一种自适应模糊CMAC控制器

本文提出一种自适应模糊ＣＭＡＣ控制器的设计方法，该控制器由模糊ＣＭＡＣ神经网络的五层节点实现模糊控制的输入，模糊化，模糊逻辑运算，归一化及输出值准确化运算，并由合适的ＢＰ训练算法修改相应的权系数，实现模糊控制规则的调整。     

基于CPN的模糊控制器系统设计及其应用研究

本文给出一种基对于向传播网络ＣＰＮ的模糊控制器设计。该网络经训练后能得到模糊规则并且具有自动学习功能，在控制性能上较普通控制器有所改进。最后给出加压钢前箱系统的应用实例。     

一种基于PFLC的可演化模糊逻辑控制器设计与实现

常规的模糊控制器主要通过计算机软件或单片机实现，但模糊控制器是一个高度并行的系统，实时性、自适应性要求较高，这种实现方式不能满足现代模糊控制器的设计要求。要解决这个问题必须从算法和器件结构入手。本文提出以可编程模糊逻辑控制器芯片（PFLC）作为可演化的部件，利用遗传算法优化生成模糊规则的演化硬件结构。模糊规则的自适应性是通过引入可调整因子，根据环境的变化自寻优获得。以典型二阶系统模糊控制为例进行仿真实验，其结果表明了这个可演化的模糊逻辑控制器结构的可行性。     

非线性大系统的分散自适应模糊控制*

本文针对非线性大系统，利用模糊系统的逼近能力，提出了一种分散自适应模糊控制器设计的系统方法。控制结构中采用分散模糊系统去自适应补偿过程不确定性，同时用模糊控制器的输出代替常规变结构控制律中的符号函数。利用李亚普诺夫理论，证明了控制算法是全局稳定的，跟踪误差可收敛到零的一个领域内。     

模糊控制器软件开发系统的研究

为缩短模糊控制器的开发周期，本文设计了一模糊控制器的软件开发的总体框架,建立了一个模糊控制器的软件开发平台，并设计了基于BITBUS位癖线结构的计算机模糊控制系统。     

基于递阶遗传算法的模糊控制器的规则生成和参数整定

提出了一种基于递阶遗传算法的模糊控制器的优化设计方法．采用具有层次结构染色体编码方式的遗传算法来设计模糊控制器，实现了语言控制规则的自动生成和隶属函数参数的自动整定．设计过程无需系统的先验知识和训练数据，具有自组织、自学习的特点．仿真结果表明，该方法优化得到的模糊控制器结构简单、性能优良．     

基于在线系统辨识的DMFC模型预测与模糊控制

利用神经网络具有逼近任意复杂非线性函数的能力，以甲醇浓度为神经网络辨识模型的输入量，电池电压／电流密度为输出量．利用1000组实验数据，建立了DMFC电堆的神经网络模型。然后，基于电特性的输入输出关系设计了一个模糊控制器，且利用模糊控制器的输入输出样本训练神经网络。仿真结果表明，所设计的神经网络模糊控制器具有自学习、自适应等优点，达到了在线控制的目的。     

参数自整定模糊控制器在弧焊焊缝跟踪中的应用

本文阐述了焊缝跟踪参数自整定模糊控制器的工作原理，提出了比例因子和量化因子自动调节的方案和实现方法，介绍了模糊控制器的具体设计步骤和单片机参数自整定模糊控制系统的软硬件设计。结果表明，在焊缝跟踪中采用参数自整定模糊控制器可以明显改善系统的性能。     

基于VB.NET和SQL查询命令设计的模糊控制器

VB.NET是功能强大的编程语言，本文在VB.NET编程环境中，结合SQL查询语句设计模糊控制器。重点介绍了模糊控制器设计原理及过程，并给出了在VB.Net编程环境中实现的实例。     

一种通用模糊控制器的研究与设计

设计了一种通用模糊控制器，可适用于不同控制对象。采用软件的方法在线调整量化因子及比例因子，并在控制过程中对规则自动调整和完善，从而使控制规则趋于最优。仿真结果表明，这种通用模糊控制器的控制性能优于普通的模糊控制器，达到较高的控制精度。     

Genetic algorithm design of neural network and fuzzy logic controllers

This paper discusses the design of neural network and fuzzy logic controllers using genetic algorithms, for real-time control of flows in sewerage networks. The soft controllers operate in a critical control range, with a simple set-point strategy governing “easy” cases. The genetic algorithm designs controllers and set-points by repeated application of a simulator. A comparison between neural network, fuzzy logic and benchmark controller performance is presented. Global and local control strategies are compared. Methods to reduce execution time of the genetic algorithm, including the use of a Tabu algorithm for training data selection, are also discussed. The results indicate that local control is superior to global control, and that the genetic algorithm design of soft controllers is feasible even for complex flow systems of a realistic scale. Neural network and fuzzy logic controllers have comparable performance, although neural networks can be successfully optimised more consistently.     

Synchronization of uncertain chaotic systems based on adaptive type-2 fuzzy sliding mode control

A novel direct adaptive interval type-2 fuzzy neural network (FNN) controller in which linguistic fuzzy control rules can be directly incorporated into the controller is developed to synchronize chaotic systems with training data corrupted by noise or rule uncertainties involving external disturbances, in this paper. By incorporating direct adaptive interval type-2 FNN control scheme and sliding mode approach, two non-identical chaotic systems can be synchronized based on Lyapunov stability criterion. Moreover, the chattering phenomena of the control efforts can be reduced and the external disturbance on the synchronization error can be attenuated. The stability of the proposed overall adaptive control scheme will be guaranteed in the sense that all the states and signals are uniformly bounded. From the simulation example, to synchronize two non-identical Chua’s chaotic circuits, it has been shown that type-2 FNN controllers have the potential to overcome the limitations of tpe-1 FNN controllers when training data is corrupted by high levels of uncertainty.     

Recurrent neuro-fuzzy networks for nonlinear process modeling

A type of recurrent neuro-fuzzy network is proposed in this paper to build long-term prediction models for nonlinear processes. The process operation is partitioned into several fuzzy operating regions. Within each region, a local linear model is used to model the process. The global model output is obtained through the centre of gravity defuzzification which is essentially the interpolation of local model outputs. This modeling strategy utilizes both process knowledge and process input/output data. Process knowledge is used to initially divide the process operation into several fuzzy operating regions and to set up the initial fuzzification layer weights. Process I/O data are used to train the network. Network weights are such trained so that the long-term prediction errors are minimized. Through training, membership functions of fuzzy operating regions are refined and local models are learnt. Based on the recurrent neuro-fuzzy network model, a novel type of nonlinear model-based long range predictive controller can be developed and it consists of several local linear model-based predictive controllers. Local controllers are constructed based on the corresponding local linear models and their outputs are combined to form a global control action by using their membership functions. This control strategy has the advantage that control actions can be calculated analytically avoiding the time consuming nonlinear programming procedures required in conventional nonlinear model-based predictive control. The techniques have been successfully applied to the modeling and control of a neutralization process.     

Intelligent Robust Tracking Controls for Holonomic and Nonholonomic Mechanical Systems Using Only Position Measurements

Intelligent robust tracking control designs are proposed in this paper for both uncertain holonomic and nonholonomic mechanical systems. A unified and systematic procedure, that is based on an adaptive fuzzy (or neural network) system and a linear observer, is employed to derive the controllers for these two constrained mechanical systems. Adaptive fuzzy-based (or neural network-based) position feedback tracking controllers can be constructed such that all the states and signals of the closed-loop systems are bounded and the tracking error locally converges to a small region around zero. Only position measurements are required for feedback. The implementation of the fuzzy (or neural network) basis functions depends only on the desired reference information and so once a set of desired trajectories is given, the required basis functions can be explicitly preassigned. Consequently, the intelligent robust position feedback tracking controllers developed here possess the properties of computational simplicity and easy implementation. Finally, simulation examples are presented to demonstrate the effectiveness of the proposed control algorithms.     

An approach to enlarge learning space coverage for robot learningcontrol

In robot learning control, the learning space for executing general motions of multijoint robot manipulators is quite large. Consequently, for most learning schemes, the learning controllers are used as subordinates to conventional controllers or the learning process needs to be repeated each time a new trajectory is encountered, although learning controllers are considered to be capable of generalization. In this paper, we propose an approach for larger learning space coverage in robot learning control. In this approach, a new structure for learning control is proposed to organize information storage via effective memory management. The proposed structure is motivated by the concept of human motor program and consists mainly of a fuzzy system and a cerebellar model articulation controller (CMAC)-type neural network. The fuzzy system is used for governing a number of sampled motions in a class of motions. The CMAC-type neural network is used to generalize the parameters of the fuzzy system, which are appropriate for the governing of the sampled motions, to deal with the whole class of motions. Under this design, in some sense the qualitative fuzzy rules in the fuzzy system are generalized by the CMAC-type neural network and then a larger learning space can be covered. Therefore, the learning effort is dramatically reduced in dealing with a wide range of robot motions, while the learning process is performed only once. Simulations emulating ball carrying under various conditions are presented to demonstrate the effectiveness of the proposed approach     

ANFIS实现的模糊神经网络在交通信号配时优化中的应用

提出一种使用Matlab中的ANFIS模糊神经网络(FNN)工具箱来对传统的模糊控制器进行参数优化的方法，改善了控制器中的隶属度函数形状及分布，并应用于城市单交叉路口的多相位信号配时上．仿真实验证明所提出的算法可以降低车辆平均延误时间，保证车队更顺畅地通过交叉路口．     

A method for design of a hybrid neuro-fuzzy control system based onbehavior modeling

It is known that control signals from a fuzzy logic controller are determined by a response behavior of a controlled object rather than its analytical models. That implies that the fuzzy controller could yield a similar control result for a set of plants with a similar dynamic behavior. This idea lends to modeling of a plant with unknown structure by defining several types of dynamic behaviors. On the basis of dynamic behavior classification, a new method is presented for the design of a neuro-fuzzy control system in two steps: 1) we model a plant with unknown structure by choosing a set of simplified systems with equivalent behavior as “templates” to optimize their fuzzy controllers off-line; and 2) we use an algorithm for system identification to perceive dynamic behavior and a neural network to adapt fuzzy logic controllers by matching the “templates” online. The main advantage of this method is that convergence problem can be avoided during adaptation process. Finally, the proposed method is used to design neuro-fuzzy controllers for a two-link manipulator     

A new LMI-based approach to relaxed quadratic stabilization of T-S fuzzy control systems

This paper proposes a new quadratic stabilization condition for Takagi-Sugeno (T-S) fuzzy control systems. The condition is represented in the form of linear matrix inequalities (LMIs) and is shown to be less conservative than some relaxed quadratic stabilization conditions published recently in the literature. A rigorous theoretic proof is given to show that the proposed condition can include previous results as special cases. In comparison with conventional conditions, the proposed condition is not only suitable for designing fuzzy state feedback controllers but also convenient for fuzzy static output feedback controller design. The latter design work is quite hard for T-S fuzzy control systems. Based on the LMI-based conditions derived, one can easily synthesize controllers for stabilizing T-S fuzzy control systems. Since only a set of LMIs is involved, the controller design is quite simple and numerically tractable. Finally, the validity and applicability of the proposed approach are successfully demonstrated in the control of a continuous-time nonlinear system.     

A multirule-base controller using the robust property of a fuzzycontroller and its design method

This paper suggests a new fuzzy adaptive controller, which is able to solve the problems of classical adaptive controllers and conventional fuzzy adaptive controllers. It explains the architecture of a fuzzy adaptive controller using the robust property of a fuzzy controller. The basic idea of new adaptive control scheme is that an adaptive controller can be constructed with parallel combination of robust controllers. This new adaptive controller uses a multirule-base architecture which has several independent fuzzy controllers in parallel, each with different robust stability area. Out of several independent fuzzy controllers, the most suited one is selected by a system identifier which observes variations in the controlled system parameter. Here, we propose a design procedure which can be carried out mathematically and systematically from the model of a controlled system; related mathematical theorems and their proofs are also given. The performance of the proposed adaptive control algorithm is analyzed through a design example and a DC motor control simulation     

An adaptive fuzzy strategy for motion control of robot manipulators

This paper makes an attempt to develop a self-tuned proportional-integral-derivative (PID)-type fuzzy controller for the motion control of robot manipulators. In recent past, it has been widely believed that static fuzzy controllers can not be suitably applied for controlling manipulators with satisfaction because the robot manipulator dynamics is too complicated. Hence more complicated and sophisticated neuro-fuzzy controllers and fuzzy versions of nonlinear controllers have been more and more applied in this problem domain. The present paper attempts to look back at this widely accepted idea and tries to develop a self-tuned fuzzy controller with small incremental complexity over conventional fuzzy controllers, which can yet attain satisfactory performance. The proposed controller is successfully applied in simulation to control two-link and three-link robot manipulators.