高速公路实时交通流模糊逻辑优化控制系统设计

高速公路交通流特征复杂多变,受到诸多影响因素的共同作用,如天气、道路状况、车辆类型等等。因此,如何获取交通流逻辑控制条件并实现精准调节难度较大。为此,本研究设计新的高速公路实时交通流模糊逻辑优化控制系统。对于系统的硬件部分,设计车流控制模块、无线传输车载终端、实时交通流逻辑调度模块、模糊控制器模块,并确定各个模块之间实时连接关系。对于系统的软件部分,求解交通流逻辑控制条件,联合高速公路实时通行相位条件,调节模糊控制器,并根据高速公路交通路网模型,推导交通流优化控制条件的求解表达式,完成高速公路实时交通流模糊逻辑优化控制系统的设计。实验结果表明,本系统可以解决逻辑协调错误的问题,且能够有效控制高速公路车流量。     

Intelligent QoS management for multimedia services support in wireless mobile ad hoc networks

In this paper, we propose a new intelligent cross-layer QoS support for wireless mobile ad hoc networks. The solution, named FuzzyQoS, exploits fuzzy logic for improving traffic regulation and the control of congestion to support both real-time multimedia (audio/video) services and non-real-time traffic services. FuzzyQoS includes three contributions: (1) a fuzzy logic approach for best-effort traffic regulation (FuzzyQoS-1), (2) a new fuzzy Petri nets technique (FuzzyQoS-2) for modeling and analyzing the QoS decision making for traffic regulation control, and (3) a fuzzy logic approach for threshold buffer management (FuzzyQoS-3). In FuzzyQoS-1, the feedback delay information received from the network is used to perform a fuzzy regulation for best-effort traffic. Using fuzzy logic, FuzzyQoS-3 uses fuzzy thresholds to adapt to the dynamic conditions. The evaluation of FuzzyQoS performances was studied under different mobility, channel, and traffic conditions. The results of simulations confirm that a cross layer design using fuzzy logic at different levels can achieve low and stable end-to-end delay, and high throughput under different network conditions. These results will benefit delay- and jitter-sensitive real-time services.     

改进动态因果图与模糊推理融合故障诊断方法

在复杂的控制系统发生故障时,运维系统能保证对其进行快速、可靠的故障诊断尤为重要。针对复杂控制系统中控制信号多、信号关联性强、故障状态多、部件故障模式多的情况,提出一种改进动态因果图与模糊推理融合的故障诊断方法,利用改进动态因果图逻辑表达能力强,能因果互推的特点,构建多重（正向、反向、混合）模糊规则,有效克服了模糊逻辑推理中只能由因溯果而不能由果溯因的难题,同时,将动态因果图的动态特性引入到模糊规则的动态更新中,增强了模糊推理的实时性。最后,以某型装甲设备垂直力矩电机控制过程的故障诊断为应用背景,在自行研制的故障诊断平台中嵌入此法进行故障诊断测试,测试结果分析表明,此法能有效提高诊断效率,具有更高的准确性、先进性、适用性。     

二级倒立摆的Sugeno型模糊神经网络控制

为了提高二级倒立摆系统实时控制的响应速度和稳定性,在设计Mamdani型模糊推理规则控制器控制倒立摆系统稳定的基础上,设计了一种更有效率的基于Sugeno型模糊推理规则的模糊神经网络控制器.该控制器使用BP神经网络和最小二乘法的混合算法进行参数训练.能够准确归纳输入输出量的模糊隶属度函数和模糊逻辑规则.通过与Mamdani型控制器的仿真对比及实际控制实验结果,表明该Sugeno型模糊神经网络控制器时二级倒立摆实验装置的控制具有良好的稳定性、快速性和较高的控制精度.     

Intelligent workload balance control of the assembly process considering condition-based maintenance

Balancing the workloads of workstations is key to the efficiency of an assembly line. However, the initial balance can be broken by the changing processing abilities of machines because of machine degradation, and at some point, re-balancing of the line is inevitable. Nevertheless, the impacts of unexpected events on assembly line re-balancing are always ignored. With the advanced sensor technologies and Internet of Things, the machine degradation process can be monitored continuously, and condition-based maintenance can be implemented to improve the health state of each machine. With the technology of robotic process automation, workflows of the assembly process can be smoothed and workstations can work autonomously together. A higher level of process automation can be achieved. Real-time information of the processing abilities of machines will bring new opportunities for automated workload balance via adaptive decision-making. In this study, a fuzzy control system is developed to make real-time decisions to balance the workloads based on the processing abilities of workstations, given the policy of condition-based maintenance. Fuzzy controllers are used to decide whether to re-balance the assembly line and how to adjust the production rate of each workstation. The numerical experiments show that the buffer level of the assembly line with the proposed fuzzy control system is lower than that of the assembly line without any control system and the buffer level of the assembly line with another control system is the lowest. The demand can always be satisfied by assembly lines except the one with another control system since there is too much production loss sacrificed for the low buffer level. The sensitivity analysis of the control performance to the parameter settings is also conducted. Thus, the effectiveness of the proposed fuzzy control system is demonstrated, and intelligent automation can improve the performance of the assembly process by the fuzzy control system since real-time information of the assembly line can be used for adaptive decision-making.     

改进的RBF神经网络在磨矿指标预测中的应用

针对选矿生产过程中磨矿生产率和磨矿浓度、介质填充率和料球比3个指标之间的关系,分析了磨矿生产率对磨矿过程产品质量和工作效率的影响,提出了磨矿过程预测模型,采用了模糊聚类与改进的遗传算法相结合训练RBF神经网络的新方法。仿真结果表明,该模型可以快速准确地预报生产状况,对于企业进行实时生产控制具有较高的实用价值。     

一种基于模糊逻辑的Hop-by-Hop实时流传输控制机制研究与设计

差分服务被证明是Internet网中引入服务质量保证(QoS)有效的解决方案.分析了已有的一些流控制方式,在差分服务的框架下提出一种基于逐跳技术和模糊逻辑来保证实时流传输需求和提高网络效率的机制,重点阐述了其行为逻辑和模糊判别机制.该机制通过模糊逻辑对路由器当前的情况进行判别,利用逐跳过程调整输入速率.采用NS2对该机制进行了模拟实现和评估,结果表明相比较于传统的端到端控制方法在带宽利用、延迟控制方面有明显提高.     

基于EHA的车辆主动悬架建模与仿真研究

采用一种新颖的可控减振技术,提出基于EHA的主动悬架系统.文章在建立1/4车辆悬架动力学模型和EHA综合模型的基础上,以天棚阻尼模型作为参考模型,利用模糊控制方法对簧载质量的垂直加速度进行控制并与被动悬架进行了比较.仿真结果表明:在随机和脉冲两种信号的激励下,相对于被动悬架,主动悬架簧载质量的垂直加速度均方根值分别下降了27%、16%,结果证明了所建模型的正确性以及模糊控制方法对改善汽车性能的可行性和有效性.     

Dynamic switching based fuzzy control strategy for a class of distributed parameter system

In this work, a dynamic switching based fuzzy controller combined with spectral method is proposed to control a class of nonlinear distributed parameter systems (DPSs). Spectral method can transform infinite-dimensional DPS into finite ordinary differential equations (ODEs). A dynamic switching based fuzzy controller is constructed to track reference values for the multi-inputs multi-outputs (MIMO) ODEs. Only a traditional fuzzy logic system (FLS) and a rule base are used in the controller, and membership functions (MFs) for different ODEs are adjusted by scaling factors. Analytical models of the dynamic switching based fuzzy controller are deduced to design the scaling factors and analyze stability of the control system. In order to obtain a good control performance, particle swarm optimization (PSO) is adopted to design the scaling factors. Moreover, stability of fuzzy control system is analyzed by using the analytical models, definition of the stability and Lyapunov stability theory. Finally, a nonlinear rod catalytic reaction process is used as an illustrated example for demonstration. The simulation results show that performance of proposed dynamic switching based fuzzy control strategy is better than a multi-variable fuzzy logic controller.     

A fuzzy PID controller for nonlinear and uncertain systems

 In order to control systems that contain nonlinearities or uncertainties, control strategies must deal with the effects of these. Since most control methods based on mathematical models have been mainly focused on stability robustness against nonlinearities or uncertainties, they are limited in their ability to improve the transient responses. In this paper, a nonlinear fuzzy PID control method is suggested, which can stably improve the transient responses of systems disturbed by nonlinearities or unknown mathematical characteristics. Although the derivation of the control law is based on the design procedure for general fuzzy logic controllers, the resultant control algorithm has analytical form with time varying PID gains rather than linguistic form. This means the implementation of the proposed method can be easily and effectively applied to real-time control situations. Control simulations are carried out to evaluate the transient performance of the suggested method through example systems, by comparing its responses with those of the nonlinear fuzzy PI control method developed in [9].     

多输入–多输出非线性系统的特征模型及在挠性卫星姿态控制中的应用

研究多输入–多输出（MIMO）高阶非仿射非线性系统的特征建模问题.首先证明了MIMO高阶非仿射非线性系统的特征模型可用二阶时变差分方程组描述,并给出了特征模型的建模误差.然后设计了基于特征模型的自适应模糊广义预测控制器,利用Lyapunov方法分析了闭环系统的稳定性.由于控制结构中使用了分层模糊逻辑系统,从而极大减少了模糊规则和可调参数的个数,提高了控制的实时性.通过对挠性卫星姿态控制的仿真研究验证了所给控制方案的有效性,可实现高精度的姿态控制,且该方法具有较强的鲁棒性.     

移动机器人未知环境避障研究

针对移动机器人的避障问题,以AS-R移动机器人为实验平台,提出了一种改进人工势场和模糊逻辑相结合的路径规划方法.对于未知障碍物环境采用人工势场法进行实时路径规划,对于动态近距离动态障碍物采用模糊逻辑方法引导机器人做出避障行为.为了有效将2种方法结合,根据传感器信息对于人工势场方法引入转角的信任度,机器人运行方向由上述2...     

Fuzzy logic for large mining bucket wheel reclaimer motion control——from an engineer's perspective

The bucket wheel reclaimer(BWR) is a key piece of equipment which has been widely used for stacking and reclaiming bulk materials(i.e.iron ore and coal) in places such as ports,iron-steel plants,coal storage areas,and power stations from stockpiles.BWRs are very large in size,heavy in weight,expensive in price,and slow in motion.There are many challenges in attempting to automatically control their motion to accurately follow the required trajectories involving uncertain parameters from factors such as friction,turbulent wind,its own dynamics,and encoder limitations.As BWRs are always heavily engaged in production and cannot be spared very long for motion control studies and associated developments,a BWR model and simulation environment closely resembling real life conditions would be beneficial.The following research focused mainly on the implementation of fuzzy logic to a BWR motion control from an engineer’s perspective.First,the modeling of a BWR including partially known parameters such as friction force and turbulence to the system was presented.This was then followed by the design of a fuzzy logic-based control built on a model-based control loop.The investigation provides engineers with an example of applying fuzzy logic in a model based approach to properly control the motion of a large BWR following defined trajectories,as well as to show possible ways of further improving the controller performance.The result indicates that fuzzy logic can be applied easily by engineers to overcome most motion control issues involving a large BWR.     

质子交换膜燃料电池动态建模及其双模控制

由于已提出的质子交换膜燃料电池(PEMFC)模型难于控制, 提出利用MATLAB/SIMULINK仿真工具进行PEMFC系统动态建模, 同时为实现对PEMFC系统输出电压的控制, 采用了基于模糊规则切换的模糊逻辑控制器(FLC)和比例积分微分控制器(PID)相结合的双模控制方式. 仿真结果证明该动态模型易于控制, 能够反映出PEMFC系统的动态输出特性, 而且验证了基于模糊规则切换的双模控制能够有效抑制扰动, 改善PEMFC系统的动态输出特性, 保证系统的稳定运行, 有助于对PEMFC系统的输出性能分析以及实时控制系统的设计.     

A multiregion fuzzy logic controller for nonlinear process control

Although a fuzzy logic controller is generally nonlinear, a PI-type fuzzy controller that uses only control error and change in control error is not able to detect the process nonlinearity and make a control move accordingly. In this paper, a multiregion fuzzy logic controller is proposed for nonlinear process control. Based on prior knowledge, the process to be controlled is divided into fuzzy regions such as high-gain, low-gain, large-time-constant, and small-time-constant. Then a fuzzy controller is designed based on the regional information. Using an auxiliary process variable to detect the process operating regions, the resulting multiregion fuzzy logic controller can give satisfactory performance in all regions. Rule combination and controller tuning are discussed. Application of the controller to pH control is demonstrated     

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。     

模糊推理协处理器芯片

模糊推理协处理器VLSI芯片F200采用0.8μm CMOS工艺,作为一种模糊 控制器,主要用于实时过程控制和其它适合的应用场合,例如机器人控制、分类器、专家系 统等.F200芯片支持多个模糊知识库工作,支持最常用的两种模糊模型,Mamdani和 Trakagi-Sugeno模型.芯片精度12位,主频20MHz,推理速度约为每秒1.2M条模糊规则.     

基于视觉伺服控制的光伏板清洗系统设计

光伏板上的鸟粪、枯叶等污渍若不及时清理会导致光伏板热斑现象的发生,严重影响光伏板的使用寿命和安全。现有光伏板清理方法存在清洗效果不好、浪费水资源、清洗效率低等问题,为此,及时清理维护光伏板成为光伏发电企业亟待解决的首要问题。基于以上问题,该文提出光伏板清洗视觉伺服控制系统和一种改进型模糊PID控制算法,利用从实时视频图像中提取的反馈信息控制机械臂对准污渍点,实现定点喷射清洗污渍点,达到节水高效的目的。实验结果表明,基于改进型模糊PID控制算法的光伏板清洗视觉伺服控制系统能够准确地定位到光伏板污渍位置并精准喷射清洗污渍,最大限度地节约水资源。     

Robust fuzzy CPU utilization control for dynamic workloads

In a number of real-time applications such as target tracking, precise workloads are unknown a priori but may dynamically vary, for example, based on the changing number of targets to track. It is important to manage the CPU utilization, via feedback control, to avoid severe overload or underutilization even in the presence of dynamic workloads. However, it is challenge to model a real-time system for feedback control, as computer systems cannot be modeled via physics laws. In this paper, we present a novel closed-loop approach for utilization control based on formal fuzzy logic control theory, which is very effective to support the desired performance in a nonlinear dynamic system without requiring a system model. We mathematically prove the stability of the fuzzy closed-loop system. Further, in a real-time kernel, we implement and evaluate our fuzzy logic utilization controller as well as two existing utilization controllers based on the linear and model predictive control theory for an extensive set of workloads. Our approach supports the specified average utilization set-point, while showing the best transient performance in terms of utilization control among the tested approaches.