An intelligent usage parameter controller based on dynamic rate leaky bucket for ATM networks

In this paper we present a high performance intelligent traffic controller based on dynamic rate leaky bucket algorithm. In our proposed model, by using a fuzzy controller, the leaky rate is dynamically tuned according to the state of traffic source. Control actions are taken based on real time estimation of actual mean cell rate. Simulation results show that the proposed traffic controller has a high selectivity, good dynamic behavior and has also a null false alarm probability. To achieve the benefits of statistical multiplexing gain, the proposed traffic controller employs a network congestion feedback to take a decision to discard or tag the input violating cells. It is seen that the proposed fuzzy traffic controller protects the QoS of well-behavior connections far better than any traditional traffic controller does.     

Fuzzy‐Neuron Intelligent Coordination Control Fora Unit Power Plant

A novel fuzzy‐neuron intelligent coordination control method for a unit power plant is proposed in this paper. Based on the complementarity between a fuzzy controller and a neuron model‐free controller, a fuzzy‐neuron compound control method for Single‐In‐Single‐Out (SISO) systems is presented to enhance the robustness and precision of the control system. In this new intelligent control system, the fuzzy logic controller is used to speed up the transient response, and the adaptive neuron controller is used to eliminate the steady state error of the system. For the multivariable control system, the multivariable controlled plant is decoupled statically, and then the fuzzy‐neuron intelligent controller is used in each input‐output path of the decoupled plant. To the complex unit power plant, the structure of this new intelligent coordination controller is very simple and the simulation test results show that good performances such as strong robustness and adaptability, etc. are obtained. One of the outstanding advantages is that the proposed method can separate the controller design procedure and control signals from the plant model. It can be used in practice very conveniently.     

通孔井式加热炉模糊控制研究

本文将介绍通孔井式加热炉的结构和自身特点,并根据这些特点提出把双模糊控制器应用于这种特殊结构加热炉的温度控制。研究及工程实践结果表明,这种基于模型参考输入的双模糊控制器对通孔井式炉的温度控制能够取得满意的动态和稳态性能。     

Adaptive fuzzy control for multi-input multi-output nonlinear systems with unknown dead-zone inputs

This paper presents an adaptive fuzzy control scheme for a class of uncertain multi-input multi-output (MIMO) nonlinear systems with the nonsymmetric control gain matrix and the unknown dead-zone inputs. In this scheme, fuzzy systems are used to approximate the unknown nonlinear functions and the estimated symmetric gain matrix is decomposed into a product of one diagonal matrix and two orthogonal matrices. Based on the decomposition results, a controller is developed, therefore, the possible controller singularity problem and the parameter initialization condition constraints problem are avoided. In addition, a dynamic robust controller is employed to compensate for the lumped errors. It is proved that all the signals in the proposed closed-loop system are bounded and that the tracking errors converge asymptotically to zero. A simulation example is used to demonstrate the effectiveness of the proposed scheme.     

Indirect adaptive fuzzy control for a class of nonaffine nonlinear systems with unknown control directions

This article presents an indirect adaptive fuzzy control scheme for a class of nonlinear uncertain nonaffine systems with unknown control directions. The nonlinear nonaffine system is first transformed into an affine form by using a Taylor series expansion, and then fuzzy systems are employed to approximate the equivalent affine system’s unknown nonlinearities. By modifying the estimated input control gain and using a novel smooth robust control term, a stable well-defined adaptive controller is proposed. Simulation results are provided to illustrate the efficiency of the proposed scheme.     

A Highly Accurate Model-Free Motion Control System with a Mamdani Fuzzy Feedback Controller combined with a TSK Fuzzy Feed-forward Controller

In this paper, a new intelligent robot motion control architecture – a highly accurate model-free fuzzy motion control- is proposed in order to achieve improved robot motion accuracy and dynamic performance. Its architecture combines a Mamdani fuzzy proportional (P) and a conventional integral (I) plus derivative (D) controller for the feedback part of the system, and a Takagi-Sugeno-Kang fuzzy controller for the feed-forward, nonlinear part. The fuzzy P + ID controller improves the performance of the nonlinear system, and the TSK fuzzy controller uses a TSK fuzzy inference system based on extended subtractive- clustering method which integrates information on joint angular displacement, velocity and acceleration for torque identification. The advantage of this kind of model-free control is that it uses the information directly from the input/output of the nonlinear system, without any complex robot model computation, in order to decrease the control system’s sensitivity to any dynamical uncertainty. Furthermore, parametric search for clustering parameters in extended subtractive clustering secures the high accuracy of the system identification. Consequently, this proposed model-free fuzzy motion control benefits from the advantages of two kinds of fuzzy system. It not only incorporates flexible design, good performance and simple conception but also ensures precise motion control and great robustness. Comparisons with other intelligent models and results from numerical studies on a 4-bar planar parallel mechanism show the effectiveness and competitiveness of the proposed control.     

变论域智能积分控制器设计

结合模糊控制理论,从优化模糊控制器的角度出发,将论域伸缩因子和智能积分结合起来,设计开发了基于可变论域的智能积分模糊控制器。利用LPC2132 ARM嵌入式芯片作为模糊控制器的核心,LabVIEW搭建监测平台。并针对带有纯滞后的温度控制系统给出了仿真结果。仿真结果表明,对于带有纯滞后的系统,变论域智能积分模糊控制器能够很好地改善纯滞后系统的缺点,对于大延迟、时变、非线性的工业过程系统能够很好的控制效果,其控制效果优于常规的模糊控制器,有较高的工程实用价值。     

Predictive control based on local linear fuzzy models

This paper deals with predictive control based on fuzzy models. A novel algorithm (LOLIMOT) is proposed for the construction of Takagi-Sugeno fuzzy models. The rule consequents are optimized by a local orthogonal least-squares method that selects the significant regressors. The rule premises are optimized by a tree construction algorithm which partitions the input space in hyper-rectangles. A generalized predictive controller (GPC) and a dynamic matrix controller (DMC) are designed. Both controllers require the extraction of a linear model from the Takagi-Sugeno fuzzy model. For the GPC a new technique called local dynamic linearization is proposed that exploits the special structure of the local linear models. The DMC is based on the evaluation of a step response. The effectiveness of both the identification algorithm and the predictive controllers is shown by application to temperature control of an industrial-scale cross-flow heat exchanger.     

某机载雷达智能环境控制器模糊算法设计

为了提高某机载雷达环境控制系统控制品质,设计了一种基于PID控制与模糊控制相结合的智能控制器。文章介绍了该智能控制器的基本原理、系统组成,详细论述了温度控制算法。该算法具有更大的灵活性、更快的响应速度、抗干扰性强和鲁棒性高的优点,特别适用于非线性、时变和大滞后的控制系统。试验表明,采用该算法的环境控制系统,具有良好的控制性能,对机载设备冷却或加热取得了满意的效果。     

具有自适应功能的新型时滞系统控制器

将Smith预估器和内模控制结构结合起来 ,利用模糊控制方法研究出一种智能控制器 ,能在一定的模型误差范围内得到良好的控制品质。主要控制量来源于模糊控制器 ,通过智能积分对模糊控制器不能消除的稳态误差进行克服 ,并对系统性能进行监测 ,使用模糊控制对控制量进行校正。经仿真研究发现 ,这种智能控制器在一定的模型误差范围内有很好的鲁棒性 ,稳态误差为 0。     

Precision positioning of a three-axis optical pickup via a double phase-lead compensator equipped with auto-tuned parameters

The objective of this study is to design an intelligent control servo scheme for the three-axis optical pickups employed in the next-generation optical disc drives. The three-axis pickup owns the capability to move the lens holder in three directions of focusing, tracking and tilting, which is required particularly for higher data-density optical disks and precision measuring instruments to annihilate non-zero lens tilting. The intelligent controller utilizes a commercially often-used double phase-lead compensator equipped with the capability of auto-tuning on control parameters. In this way, the model uncertainty of the pickups caused by manufacturing tolerance and the coupling between three different DOFs of the three-axis pickup can be overcome to render desired precision data-reading. In the initial stage of the study, Lagrange??s equations are employed to derive equations of motion for the lens holder. A double-lead controller equipped with a fuzzy logic parameter tuning algorithm is then designed to perform dynamic decoupling and forge control efforts toward the goals of precision tracking, focusing and zero tilting simultaneously. Along with the controller, a genetic algorithm is developed to search the optimal designed parameters of previously designed auto-tuning algorithm. Finally, the experiments are conducted to show the effectiveness of the controller. With validated performance, the designed intelligent controller is ready to be employed for the next-generation optical disc drives.     

变频多联机空调模糊逻辑控制及仿真研究 *

变频多联机空调由于各蒸发器之间参数相互影响 ,使其运行特性非常复杂。以各室内温度和吸气压力为被控变量分别调节电子膨胀阀开度和压缩机转速 ,提出了一种鲁棒自适应两级模糊比例 —积分 —微分控制器并进行了仿真。该控制器通过一个模糊切换开关将模糊比例 —微分控制器与模糊积分控制器结合起来 ,模糊比例—微分控制器用来在系统动态响应期间减少上升时间和超调 ;模糊积分控制器用来抗干扰和消除稳态误差。仿真结果表明 ,该控制方法和控制器用于温度控制时不仅鲁棒性好 ,而且能够获得优良的动静态控制性能。     

基于数据融合的自适应神经网络模糊控制系统设计

基于数据融合的思想，提出一种非线性系统的自适应神经网络模糊控制器的设计方法。该方法利用数据融合技术降低了模糊控制器的输入维数，简化了模糊控制器的设计。用自适应神经模糊推理系统的神经网络自学习功能完成模糊控制器的设计。仿真结果表明，自适应神经网络模糊控制系统性能优于采用普通的模糊控制器的情况，为数据融合与智能系统技术在非线性系统中的应用作了有益的探索.     

一种染色机温度控制器的设计

以染色过程的温度跟踪控制系统为背景,针对温度对象动态范围宽,其动态特性随温度变化且存在结构变化,常规PID控制不能适应的特点,设计了一种参数可变的PID控制器。在详细分析染色过程温度对象动态特性的基础上,根据染色过程的实际温度和温度偏差．应用模糊推理实时改变控制器增益,并引入了积分分离算法,有效地克服了积分饱和问题。实际运行结果表明,该控制器能适应染色过程动态特性的变化,保证了温度控制的精度,可有效地提高染色质量。     

一种用于非最小相位系统的模糊控制器设计

针对非最小相位系统具有右半平面零点,使系统阶跃响应存在负调,以及常规方法控制不理想等问题,在分析常规模糊控制器的基础上,提出了一种新型的模糊积分控制器的设计方法,改善了常规模糊控制器的动态和稳态性能。对输入变量设计了一种简单可调整宽度的三角形隶属度函数;并针对非最小相位系统,提出了一般情况下控制规则基的设计原则。计算机仿真结果证实了这种设计方法的有效性。     

Real-time control of a water-gas shift reactor by a model-based fuzzy gain scheduling technique

A model-based fuzzy gain scheduling technique is proposed. Fuzzy gain scheduling is a form of variable gain scheduling which involves implementing several linear controllers over a partitioned process space. A higher-level rule-based controller determines which local controller is executed. Unlike conventional gain scheduling, a controller with fuzzy gain scheduling uses fuzzy logic to dynamically interpolate controller parameters near region boundaries based on known local controller parameters. Model-based fuzzy gain scheduling (MFGS) was applied to PID controllers to control a laboratory-scale water-gas shift reactor. The experimental results were compared with those obtained by PID with standard fuzzy gain scheduling, PID with conventional gain scheduling, simple PID and a nonlinear model predictive control (NMPC) strategy. The MFGS technique performed comparably to the NMPC method. It exhibited excellent control behaviour over the desired operating space, which spanned a wide temperature range. The other three PID-based techniques were adequate only within a limited range of the same operating space. Due to the simple algorithm involved, the MFGS technique provides a low cost alternative to other computationally intensive control algorithms such as NMPC.     

板料拉深成形液压系统模糊控制建模

针对板料拉深成形系统中的变压边力液压控制动态性能和稳态精度较差的难题,建立了一个基于模糊PID自适应调整的液压模糊控制模型,并借助MATLAB仿真得到了控制参数的优化模糊查询表．最后结合实际板料拉深成形系统,选取两种变压边力加载模式进行实时变压边力模糊控制应用效果验证．应用结果表明:采用模糊PID自适应控制方式可大大改善拉深成形液压控制系统的动态响应性能和稳态精度．当压边力输入曲线为4吨的阶跃信号,模糊控制下系统的最大超调量仅为理想设定值的4．25％,大大小于原来无PID控制时的21．24％及常规PID控制下的31．07％,且达到稳定状态的调节高度也大大缩小,仅略为5 mm左右,调节时间略为0．24 s;而当输入为U型变压边力加载曲线时,系统的最大超调量比原来无PID控制时下降了约5％,且达到稳态时的调节高度也缩短了约1 mm左右,使整个拉深变压边力系统的动态性能和稳态精度得到了有效的提高．     

A fuzzy logic based supervisory hierarchical control scheme for real time pressure control

This paper describes a supervisory hierarchical fuzzy controller （SHFC） for regulating pressure in a real-time pilot pressure control system. The input scaling factor tuning of a direct expert controller is made using the error and process input parameters in a closed loop system in order to obtain better controller performance for set-point change and load disturbances. This on-line tuning method reduces operator involvement and enhances the controller performance to a wide operating range. The hierarchical control scheme consists of an intelligent upper level supervisory fuzzy controller and a lower level direct fuzzy controller. The upper level controller provides a mechanism to the main goal of the system and the lower level controller delivers the solutions to a particular situation. The control algorithm for the proposed scheme has been developed and tested using an ARM7 microcontroller-based embedded target board for a nonlinear pressure process having dead time. To demonstrate the effectiveness, the results of the proposed hierarchical controller, fuzzy controller and conventional proportional-integral （PI） controller are analyzed. The results prove that the SHFC performance is better in terms of stability and robustness than the conventional control methods.     

智能充电器模糊控制技术的研究

在非线性、时变性、有干扰、具有纯滞后的情况下实现对蓄电池充放电的最佳控制，文章提出了采用模糊控原理来进行蓄电池的充电控制，确定了模糊控制器的结构和算法，进行了双输入单输出模糊控制器的设计，讨论了精确量的模糊化和模糊规则的设计方法以及输出量的反模糊化方法，扩大了查表控制算法所需的存储空间，构建了模糊智能充电系统。     

Comparison and application of three integral-improved methods on conventional fuzzy control strategy

This paper presents three integral methods of a fuzzy knowledge-based control system, to diminish the steady-state output errors. The three controllers, viz the three-input fuzzy controller, fuzzy plus integral controller and are fuzzy-PI controller, have been described and are applied to the two systems. The first is a zero-type-number first-order system whose input is step, and the other is a linearized one-type-number fifth-order electrohydraulic servo system whose input is ramp. Responses of both systems have steady-state errors without integral control. Simulation results have shown that all these three integral methods can effectively eliminate steady-state errors. The three-input fuzzy controller that produces the least overshoot and shortest rise time is the first choice for producing rapid dynamic response and minimum output errors. Fuzzy-PI controller is the second best for producing small output errors.