控制器输出论城上的一种伸缩因子的微分方程模型,得到该伸缩因子的数学表达式.仿真实验表明在输出论城上使用这种新的伸缩因子构造的变论城模糊控制器可以快速准确地实现拖车倒车.在同样的条件下,与经过遗传算法参数优化的模糊控制器和普通模糊控制嚣的控制效果相比较,变论域模糊控制器控制得更加灵敏且几乎无超调.     

多变量模糊控制器的研究

自L.A.Zadeh的《Fuzzy Sets》一文后,模糊系统理论得到了很大发展,一些学者作了许多有益的研究工作.1974年,E.H.Mamdami首先在生产过程使用模糊控制.近十几年来,模糊控制形成了比较一般的语言控制规则及控制算法. 关于多变量的模糊控制的实现非常困难.本文在单变量的基础上,将一个多变量模糊控制器转化成多个单变量模糊控制器的组合,利用补偿的方法,消除多变量模糊系统间的耦合.     

渗氮炉技改中模糊温度控制系统设计及仿真实现

结合形状复杂、易变形渗氮工件工艺优化的需求,运用模糊控制对一台气体渗氮炉的炉温控制系统进行技术改进。首先分析了单一的PID温控方式和模糊温控方式的不足,随后根据工艺升温过程优化的要求提出采用PID控制和模糊控制相结合的(Fuzzy-PID复合控制)两段控温方式,进一步,由控温经验设计模糊温度控制器并在MATLAB7.0软件平台上进行温控系统的仿真实现。仿真结果表明,Fuzzy-PID复合温度控制器的控温效果较单一PID温度控制器的控温效果为好,此仿真研究结果对气体渗氮工艺升温过程的优化有着积极的指导作用。     

检测模糊控制器是否有隐匿缺陷的一种方法

目的:检测模糊控制系统中的模糊控制器是否有隐匿的缺陷。方法:通过分析Zadeh算子与Einstain算子及2种广义算子之间的内在联系,给出了Zadeh算子与Einstain算子及2种广义算子之间的隶属关系,分别用各类算子检测模糊控制系统中的模糊控制器。结果:在检测模糊控制系统中的模糊控制器是否有隐匿的缺陷时,广义算子(如Einstain算子)是一种非常有效的工具。结论:广义算子(如Einstain算子)能检测出模糊控制系统中的模糊控制器是否有隐匿缺陷。     

具有函数的泛逼近性能的模糊控制器的设计

构造出9类具有函数的泛逼近性能的模糊控制器,这些模糊控制器均由模糊蕴涵算子构造而成.利用倒车仿真说明采用具有函数的泛逼近性能的模糊控制器可以用于实际的模糊控制系统中.     

一类离散T-S模糊广义系统的控制器设计

利用离散广义Lyapunov方法和分段模糊Lyapunov函数并借助子系统的性质,解决了一类离散T-S广义模糊系统的模糊控制及模糊状态观测器设计问题,得出了离散T-S模型广义系统其极值子系统的基础上设计出的新的模糊控制器和模糊状态观测器.     

一类具有扇形死区的分数阶神经网络系统的模糊自适应同步

本文研究了一类具有扇形死区的分数阶神经网络系统的同步问题,提出了一种自适应模糊控制方法。首先,采用模糊逻辑系统对不确定的非线性函数进行逼近,通过分数阶自适应定律来更新模糊系统的参数。其次,基于分数阶李雅普诺夫稳定性准则,设计了一种自适应模糊变结构控制器,该控制器可以保证系统状态同步误差收敛到原点的足够小的邻域。最后,通过数值仿真验证本文方法的有效性。     

基于生物适应对策的间接自适应模糊控制

针对一类不确定非线性系统,提出了一种基于生物适应对策的间接自适应模糊控制方法.方法在控制器的设计中,将生态位态势理论函数作为模糊规则的后件构造模糊系统,给出了基于生物适应对策的自适应控制器.控制器的设计既体现了生物对环境的适应性,又体现了生物开发和利用环境的能力.通过实例仿真说明,控制器与常规控制器相比,具有更好的控制效果,而且具有良好的抗干扰性.     

基于模糊自抗扰的塔机消摆控制方法

塔机消摆控制系统是一个非线性、强耦合的复杂系统,传统PID控制效果往往欠佳.对此,建立了一个含阻尼的塔机偏摆系统数学模型,并提出模糊自抗扰控制策略.通过自抗扰控制器对塔机回转与变幅运动进行解耦,模糊算法对自抗扰控制器各参数实施在线调整,并对解耦后的回转、变幅子系统分别进行控制.在仿真实验中,对比其他典型方法,提出的方法摆角消失速度更快,这表明在负载运动过程中,所设计控制器实时性和鲁棒性较好.     

Optimization of Fuzzy Controller for an Uninhabited Airplane Using Genetic Algorithms

The design of a fuzzy logic based controller for an uninhabited airplane using genetic algorithms for parameter optimization is illustrated. The airvehicle mission requires that a prescribed trajectory be followed with a satisfactory accuracy. Fuzzy control modules are present in each of the four control channels. Inputs are position and velocity errors. The parameters of the fuzzy controller are: trapezoidal membership functions, five linguistic values, and height defuzzification method associated with peak value. The scaling factors of the fuzzy controller are optimized by means of a genetic algorithm such that, a performance index, based on errors from a stationary flight path, is minimized. The genetic algorithm is based on binary genetic representation, an elitist roulette wheel selection technique and two genetic operators: mutation and crossover. The performance of the resulting optimal fuzzy controller is assessed through numerical simulation.     

基于GA-BP的模糊神经网络控制器与Elman辨识器的系统设计

提出了一种基于神经网络的模糊控制系统 ,该系统由模糊神经网络控制器和模型辨识网络组成 .文中介绍了模糊神经网络控制器采用遗传算法离线优化与 BP算法在线调整 ,给出了具体控制算法 ,推导了变形 Elmam网络的系统辨识算法 .仿真结果表明了此法的可行性和有效性 .     

Robust control of a spatial robot using fuzzy sliding modes

In order to improve the performance of the sliding mode controller, fuzzy logic sliding mode controller is proposed in this study. The control gain of the conventional sliding mode controller is tuned by a fuzzy logic rule base and, also dynamic sliding surfaces are obtained by changing their slopes using the error states of the system in another fuzzy logic algorithm. These controllers are then combined in order to enhance the performance. Afterwards, proposed controllers were used in trajectory control of a three degrees of freedom spatial robot, which is subjected to noise and parameter variations. Finally, the controllers introduced are compared with a PID controller which is commonly used for control of robotic manipulators in industry. The results indicate the superior performance of the proposed controller.     

Optimal input for identification versus sensitivity reduction of systems with large parameter variations

In this article, we present an algorithm leading to an optimal controller gain for the automatic regulation of a linear system (closed-loop policy) and to an optimal auxiliary input. This is used for the purpose of either system identification, resulting in the maximum sensitivity measure and thus increased accuracy (Refs. 1–25), or system sensitivity reduction, resulting in the minimum sensitivity measure and thus reliable operation (Refs. 26–46). These results, which are robust in terms of parameter variations, are developed without constraints on the input functions.     

Real-coded genetic algorithm for system identification and controller tuning

This paper presents an application of real-coded genetic algorithm (RGA) for system identification and controller tuning in process plants. The genetic algorithm is applied sequentially for system identification and controller tuning. First GA is applied to identify the changes in system parameters. Once the process parameters are identified, the optimal controller parameters are identified using GA. In the proposed genetic algorithm, the optimization variables are represented as floating point numbers. Also, cross over and mutation operators that can directly deal with the floating point numbers are used. The proposed approach has been applied for system identification and controller tuning in nonlinear pH process. The simulation results show that the GA based approach is effective in identifying the parameters of the system and the nonlinearity at various operating points in the nonlinear system.     

Adaptive tuning of the fuzzy controller for robots

An adaptive tuning algorithm of the fuzzy controller is developed for a class of serial-link robot arms. The algorithm can on-line tune parameters of premise and consequence parts of fuzzy rules of the fuzzy basis function (FBF) controller. The main part of the fuzzy controller is a fuzzy basis function network to approximate unknown rigid serial-link robot dynamics. Under some mild assumptions, a stability analysis guarantees that both tracking errors and parameter estimate errors are bounded. Moreover, a robust technique is adopted to deal with uncertainties including approximation errors and external disturbances. Simulations of the proposed controller on the PUMA-560 robot arm demonstrate the effectiveness.     

Willis环状脑动脉瘤系统的自适应模糊控制

针对不确定非线性生物系统—W illis环状脑动脉瘤系统,利用高斯型模糊逻辑系统的逼近能力及新构造的Lyapunov函数,基于模糊建模提出了一种自适应模糊控制器设计的新方案.该方案把逼近误差引入到控制器设计条件中用以改善系统的动态性能.不但设计简单还保证了控制方法的鲁棒性与稳定性.通过反向传播算法调整模糊基函数参数及递归最小二乘法调整参数向量,θ更新控制律,实现了理想跟踪.从理论上研究了脑动脉瘤内血流速度的非线性行为及控制,具有实际意义.仿真结果表明该控制方法的有效性.     

A new hybrid algorithm based on optimal fuzzy controller in multimachine power system

In this article, a new methodology based on fuzzy proportional‐integral‐derivative (PID) controller is proposed to damp low frequency oscillation in multimachine power system where the parameters of proposed controller are optimized offline automatically by hybrid genetic algorithm (GA) and particle swarm optimization (PSO) techniques. This newly proposed method is more efficient because it cope with oscillations and different operating points. In this strategy, the controller is tuned online from the knowledge base and fuzzy interference. In the proposed method, for achieving the desired level of robust performance exact tuning of rule base and membership functions (MF) are very important. The motivation for using the GA and PSO as a hybrid method are to reduce fuzzy effort and take large parametric uncertainties in to account. This newly developed control strategy mixed the advantage of GA and PSO techniques to optimally tune the rule base and MF parameters of fuzzy controller that leads to a flexible controller with simple structure while is easy to implement. The proposed method is tested on three machine nine buses and 16 machine power systems with different operating conditions in present of disturbance and nonlinearity. The effectiveness of proposed controller is compared with robust PSS that tune using PSO and the fuzzy controller which is optimized rule base by GA through figure of demerit and integral of the time multiplied absolute value of the error performance indices. The results evaluation shows that the proposed method achieves good robust performance for a wide range of load change in the presents of disturbance and system nonlinearities and is superior to the other controllers. © 2014 Wiley Periodicals, Inc. Complexity 21: 78–93, 2015     

Decomposition of a complex fuzzy controller for the truck-and-trailer reverse parking problem

The use of fuzzy logic has, in the last twenty years, become standard practice in the field of control. The reason lies in the fuzzy logic’s ability to relatively quickly transfer uncertain experience and knowledge about the observed object’s behaviour into the process of decision making. Nevertheless, one of the biggest problems that arises when using a fuzzy approach is the large number of fuzzy rules that have to be processed in order to produce one decision (i.e. one control output). The number of rules in a fuzzy controller primarily originates from the number of input variables that are entering the decision process and one possible solution for decreasing it is to use the method of decomposition. Its main goal is to implement the equivalent control functionality with a hierarchy of simpler fuzzy controllers. Their main characteristic is a lower number of input variables, which as a consequence leads to a smaller number of fuzzy rules. In our paper we apply the decomposition approach to the classical complex control case of the Truck-and-Trailer (T&T) reverse parking control problem. In such cases the implementation of control using only one fuzzy controller is very complex and the existing solutions, in some details, even deviate from the classical fuzzy approach. Our solution is, on the other hand, based only on the uncertain knowledge about the behaviour of the T&T driver and the results achieved are even better than those achieved by using the existing solutions.     

Model recovery for Hammerstein systems using the auxiliary model based orthogonal matching pursuit method

This article investigates parameter and order identification of a block-oriented Hammerstein system by using the orthogonal matching pursuit method in the compressive sensing theory which deals with how to recover a sparse signal in a known basis with a linear measurement model and a small set of linear measurements. The idea is to parameterize the Hammerstein system into the linear measurement model containing a measurement matrix with some unknown variables and a sparse parameter vector by using the key variable separation principle, then an auxiliary model based orthogonal matching pursuit algorithm is presented to recover the sparse vector.The standard orthogonal matching pursuit algorithm with a known measurement matrix is a popular recovery strategy by picking the supporting basis and the corresponding non-zero element of a sparse signal in a greedy fashion. In contrast to this, the auxiliary model based orthogonal matching pursuit algorithm has unknown variables in the measurement matrix. For a K-sparse signal, the standard orthogonal matching pursuit algorithm takes a fixed number of K stages to pick K columns (atoms) in the measurement matrix, while the auxiliary model based orthogonal matching pursuit algorithm takes steps larger than K to pick K atoms in the measurement matrix with the process of picking and deleting atoms, due to the gradually accurate estimates of the unknown variables step by step.The auxiliary model based orthogonal matching pursuit algorithm can simultaneously identify parameters and orders of the Hammerstein system, and has a high efficient identification performance.