基于FPGA和模糊控制的模型车运动学仿真

模型车运动学仿真系统是集硬件、软件于一身的完整且复杂的系统,它在汽车驾驶模拟训练领域的市场前景非常广阔。但是,采用传统的数学建模方法,很难得到精确而又简单的数学模型。该文介绍了一种基于C8051F020和FPGA的模糊控制模型车运动学仿真系统,简洁高效地实现了对真车精确的运动学仿真,并详细地阐述了该系统的设计思想及工作流程。     

基于MATLAB的智能灌溉模糊控制系统的研究

为提高水资源的有效利用率,加快我国灌溉自动化的步伐,缩短与国外灌溉水平的差距,该文提出了一种基于模糊控制的智能灌溉系统的设计方案。文中详细介绍了系统模糊控制器的设计方法,并利用MATLAB对其进行仿真,仿真结果显示不断调整模糊控制规则和系统参数,可以改进控制系统性能使其满足系统的动静态性能要求。另外在0.25s时对系统加入干扰进行仿真,仿真结果显示该设计可以提高系统的抗干扰性能。     

未知环境下的移动机器人仿真平台设计

从理论上分析了机器人控制系统和检测外部环境的传感器,并在该基础上阐述了航姿推算定位算法.利用Matlab GUI设计了移动机器人的控制仿真平台,并通过算法对超声波测距进行了模拟.最后,设计模糊控制器应用于该仿真平台对移动机器人进行实时控制.仿真结果表明,在非结构化的未知环境中,系统响应速度快,实现了机器人从起点到静态目标点的安全行驶.     

基于神经网络模糊控制理论的转台伺服系统控制设计

针对三轴飞行仿真转台伺服系统非线性、模型不精确等特点,在分析转台系统结构的基础上,采用模糊PID控制的方法对转台伺服系统进行仿真控制,得到较好的控制效果;模糊控制控制规则的获得带有很大的人为因素,并且在控制过程中对规则采用查表法占用大量的内存;基于以上原因,设计了神经网络模糊控制器(NNSOC),利用神经网络控制自学习、自调整的能力,为模糊控制器提供自动生成控制规则的能力;同时由于神经网络具有联系记忆能力,可对未训练的样本做出决策;对NNSOC的控制效果进行了仿真;结果表明:其具有很好的动态性能和鲁棒性,对转台的控制效果良好。     

十一自由度汽车转弯防抱制动系统仿真的研究

防抱制动系统（ABS）仿真的目的是建立汽车动力学仿真模型,并在此模型上加载防抱制动控制器模型,以观察不同控制策略在不同工况条件下的控制效果。采用解析法建立的十一自由度汽车转弯制动数学模型,并用MATLAB／SIMULINK建立仿真模型。用PID控制器对防抱制动系统的制动过程进行了仿真分析,针对PID控制器抗干扰能力不强的缺点,设计了抗干扰能力更强的模糊控制器,对仿真结果进行了对比分析。仿真结果表明模糊控制器在理想工况条件下控制效果不如PID控制器,但抗干扰能力更强。本实验为进一步硬件在线实时仿真做好准备。     

交通系统的模糊控制及其神经网络实现

本文根据城市交通系统的特点设计了单个路口信号灯的模糊控制器,研究了用神经网络实现模糊控制器的方法和过程,并对该控制器进行了仿真研究,本文所设计的控制方法适合于各种车流大小随机变化的单个路口,且决策过程迅速、合理,无需对车流进行预测,是一种实时单点控制方法,由于模糊控制器由神经网络实现,控制具有学习和联想功能,仿真结果说明了该方法的有效性。     

基于Simulink变论域算法仿真技术研究

针对目前通过Simulink模糊控制工具箱对变论域模糊控制算法仿真缺少理论支撑的现象,利用模糊数学理论,推导出变论域模糊控制插值函数,并对此插值函数分析得出：将伸缩因子的作用对象向输入输出移动而保持论域固定可等效实现变论域算法。为验证理论,以单相逆变电源为例,在Simulink中搭建变论域模糊控制+PID算法仿真模型。实验结果表明其具有良好控制效果,为借Simulink模糊控制工具箱仿真变论域模糊控制算法提供了理论依据和方法,验证了上述理论研究和结论。     

倒立摆系统的T-S模糊控制研究

针对倒立摆的非线性采用T-S模糊控制的方法对倒立摆进行控制,建立了倒立摆的数学模型,并用Matlab进行了仿真,仿真结果表明该控制方法对倒立摆具有好的控制效果。     

专家模糊控制方法

研究用于模糊控制的修正因子在线专家自学习方法，并在计算机上进行了仿真。结果表明：该专家模糊控制方法对其修正因子调整具有良好的自学习能力及收敛性。     

模糊控制在液位控制器中的应用

在模糊控制原理的基础上设计出模糊控制器,利用模糊推理的方法实现了对液位系统的控制,本文详细介绍了模糊控制器设计思路和步骤。并在MATLAB仿真软件上进行仿真研究,结果表明,模糊控制能使液位控制系统的控制效果大大提高,对进一步的研究应用提供了一定的参考价值。     

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.     

单路口右转信号的模糊控制策略

针对单路口右转车辆在转入方向车流稀疏的情况下仍在等待而造成不必要延误的现象作了分析 ,基于人的对单路口右转车辆控制决策过程 ,提出了一种控制算法并设计了模糊控制器 ,把禁行方向机动车和自行车队长度作为输入 ,以此来决定下一相位右转车辆绿时提前的时间 (前时 )长短。仿真结果表明 :此种方法能够有效地减少右转车辆不必要的延误     

Nussbaum gain adaptive fuzzy control for switched nonlinear systems with predefined output and full time-varying states constraints

In this paper, the problem of adaptive fuzzy tracking control for a class of uncertain switched nonlinear systems with unknown control direction is studied. Aiming at the problem, an adaptive control scheme with Nussbaum gain technology is constructed by using the average dwell time (ADT) method and the backstepping method to overcome the unknown control direction, and time-varying asymmetric barrier Lyapunov functions (ABLFs) are adopted to ensure the full-state constraints satisfaction. The proposed control scheme guarantees that all closed-loop signals remain bounded under a class of switching signals with ADT, while the output tracking error converges to a small neighborhood of the zero. An important innovation of this design method is that the unknown control direction, asymmetric time-varying full state constraints, and predefined time-varying output requirements are simultaneously considered in uncertain switched nonlinear systems for the first time. We set a moment in advance, and make the systems comply with the constraint conditions before running the moment by the shift function nested in the first time-varying ABLF. Finally, a simulation example verifies the effectiveness of the proposed scheme.     

Selecting the optimal shift numbers using fuzzy control model: a paint factory’s facility application

Number of shifts is one of the most important criteria for the production planners to minimize the production costs. The first measurement of the facility, where we executed the application, is optimizing the manpower rotation in B12 production, a special kind of chemical, which is used for paint binders. Seasonal variations in demand of B12 add more importance to the number of shifts in the facility. The purpose of this article is to optimize the shift periods subject to raw material, shipping date, inventory and demand constraints. The model is developed for a paint factory’s reactor facility. The production system has a raw material inventory, a reactor, and a finished goods inventory. We use fuzzy control to optimize the number of shifts under the constraints, given before.     

Mean shifts detection and classification in multivariate process: a neural-fuzzy approach

For monitoring multivariate quality control process, traditional multivariate control charts have been proposed to detect mean shifts. However, a persistent problem is that such charts are unable to provide any shift-related information when mean shifts occur in the process. In fact, the immediate classification of the magnitude of mean shifts can greatly narrow down the set of possible assignable causes, hence facilitating quick analysis and corrective action by the technician before many nonconforming units are manufactured. In this paper, we propose a neural-fuzzy model for detecting mean shifts and classifying their magnitude in multivariate process. This model is divided into training and classifying modules. In the training module, a neural network (NN) model is trained to detect various mean shifts for multivariate process. Then, in the classifying module, the outputs of NN are classified into various decision intervals by using a fuzzy classifier and an additional two-point-in-an-interval decision rule to determine shift status. An example is presented to illustrate the application of the proposed model. Simulation results show that it outperforms the multivariate T²control chart in terms of out-of-control average run length under fixed type I error. In addition, the correct classification percentages are also studied and the general guidelines are given for the proper use of the proposed model.     

基于模糊逻辑的一类非线性系统直接自适应控制

针对一类连续非线性不稳定系统,基于模糊逻辑提出了一种新的自适应跟踪控制方法,在此方法中,控制器由两部分组成：模糊逼近控制器（FAC）和模糊滑模补偿控制器（FSMCC）,其中,FAC利用模糊逻辑系统全局逼近理论控制器,FSMCC用于全局补偿逼近误差和系统的不确定性及消除外部干扰的影响,整个闭环控制系统在Lyapunov意义下全局渐进稳定踊跃误差收敛于零的某一领域内,最后通过示例验证了本方法的有效性。     

用于SVC的一种模糊非线性变结构控制方法

采用直接反馈线性化方法得到有SVC单机无穷大系统线性模型状态方程。以此为被控对象,设计了一个滑膜变结构控制器,将滑膜切换面函数值及其变化率作为模糊控制器输入,以调整控制量输出。通过仿真验证,所设计的SVC模糊滑膜控制器可以有效的改善系统功率环境,受到扰动时可以迅速的平息功率的变化。     

自动档车辆换档品质评价系统

应用MATLAB和C++设计了基于BP神经网络的汽车换档品质评价系统。对换档过程中的客观评价指标构成的样本向量进行学习和训练,建立换档品质客观评价系统,训练后的BP神经网络嵌入C++中,并实时显示客观评价指标曲线。试验结果表明,该系统可以客观有效地评价换档品质。     

模糊滑模控制研究综述

本文介绍了90年代以来将模糊逻辑和滑模控制相结合的研究工作,着重描述了模糊控制和滑模控制的相似性、启发式模糊滑模控制、直接和间接自适应模糊滑模控制、模糊神经网络在滑模控制中的应用等方面的原理和方法,分析了它们的特点和联系,本文最后还展望了这一领域的研究方向.