DISCRETIZING CONTINUOUS‐TIME CONTROLLERS WITH FUZZY LOGIC SYSTEMS AND ITS STABILITY ANALYSIS

In this paper, a new method, applying the fuzzy logic system, is proposed to discretize the continuous‐time controller in computer‐controlled systems. All the continuous‐time controllers can be reconstructed by the proposed method under the Sampling Theorem. That is, the fuzzy logic systems are used to add nonlinearity and to approximate smooth functions. Hence, the proposed controller is a new smooth controller that can replace the original controller, independent of the sampling time under the Sampling Theorem. Consequently, the proposed controller not only can discretize the continuous‐time controllers, but also can tolerate a wider range of sampling time uncertainty. Besides, the input‐output stability is proposed for discretizing the continuous‐time controller of the fuzzy logic systems. Finally, computer simulation shows that the proposed method can easily reconstructthe continuous‐time controller and has very good robustness for different sampling times.     

时变修正因子二级自寻优模糊控制器

首先给出一种实时性好精度高的局部连续模糊算法,并与已有的方法相结合,构造了 一个时变修正因子的模糊控制器;其次给出了一种可快速精确逼近最优解的模拟退火算法,融 合这两种方法构成了自寻优模糊控制器.仿真结果表明这种自寻优控制器对大时滞对象,非线 性不稳定且模型未知对象都有良好的控制效果.该方法最大的优点是无需对象的数学模型和人 的经验,这为非线性模型不确定且不稳定对象的最优控制提供了一条途径.     

Dynamic output feedback control with output quantizer for nonlinear uncertain T‐S fuzzy systems with multiple time‐varying input delays and unmatched disturbances

The dynamic output feedback control problem with output quantizer is investigated for a class of nonlinear uncertain Takagi‐Sugeno (T‐S) fuzzy systems with multiple time‐varying input delays and unmatched disturbances. The T‐S fuzzy model is employed to approximate the nonlinear uncertain system, and the output space is partitioned into operating regions and interpolation regions based on the structural information in the fuzzy rules. The output quantizer is introduced for the controller design, and the dynamic output feedback controller with output quantizer is constructed based on the T‐S fuzzy model. Stability conditions in the form of linear matrix inequalities are derived by introducing the S‐procedure, such that the closed‐loop system is stable and the solutions converge to a ball. The control design conditions are relaxed and design flexibility is enhanced because of the developed controller. By introducing the output‐space partition method and S‐procedure, the unmatched regions between the system plant and the controller caused by the quantization errors can be solved in the control design. Finally, simulations are given to verify the effectiveness of the proposed method.     

基于模糊内模控制的球磨机系统的研究

球磨机系统具有多变量、强耦合、长时滞以及生产工况变化大等特点,采用常规控制策略很难达到控制目标。针对该问题,文章提出了一种基于模糊内模控制的球磨机系统的设计方案,分析了球磨机工作原理,建立了其数学模型,详细介绍了球磨机系统的解耦分析,最后给出了球磨机模糊内模控制器的设计方法。仿真结果表明,基于模糊内模控制的球磨机系统具有良好的解耦能力,鲁棒性强,能够较好地解决球磨机系统的时变性、耦合性和延迟性问题,具有一定的工程应用价值。     

A Clustering-analysis-based membership functions formation method for fuzzy controller of ball mill pulverizing system

This paper proposes a clustering-analysis-based membership functions formation method for fuzzy controller of ball mill pulverizing system. An improved density-based clustering algorithm is presented to detect the clusters in the field database and the clusters are agglomerated or divided to form the new clusters according to the proximity or the dissimilarity between objects. Then, the new clusters are projected into the domains of the control variables to form the intervals, and the membership functions are established based on the intervals and the projections of the centroids of the clusters. The experiments results verify that the performance of our membership functions formation method is better and the fuzzy controller designed by our method has higher control quality. In addition, the fuzzy controller has been put into practice and the field data verify the effectiveness of the fuzzy controller.     

大时滞系统自适应模糊Smith控制

针对大时滞对象,把史密斯（Smith）预估控制原理和模糊控制器参数的自适应调整方法结合起来,即在Smith预估控制系统中,利用自适应机制在线整定模糊控制器的参数,即根据控制系统在各个阶段呈现出的不同特点来适时调节模糊控制器的量化和比例因子,以适应对象特性的变化。仿真研究表明,所提方法能有效克服普通模糊控制算法不适应大时滞系统控制和常规Smith算法过于依赖模型精度的缺陷,提高了普通模糊控制器对大滞后系统的控制能力。同时该算法还具有很强的鲁棒性和良好的控制品质。     

自主轮式机器人THMR－V的混合模糊逻辑控制

轮式机器人的控制问题是控制研究的关键问题之一，对高速自主导航的轮式机器人，控制器的实时性、精确性和鲁棒性要求很高．在本文中，根据PID控制和模糊逻辑控制的各自优点，将传统的PID控制与模糊逻辑控制结合起来，提出了一种混合模糊逻辑控制算法. 经实验检验，该算法具有很高的实时性、控制精度和鲁棒性，能够满足机器人高速自主导航的需要．     

线性插值模糊控制及其在PLC上的实现

文章针对常规模糊控制器控制精度低的问题,提出了一种提高模糊控制器控制精度的方法;首先分析了常规模糊控制器存在的稳态控制精度差的原因,在此基础上给出一种通过在线查表插值算法提高模糊控制精度的方法及该算法在PLC上的实现,最后在液位控制系统上进行实验应用并和常规模糊控制器的控制效果进行了比较;结果表明,这种改进后的插值模糊控制器能有效消除系统稳态误差,提高模糊控制器的控制精度,而且易于在PLC上实现,有一定的实用价值。     

HVPP算法在指针表盘视觉检测中的研究

摘  要：选矿厂球磨机润滑油压力是一个至关重要的实时监测参数。基于FPGA采用了双线性插值法、DoG滤波器（高斯差分滤波器）、Marr-Hildreth边缘检测器及分水岭算法,并提出一种新算法--横竖端点寻点法（HVPP）,对压力表图像进行分割、提取及数字识别,从而实现了对指针表盘图像的智能读数。实验结果表明采用基于逻辑电路的快速算法实现表盘图像的机器识别具有实时性好、识别率较高的优点。     

Brief communication: Uniform ultimate boundedness of a fuzzy logic controlled industrial robot

This article addresses the proof of uniform ultimate boundedness of a fuzzy logic controller plus a computed torque control scheme applied to trajectory tracking control of robotic manipulators. Further improvement of the performance of this fuzzy logic control scheme is achieved through automatic tuning of a weight parameter α leading to a self‐tuning fuzzy logic compensator. Experimental results demonstrate the effectiveness of the computed torque and fuzzy compensation scheme, as well as the self‐tuning fuzzy logic controller, applied to an industrial CRS Robotics Corporation A460 robot during a trajectory tracking task. © 2001 John Wiley & Sons, Inc.     

卷染机模糊控制系统设计

采用模糊逻辑控制器设计控制规律,提出了对具有明显耦合作用的二输入/二输出系 统的一种模糊控制器设计方案,即"在线规则自调整"和"主从交替"策略.在模糊控制系统中采 用了变频调速器作为执行元件控制张力和线速.以卷染机为控制对象,用8098和两台变频调速 器实现了整个模糊控制系统,样机运行的测试结果表明,该模糊控制系统设计是成功的.     

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。     

Real-time NURBS interpolation using FPGA for high speed motion control

Modern motion control adopts NURBS (Non-Uniform Rational B-Spline) interpolation for the purpose of achieving high-speed and high-accuracy performance. However, in conventional control architectures, the computation of the basis functions of a NURBS curve is very time-consuming due to serial computing constraints. In this paper, a novel FPGA (Field Programmable Gate Array) based motion controller utilizing its high-speed parallel computing power is proposed to realize the Cox-de Boor algorithm for second and higher degrees NURBS interpolation. The motion control algorithm is also embedded in the FPGA chip to implement real-time control and NURBS interpolation simultaneously for multi-axis servo systems. The proposed FPGA-based motion controller is capable of performing the Cox-de Boor algorithm and the IIR (Infinite Impulse Response) control algorithm in about 46 clock cycles, as compared to the 1303 clock cycles by the traditional approach. Numerical simulations and experimental tests using an X-Y table verify the outstanding computation performance of the FPGA-based motion controller. The result indicates that shorter sampling time (10 μs) can be achieved for NURBS interpolation which is highly critical to the success of high-speed and high-accuracy motion control.     

一种用于非线性控制的神经网络模糊自组织控制器

本文提出一种神经网络自组织控制器，并应用于非线性跟踪控制中，为了加快模糊控制器的在线学习，文中给出了一种变的最速梯度下降学习算法，仿真结果表明，该控制是有效的。     

基于数字温度传感器的模糊温度控制系统

应用数字温度传感芯片LM74和单片机设计了一种自优化规则模糊温度控制系统应用于环氧树脂基纳米复合材料固化温度控制,该控制器应用带灵敏度因子的全论域参数自调整算法在控制过程中实现控制规则自动优化。通过离线计算将控制规则优化信息融合于控制查询表中,在不增加在线计算量的前提下依据控制状态动态优化控制规则,增强了对控制对象参数变化的适应性,改善了动态性能。仿真和实际应用证明了该温度控制器的有效性。     

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.     

遗传算法和模糊控制的融合研究与设计

将遗传算法与模糊控制相结合，能达到取长补短的作用，一方面，模糊控制能够表达非线性和模糊性的系统知识；另一方面，通过遗传算法所拥有的高度并行，随机、全局搜索能力，使控制系统具有了学习能力，提出了先使用模糊逻辑的思想进行交叉概率和变异概率的整定，再利用模糊遗传算法对模糊子集进行了划分，可获得一个基于一定性能指标的次优或最优模糊控制器，又以二阶系统为例进行了计算机仿真，研究结果表明这种方法是有效的。     

基于复合前馈模糊PID的位置伺服系统研究

在现代伺服控制系统中,PID控制在响应速度和位置跟踪精度方面存在不足。针对此种不足,提出了一种基于复合前馈模糊PID的控制算法。同时,使用该算法建立了控制系统的数学模型和策略,并使用Matlab和TMS320F28379D控制器搭建了系统仿真模型进行实验验证。实验结果表明,该复合模糊控制算法具有快速响应、准确、无超调等特性,满足机器人等伺服控制场合需要。     

Adaptive fuzzy robust control of PMSM with smooth inverse based dead-zone compensation

It is a challenging work to design high precision/high performance motion controller for permanent magnet synchronous motor (PMSM) due to some difficulties, such as varying operating conditions, parametric uncertainties and external disturbances. In order to improve tracking control performance of PMSM, this paper proposes an adaptive fuzzy robust control (AFRC) algorithm with smooth inverse based dead-zone compensation. Instead of nonsmooth dead-zone inverse which would cause the possible control signal chattering phenomenon, a new smooth dead-zone inverse is proposed for non-symmetric dead-zone compensation in PMSM system. AFRC controller is synthesized by combining backstepping technique and small gain theorem. Discontinuous projectionbased parameter adaptive law is used to estimate unknown system parameters. The Takagi-Sugeno fuzzy logic systems are employed to approximate the unstructured dynamics. Robust control law ensures the robustness of closed loop control system. The proposed AFRC algorithm with smooth inverse based dead-zone compensation is verified on a practical PMSM control system. The comparative experimental results indicate that the smooth inverse for non-symmetric dead-zone nonlinearity can effectively avoid the chattering phenomenon which would be caused by nonsmooth dead-zone inverse, and the proposed control strategy can improve the PMSM output tracking performance.     

A self-learning fuzzy logic controller using genetic algorithmswith reinforcements

This paper presents a new method for learning a fuzzy logic controller automatically. A reinforcement learning technique is applied to a multilayer neural network model of a fuzzy logic controller. The proposed self-learning fuzzy logic control that uses the genetic algorithm through reinforcement learning architecture, called a genetic reinforcement fuzzy logic controller, can also learn fuzzy logic control rules even when only weak information such as a binary target of “success” or “failure” signal is available. In this paper, the adaptive heuristic critic algorithm of Barto et al. (1987) is extended to include a priori control knowledge of human operators. It is shown that the system can solve more concretely a fairly difficult control learning problem. Also demonstrated is the feasibility of the method when applied to a cart-pole balancing problem via digital simulations